FAQ 

The post The Future of Smartlook  appeared first on Smartlook Blog.

For example, if you are interested in how many users click on the “buy” text but only on your pricing page, you can easily set everything up using “is” matching. Or conversely, you can exclude that URL using the “is not” rule and the event will count clicks on URLs other than your pricing.

This works for all event types except visited URL and Merged events.

Enhanced Element Picker

Enhanced Element Picker allows you to adjust CSS selectors that fit your needs with only a few clicks. Picker helps you create events directly from the UI without coding.

Please check out our video that summarizes the latest update:

You can find even more details in our docs:
https://help.smartlook.com/docs/event-picker-and-css-selector https://help.smartlook.com/docs/creating-events

The post Introducing enhanced Event creation appeared first on Smartlook Blog.

Although it often goes unnoticed, real-time analytics shapes our everyday lives. We rely on products equipped with real-time data to provide us with relevant information, anticipate our needs, and respond to issues promptly. 

So when it comes to user experience (UX), real-time analytics isn’t an option — it’s the only way to meet your audience’s ever-evolving expectations. 

Throughout this guide, we’ll walk you through everything you need to know to successfully implement and use real-time data analytics to create a seamless UX for your website and app users. 

Particularly speaking about:

• What is real-time analytics? 
• 6 Applications of real-time analytics 
• How to implement real-time analytics to improve UX by use case
• Best real-time data analytics solutions

What is real-time analytics?

Real-time analytics involves capturing, processing, and analyzing data as it occurs without delay. It gives you immediate access to the data you’re tracking, allowing you to make informed decisions promptly or, better yet, set up automated workflows based on real-time data. 

Real-time data analytics vs historical analytics

Historical analytics focus on analyzing data from the past, identifying trends, and understanding long-term performance. It involves generating periodic reports and summarizing data over specific time intervals. It’s useful for decision-making and long-term planning based on historical patterns.  

Real-time data analytics or historical analytics? You don’t have to choose between the two. While real-time data analysis provides immediate insight for prompt decision-making, historical analytics enables you to anticipate and plan for the future. 

It’s important that your real-time analytics system includes a significant data retention period, as real-time data is not enough to build a sustainable long-term strategy. 

​​​“While data is crucial, it’s essential to avoid analysis paralysis. 

Real-time insights are valuable, but exercise caution for significant business decisions. 

Don’t miss out on leveraging the full potential of data and analytics in your marketing strategies.”

Jorge Cunha

Consultant, Trainer, Digital Marketing Analyst & Manager & Founder at IT Tech BuZ

For instance, real-time data may inform a product team of a sudden increase in app load times. Instead of waiting for feedback from frustrated users, the team can promptly initiate an investigation and optimize app performance. 

At the same time, historical data will uncover patterns affecting your product growth in the long run, such as low usage of particular features. Based on long-term performance insights, the product team can develop a better feature adoption plan or make adjustments to product functionality. 

Historical data also fuels predictive analytics. Only by analyzing large amounts of data regarding past trends, patterns, and behaviors can AI-powered solutions forecast future scenarios with precision. 

​​​“Product Managers can leverage AI to automate repetitive tasks, perform rapid prototyping, conduct user testing, and even optimize product features based on real-time user feedback. This enables faster iterations, accelerated time-to-market, and more efficient use of resources.” 

Shaan Agara

Head of Product at Uniguest

6 Applications of real-time analytics 

The benefits of real-time analytics go far beyond just having enough data to make quick decisions. Real-time data allows businesses to: 

• Enable more personalized customer experiences 
• Improve internal operations 
• Validate ideas  
• Proactively respond to changing behaviors and emerging trends 
• Create a seamless UX 

But to fully understand the benefits of real-time insights, it’s best to observe their practical applications. Let’s explore six use cases that vividly demonstrate the importance of real-time analytics in your daily operations. 

Customer experience personalization

Real-time analytics are behind every dynamic personalization strategy. For instance, a marketing automation tool collects data on a user’s recent engagement to dynamically move them to a different email list, trigger a targeted website pop-up, or send them a personalized offer. 

In a SaaS environment, real-time data helps tailor the user experience within your product, streamlining user flows and maximizing customer satisfaction. Take Spotify or YouTube Music — both platforms capture data regarding the music you listen to and recommend similar content in real-time (by the way, historical data also plays a significant role in creating custom playlists). 

Customer relationship management (CRM) 

Many CRM software providers are powered by instant insights, enabling sales and marketing teams to keep their pipelines up-to-date. You can view lead movements, deal progress, and customer engagement as it unfolds, allowing you to seize every opportunity to create a more personalized customer experience. 

Also, by combining real-time visibility with pipeline activities, teams can allocate resources more efficiently. 

Usability testing 

For product teams, real-time data is a source of valuable UX insights. You can track how users interact with new features and design changes and drive immediate conclusions about the UX.   

Note: Don’t confuse A/B testing with usability testing. Usability testing involves watching users navigate your product, while A/B testing is a product experimentation method (we’ll cover it a little later). Additionally, usability testing focuses on analyzing qualitative insights, and A/B testing gives you quantitative data. 

That said, an emerging real-time analytics use case in usability testing is AI-powered sentiment analysis. The idea behind it is to bring positive and negative emotions to the surface, making it easier to detect friction points. This function is included with usability testing tools like UserTesting and Userlytics. 

Source: UserTesting 

Product experimentation 

As you already know, product experimentation is different from usability testing, yet these two share a common feature — they employ real-time analytics. 

A core application of real-time data in product experimentation is real-time A/B testing. This involves simultaneously presenting different variations (A and B) of a feature, design, or piece of content to equal user segments. Real-time analytics measure user interactions and responses to each variation, providing immediate insight into which performs better. 

Aren’t product experimentation results more reliable when you have plenty of historical data? Yes. But experiments often involve changes in product design or functionality, meaning that things can go completely wrong when you don’t expect it. Real-time data will help you make sure you don’t waste two weeks (or whatever timeframe you’ve set) with the “A” variation of your product being down or not performing as expected. 

Inventory management 

For those working in e-commerce, real-time data is a life-saver. It helps online retailers maintain optimal stock levels, prevent stockouts, and efficiently manage their supply chain. 

New series, TikTok trends, or influencer opinions may create a surge in demand for a particular product without you being ready for it. To avoid running out of stock, e-commerce platforms monitor stock levels and demand in real time and enable retailers to set up alerts based on predefined thresholds for timely restocks. 

UX optimization 

Real-time performance monitoring enables product teams to detect anomalies, address issues, and optimize products for an excellent user experience. Digital analytics tools like Smartlook use real-time analytics to: 

• Detect anomalies in user behavior 
• Create heatmaps for any user session 

• Alert you regarding crashes and errors 

Let’s examine how you can implement real-time data analytics with Smartlook to create a seamless website and app UX. 

How to implement real-time analytics to improve UX by use case 

You don’t need to do complex manipulations to collect real-time data. All you need is product analytics software with real-time data monitoring capabilities. If you entrust UX monitoring and data processing to Smartlook, you’ll get instant insight on two levels: 

1. User behavior 

2. Product performance 

When combined, these insights give you everything you need to create an excellent UX. Here’s how. 

1. Real-time user behavior monitoring 

How do your users interact with your website or app? Do they get what they want? Smartlook’s user behavior data answers these questions in real-time. 

Event tracking 

Events are any actions users take within your product. Tracking events keeps you informed regarding how users engage with your app and whether they take the actions you want them to. 

Smartlook allows you to set up custom events to track specific user interactions in real time. You just need to define the event category (e.g. button click, visited URL, etc.) and specify the event details.  

Creating a new event in Smartlook 

Use case: You release a new feature and create events, but the real-time data uncovers that people hardly interact with it at all — it looks like your marketing campaign has failed to attract user attention. It’s time to revisit your communication strategy to get traction for the new feature. 

Heatmaps

A heatmap is a data visualization method that reveals how users interact with a website or application. It uses color gradients to represent the intensity of mouse movements or clicks on a page. 

Smartlook’s new Page analytics feature creates heatmaps from session recordings in real time. You can create a heatmap directly in the player to identify the most clicked elements. 

Wait, what are session recordings? These are actual recordings of users interacting with your app or website. As you watch a recording, you may wonder what a heatmap for a particular page looks like — just click the Page analytics button, and Smartlook will give you the answer in seconds. 

Real-time page analytics in Smartlook 

Use case: An e-commerce retailer designed landing pages specifically for Black Friday, which means they hadn’t tested them against a real audience before. Click data from Smartlook’s heatmap revealed that the “Shop Now” button, a crucial call-to-action (CTA), wasn’t receiving as many clicks as anticipated. In response, the retailer quickly adjusted the button placement to a more prominent location, ensuring it was immediately visible upon landing. 

Anomaly detection and alerts 

Smartlook’s real-time anomaly detection and alert system sends immediate notifications when unusual user behavior patterns occur. An anomalous behavior may be a sudden spike or drop in user engagement within an event or an entire funnel. 

To enable anomaly detection, you need to choose an event or funnel you want to track and click Anomalies monitoring. You can configure what kinds of anomalies you want to be alerted of (surges, drops, or both) and how you should be notified.   

Anomaly monitoring in Smartlook 

Use case: You’re releasing a major UI update, which can affect user engagement. Setting up anomaly monitoring will keep you informed of shifts in user behavior. Choose key events that reflect user engagement on your platform and configure anomaly detection settings to monitor engagement drops in selected events. Sudden drops will indicate that users don’t understand how to use features in the updated interface. 

2. Real-time product health monitoring 

These insights will help you uncover serious problems with your product experience (PX). Spot and address technical issues or confusing UI instantly without waiting for frustrated users to churn. 

Crash reports 

Smartlook’s crash reports feature informs you about technical performance issues in real time and displays recordings of sessions where a particular crash occurred.  

This unique functionality allows product teams to reproduce any crash by simply watching a session replay — no need to collect user feedback or retrace logs. 

Crash reporting in Smartlook 

Use case: A social media management company was spending hours reproducing crashes before the implementation of crash reporting. The problem was that the customer support team had to collect feedback from users on every crash before they could attempt to retrace the events that preceded a problem. User feedback was hardly ever accurate, which only made matters worse. Crash reporting, combined with session recordings, has allowed the customer support and product teams to resolve issues faster and increase customer satisfaction. 

Rage clicks

Rage clicks are events indicating user confusion. They occur when a user keeps clicking on a page element, but it doesn’t respond. Such events might indicate broken links or confusing UI (so confusing that users try to click elements that are unclickable).  

In Smartlook, you can find data on rage clicks in the Events tab. Here you can see statistics on rage clicks and play recordings of each session connected to these events. 

Rage clicks in Smartlook 

Use case: Your pricing page attracts a lot of visitors, but very few click the Contact Us button. After investigating, you discover that users have been trying to interact with the plain text (that you underlined for design purposes), not noticing the CTA button at the top of the page.  

Best real-time data analytics solutions

These are five business intelligence solutions with real-time data monitoring capabilities that serve different use cases but share one ultimate goal — to help you create an excellent customer experience. 

Smartlook: real-time UX analytics 

Source 

Smartlook provides both real-time and historical UX data on web and mobile apps. It combines user behavior monitoring (i.e. session recordings, heatmaps, etc.) and product health tracking (i.e. crash reporting) features. 

Key features: 

• Event tracking: monitor every user interaction in real-time 
• Heatmaps: visualize user engagement patterns on web and app screens 
• Anomaly detection: receive immediate alerts on unusual user behavior 
• Crash reporting: detect and diagnose crashes occurring within your web or mobile app 

Sentry: real-time product health monitoring 

Source 

Sentry gives you insight into critical real-time product performance metrics like failure rate, request time, event duration, and release health data. The latter allows you to stay on top of releases by tracking user adoption, error count, crashed user rate, and more. 

Key features: 

• Real-time error tracking: remain aware of all errors that occur in your application 
• Release tracking: monitor individual release health to make it easier to connect errors and UX issues to specific releases 
• Performance monitoring: quickly catch performance degradations like slow load times  

Optimizely: real-time experimentation 

Source 

Optimizely is your product experimentation assistant, allowing you to run and monitor low-code product tests. It tracks metrics like conversion rates, demo requests, revenue per visitor, and other conversion KPIs to evaluate the outcomes of your experiments. 

Key features: 

• A/B testing: present different variations of features, designs, and content for actionable insight into the performance of each variation 
• Feature flagging: roll out new functionalities gradually and monitor their impact on user experience and engagement 
• Advanced targeting: create audience segments that refresh every 90 seconds and adapt to customer behavior in real-time 

OptiMonk: real-time e-commerce personalization  

OptiMonk enables e-commerce personalization using real-time data analytics capabilities to enhance the overall shopping experience and drive conversions.  

The platform also uncovers critical campaign performance metrics in retrospect — analyze average order per value (AOV), conversion rates, and other metrics.  

Key features:  

• Website personalization: let OptiMonk tailor your web content to individual users dynamically 
• Campaign analytics: track and analyze the effectiveness of your marketing efforts (historical data only) 
• AI-powered conversion optimization: personalize all of your content automatically with OptiMonk’s AI algorithms 

Zoho: real-time inventory management 

Source 

Zoho Inventory helps e-commerce companies keep optimal stock levels by providing up-to-the-minute data on inventory levels for all products. Its dashboards uncover metrics like the number of active items and products to be delivered, sales returns, etc. 

Key features: 

• Real-time inventory management: always stay on top of your inventory levels and prevent stockouts 
• Real-time shipment data: keep customers informed about the status of their orders 
• Multi-channel sales: aggregate sales data from multiple data sources and manage all online sales through a single platform  

Keep track of your user experience with Smartlook’s Instant Insights 

Implementing real-time analytics is critical to ensure a seamless UX. To meet the demands of today’s consumers, you need to be able to respond quickly to unexpected events — be it an app crash or a surge in orders for a particular item. 

With features like event tracking, heatmaps, anomaly detection, and crash reporting, Smartlook’s analytics platform is a comprehensive toolkit for monitoring and optimizing web and mobile UX on the fly.  

Schedule a free demo or try Smartlook today with our full-featured 30-day trial. 

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Responsive and relevant: how real-time analytics shape the user experience  appeared first on Smartlook Blog.

Ask yourself, what delights your users? Or, more specifically, what about your app engages them, and what makes them feel confused?

The only way to answer these questions is by running usability testing—the practice of analyzing your app’s design and functionality from the users’ perspective.

Usability testing can involve observing how users interact with your app, engaging a focus group to test your app in a controlled environment, or collecting user feedback directly. In fact, there are numerous ways to test the user experience within your app, including a few dozen tools for each method.

But how do you choose the best one?

To help you make an informed decision, we’ll walk you through the eight best mobile user-testing tools. Each of which employs various methodologies to provide product teams with actionable insight into app usability. 

Read on to discover:

• The most effective usability testing methods
• The 8 Best usability testing software solutions 
  • Smartlook
  • Maze
  • Optimizely
  • Lookback
  • UserTesting
  • Survicate
  • Global App Testing
  • Dynatrace

Mobile usability testing methods: choosing the right one to optimize the testing process

Your choice of tool depends on your testing tactics. If you haven’t selected a methodology, let’s look at the most common (and effective) usability testing techniques.

Quantitative usability testing

Quantitative usability testing involves gathering numerical data to evaluate and compare your mobile app with your internal benchmarks. To quantify user behavior, you may track metrics such as task completion rates, time on task, error rates, and click-through rates.

Best for: companies that already have a large user base and enough historical data to get statistically significant results.

Guerilla testing

Guerrilla testing is an informal usability testing method conducted in real-world situations with minimal resources. Testers approach random people and ask them to use a mobile app, observing their interactions and collecting feedback. 

Guerrilla testing is excellent for identifying top-level usability issues, uncovering unexpected user behavior, and gaining rapid feedback without the constraints of a formal lab environment.

Best for: testing new products before launch.

Session recording

Session recording involves capturing all user interactions with your mobile app and reviewing them as a video. This analysis lets you pinpoint user struggles, points of frustration, and the reasons behind drop-offs.

Smartlook, an app analytics tool, records all user sessions allowing you to filter, flag interactions, and leave notes when you find something particularly interesting.

Best for: an in-depth end-user experience analysis to identify optimization opportunities in a live product.

Observation

Direct observation is the practice of watching users interact with your mobile app in real time without guiding them through it. The primary goal is to comprehensively understand how users naturally interact with the app, uncovering their successes and challenges.

It’s like watching session replays but with the ability to interject and ask users to explain certain actions or feelings. The drawback is that you can only observe a limited number of sessions at a time. 

Best for: prototype testing or collecting user feedback before releasing new products or features.

User interviews and feedback collection

User interviews are one-on-one conversations with end-users to gather qualitative feedback about their experiences with your app.

Collecting feedback within your app may be easier than engaging users in one-on-one calls if you don’t have a loyal customer base yet. You can embed in-app surveys in your product flows and instantly collect users’ opinions on particular interactions.

Source: Survicate

Best for: backing quantitative insights with qualitative data. 

A/B testing

Although generally not considered a usability testing method, A/B testing can complement any of the above techniques, allowing you to drive even more insight into user preferences.

Best for: experimenting with multiple versions of an app.

So which one should you choose?

Select your usability testing method based on which development stage you’re currently working on. 

Have you just designed the functionality of your app? Then guerilla testing or observation are helpful techniques to begin with. 

Looking to optimize your product performance or release new features? Quantitative insights backed by session recordings and user feedback will give you a grasp on the user experience and help you pinpoint areas for improvement.

Best mobile usability testing tools compared

Smartlook: best for recording user behavior

Smartlook is a user experience testing tool that gives you a complete picture of your app’s usability. It’s a great alternative for unmoderated usability testing, combining the power of quantitative data analysis with detailed session recordings.

Use case: Say you’ve implemented a new feature and want to determine if the flow is intuitive enough for early adopters. Integrate Smartlook’s lightweight SDK into your mobile app to record user sessions and capture every interaction with your app and new features in particular.

When ready, create events to represent each stage (or screen) within the flow and create a new funnel. This funnel will represent how users navigate this new feature.

This allows you to collect quantitative insights into how many users interact with the feature and how many drop off. 

Examine drop-offs closely by looking into abandoned sessions via session recordings. To watch replays of all the users who left the flow, simply click the Play button underneath the relevant stage.

Session recordings in Smartlook

By looking into individual session replays, you’ll be able to see how real users experience your app interface and spot usability issues promptly. And you don’t necessarily have to focus on drop-offs—you can test your app’s usability by a particular audience segment by applying custom filters.

Key features:

• Quantitative data on user engagement uncovers metrics such as click-through rates, task completion rates, interactions with particular features (a.k.a. events), etc
• Session recordings capture user interactions with your app in real-time
• Rage clicks highlight design flaws by helping you spot frustrating interactions, such as clicking multiple times on design elements
• Anomalies detection automatically identifies unusual user behavior patterns. With anomalies monitoring turned on, Smartlook will alert you once trigger conditions are met 
• Heatmaps provide valuable insight into user attention, helping you optimize your app’s layout and placement of essential elements
• Mobile & web analytics make it possible to track user engagement across mobile apps and desktop websites

Cons:

• Suitable only for contextual testing. No functionality for controlled experiments
• Smartlook doesn’t offer sample audiences. You’ll be testing against your customer base

Pricing: Free for up to 3,000 monthly sessions and 10 trackable events. Paid plans start at $55/month. Sign up for a free, full-featured 30-day trial today.

“Smartlook provides invaluable insight into how users navigate our mobile app. We identified dozens of usability issues within the first few weeks of using Smartlook. The funnels feature is super useful and helped us understand how our paying customers interact with our app, including which paths and features they accessed, pre-conversion. The qualitative data generated from Smartlook is a valuable complement to our quantitative analytics.”

Verified User Review

Maze: best for prototype testing

Maze is a product discovery platform that covers many usability testing methods like prototype testing, card sorting, and tree testing.

Use case: Maze is a good choice for teams in the early product development stages. To start testing your prototype in Maze, you must create a new project and import your prototype from your design tool (Maze integrates with Figma and Sketch).

Next, outline specific tasks and scenarios you want users to perform within your prototype. Engage your audience or Invite participants who match your target audience to take the usability test. Maze allows you to set participant criteria, ensuring you gather feedback from your intended user demographic.

When the test is live, Maze will capture user actions, heatmaps, and navigation paths and provide you with quantitative metrics regarding your app’s usability.

Evaluating prototype test results in Maze

Key features:

• Wireframe and prototype testing allow you to validate your design usability before the app is live
• Card sorting is a practice of asking users to organize terms or features into categories, giving you a better understanding of their logic
• Tree testing involves evaluating your product terminology and architecture by putting it in front of users
• Live website usability testing enables you to run user research on live product flows
• Customer surveys collect user feedback regarding product experience
• AI-powered interview analysis automatically summarizes your Zoom interviews and creates shareable reports

Cons: 

• High-fidelity prototypes crash frequently, especially on mobile
• While Maze offers testers for product research, companies often complain about getting unreliable results from recruited participants

Pricing: Free for 1 study/month and a total of 7 questions or missions per study. Paid plans start at $99/month and unlock access to features like screen recordings and card sorting. Involving participants is only possible with a Team Plan, starting at $1,250.

Optimizely: best for A/B testing

Optimizely is a digital experience testing and optimization platform. It offers a wide range of tools for multivariate testing, A/B testing, etc.

Experimentation results by Optimizely

Use case: Optimizely integrates with Smartlook, allowing you to validate your A/B tests with user behavior insights like session recordings. While Optimizely uncovers the best-performing version of your app and gives you quantitative metrics, Smartlook’s qualitative insights will give you a better understanding of the actual user experience.

Key features:

• AI-powered personalization enables you to deliver a targeted and personalized experience to various audience segments before a full-scale roll-out
• Team collaboration is made easy thanks to Optimizely’s collaborative interface. It offers space for capturing ideas, assigning project tasks, and discussing outcomes
• A/B testing is a fundamental feature of Optimizely, enabling you to create and test two or more versions of a webpage or app screen
• Multivariate testing allows you to experiment with multiple element variations simultaneously

Cons:

• Optimizely requires a lot of training for new users
• Quantitative metrics don’t reflect the actual behaviors of app users. You need to integrate Optimizely with a third-party UX analytics tool to gain access to heatmaps and screen recordings

Pricing: Available upon request.

Lookback: best for real-time user insights

Lookback is an on-the-go research tool that helps product teams capture usability insights through one-on-one interviews and observation.

Use case: While Lookback is definitely helpful for validating your app’s UX design before release, it includes a popular use case— bringing teams together in research sessions. 

Invite product managers, designers, customer experience (CX) experts, and other teams to Lookback’s Observation Room to watch users interact with your app to ensure you’re on the same page regarding user pain points and key learning moments.

Collaborative user research in Lookback

Key features:

• Moderated and unmoderated tests give you the freedom to choose the method that works best for you. Set tasks for users to follow or observe as testers interact with your app during live sessions
• Face-to-face interviews uncover user perspectives that might be missed in questionnaires or online interactions
• Live team collaboration enables teams to observe user interactions in real time, share notes, and communicate seamlessly
• Timestamped notes serve as references for important interactions within the user testing process
• Eureka, an AI-powered assistant, transcribes and summarizes your sessions on the fly 

Cons:

• Testers may face trouble getting into sessions
• The platform doesn’t aggregate statistical data
• Running one-on-one sessions is time-consuming

Pricing: Lookback doesn’t include a free plan, only a 60-day free trial. Paid plans start at $25/month for 10 sessions/year.

UserTesting: best for AI-driven analysis

UserTesting is a UX research platform specializing in capturing human insight through videos.

Use case: UserTesting’s AI capabilities are much stronger than other user-testing alternatives on this list. If you’re having trouble understanding your users on a granular level, this AI-powered platform will process the data and provide you with nuanced insight.

AI-powered Sentiment Analysis in UserTesting

Key features:

• Interactive Path Flow aggregates user interactions into customer journeys, giving you a better grasp of quantitative data than other human insight platforms
• Click Maps highlight which screen elements people users engage with
• Sentiment Analysis automatically surfaces negative or positive emotion points in completed interview sessions
• Intent Path uncovers user intentions as they navigate through the app, including how they correlate with the goal of a given flow
• Instant Insight generates post-test results, highlighting anomalies and identifying similarities across sessions
• Contributor Network gives you access to a large pool of testers
• IA Testing supports various usability tests, including tree tests, card sorting, and prototype testing

Cons:

• Testers hired through the UserTesting platform often don’t pay as much attention to tests as product teams would like
• The mobile testing experience isn’t user-friendly because of the screen overlay and tasks

Pricing: Available upon request.

Survicate: best for in-app user feedback collection

Survicate is a survey tool that helps you enhance insights gained from usability testing.

Use case: It’s best to use customer feedback to back your key research data—not as an alternative solution. You can integrate Survicate with Smartlook to complement your session recordings and filter sessions by particular responses.

Filtering Smartlook session recordings by Survicate feedback

Key features:

• Drag-and-drop functionality for creating surveys, no coding required
• In-app surveys collect user feedback directly from your application
• Over 400 ready-to-use survey templates, from CSAT surveys to usability evaluations, with AI-assisted search and personalized recommendations
• Audience targeting directs surveys to custom user segments
• Event-based surveys trigger feedback collection based on specific user interactions or events within your app 
• Automated feedback collection through recurring surveys
• A wide range of native integrations with third-party tools

Cons:

• Lack of AI-powered feedback analysis capabilities
• Slightly more expensive than other survey tools

Pricing: Survicate offers a 10-day free trial. Paid plans start at $53/250 responses/month.

Global App Testing

Global App Testing is a usability testing platform for teams looking to outsource testing initiatives.

Use case: The key difference between Global App Testing and other testing platforms is reliability. Through Global App Testing, you can access a community of vetted professional testers with diverse skill sets and backgrounds. Due to its narrow focus on crowd testing, it’s most suitable during pre-launch or market expansion.

Key features:

• UX testing evaluates your app’s user experience from various angles, focusing on navigation, functionality, and overall design
• Bug detection aims to identify application errors through meticulous testing
• Open-ended questions can be used to collect additional feedback from testers regarding app usability

Cons:

• It isn’t designed for testing against your own audience

Pricing: Available upon request.

Dynatrace: best for real-time performance warnings

Dynatrace is a sophisticated software observability platform. While it primarily focuses on application performance tracking, it also supports digital experience monitoring on a granular level.

Use case: Dynatrace is great for observing an app’s responsiveness, loading time, and usability issues. The platform runs synthetic monitoring tests that simulate user interactions from different locations and devices to validate whether your application is fully functional and available to all users.

Dynatrace Synthetic Monitoring

Key features:

• Real user monitoring captures data regarding user behavior, allowing you to trace user sessions and click paths
• Session replays uncover exactly how users interact with your app
• Synthetic monitoring simulates user interactions with applications under various conditions
• AI-powered root-cause analysis automatically points out reasons behind performance issues

Cons:

• On the expensive side
• Configuration can be time-consuming for inexperienced users

Pricing: Real user monitoring costs $0.0025/session, with synthetic monitoring for an additional cost of $0.001 per synthetic request.

Go beyond traditional remote usability testing with Smartlook

Mobile usability testing is crucial at each stage of the application development process. You’ll need different testing methodologies and tools at different stages to meet specific goals. Regardless of your tactic, it’s critical to remember to back your test results with real user insights.

Smartlook stands out from the competition thanks to its in-depth user behavior recording that lets you look beyond the metrics to see how users engage with your app. Smartlook gives you a 360-degree overview of your product usability by seamlessly combining quantitative data analysis with detailed session recordings.

Request a demo or start your free, full-featured 30-day trial today.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Master mobile user-testing: 8 best tools & alternatives appeared first on Smartlook Blog.

Cross-functional collaboration in app development is about bringing together members from various departments, each contributing their unique perspective and expertise for the collective good.

Although it seems reasonable in theory, building a successful cross-functional team is a serious challenge for leaders. Over one-third of employees worldwide say their colleagues don’t collaborate enough. And data silos are the top reason for collaboration failure.

In this article, we’ll go over what cross-functional collaboration means in the SaaS environment. You’ll also learn how to implement the latest trends and best practices to ensure data remains at the center of your collaborative decision-making process.

• What we mean by collaborative product development
• Latest trends and best practices in collaborative product development
• How to build a data-driven product development process as a cross-functional team
• 3 Collaborative development challenges in the SaaS environment and how to overcome them

What we mean by collaborative product development

Collaborative product development involves people with different roles and backgrounds coming together to create (ideation), develop, and optimize a product. 

In the center of this process stands a cross-functional team — a group of experts across disciplines required to build a balanced product, including UX design, marketing, customer success, development, etc.

Product development lifecycle

Before we dive into the peculiarities of collaborative product development, let’s go over the 7-step framework of software development:

• Ideation: this is where a product team comes up with the concept of a product or major update. It’s about identifying the problems worth solving or the needs that must be fulfilled with the help of market research, competitor analysis, or historical data analysis (in the case of a product update)

• Conceptualization: once an idea takes root, it’s time to shape it into a tangible concept. This involves defining a product’s scope, target market, and core features

• Design and planning: here, a product begins to take shape visually and structurally. Designers map out user interfaces, workflows, and architectural plans

• Development: this is where the coding happens. Developers bring the design and plans to life, transforming concepts into a minimum viable product (MVP)

• Testing and quality assurance (QA): testing is how you know a product works as intended, ensuring bugs are ironed out, and quality benchmarks are met

• Launch: the product is ready to be introduced to potential customers

• Post-launch optimization: the launch is just the beginning. Continuous improvement is essential, with feedback guiding updates and enhancements

Armed with an understanding of your product’s development lifecycle, you’re ready to collaborate across each stage of the process.

Is collaborative product development right for you?

Any SaaS product will benefit from collaborative product development. Among the countless positive outcomes of cross-functional product teams, the most significant include:

• Customer-centricity. Collaborative product development teams have access to insights from different touchpoints along the customer journey, ensuring that a product addresses real user needs
• Reduced silos. Cross-functional collaboration breaks down silos between departments and promotes transparency. Breaking the silo mentality encourages knowledge sharing and reduces delays associated with data hoarding and departmental bottlenecks
• Faster iterations. With a shared sense of purpose and easy access to necessary resources, collaborative teams can resolve bottlenecks and progress through SaaS projects faster
• Data-driven product decisions. Collaborative teams have access to a wealth of data from various sources, including user behavior, SaaS market trends, and performance metrics. This data empowers teams to make informed decisions at every stage of development

According to Lukasz Dec (a lead product designer), cross-functional collaboration is key to designing great products for the following reasons. 

➡️ More innovative and user-friendly products. When different business functions work together, various perspectives, skills, and experiences come together.

➡️ Time and cost-effective. Cross-functional collaboration helps you identify and address potential problems early. This saves time and money in the long run.

➡️ Builds trust and empowers teams. When people from different departments work together, they learn to trust one another. This creates a more positive and productive work environment.

➡️ Magnifies your voice as a designer. When you collaborate with individuals across functions, you have the opportunity to share your design expertise and influence the whole company.

Lukasz Dec

Lead product designer

To reap the benefits of collaborative product development, you need to be ready to adopt its best practices and address any challenges your cross-functional team may face. The following will arm you with the necessary tools to build a consistent collaborative product development process.

Latest trends and best practices in collaborative product development

Let’s explore some of the key aspects shaping today’s SaaS collaborative product development process.

Assembling a cross-functional team

Cross-functional teams are at the heart of collaborative product development. These teams bring together experts from various departments, each contributing their unique skills and perspectives to the SaaS product development process.

Eighty-three percent of “digitally maturing” companies report using cross-functional teams. Yet you shouldn’t necessarily rush into a complete organizational shift — such a drastic change may seriously threaten your company’s productivity. Start by forming a cross-functional team for a specific project or initiative (e.g., user interface (UI) redesign) to test the waters and identify team challenges.

Here are some best practices to help you build a successful cross-functional team:

• Develop a team charter. This is a document outlining project goals as well as the roles and responsibilities of teams
• Assign a team leader. Cross-functional teams require a coordinator. Their job is to set a project’s direction, facilitate communication, and keep the team focused on its objectives
• Establish a conflict resolution mechanism. Conflict is inevitable in collaborative product development. Be sure to put together communication guidelines, including a plan for what happens when teams disagree, and appoint a reliable person to de-escalate conflict

Creating a digital collaboration workspace

The digital collaboration technology stack has evolved immensely since the rise of remote work, reaching a point where it’s no longer exclusive to remote teams.

You probably rely on a few collaboration platforms, but your existing system may not be fully equipped to support the needs of cross-functional teams. For instance, tools like Jira are a common choice for developers but aren’t very intuitive for non-software teams. 

The easiest way to overcome communication challenges in cross-functional teams is by implementing a shared digital collaboration workspace such as:

• Switchboard — a simple collaboration platform that combines whiteboard and video conferencing with existing software solutions (e.g. Figma, Notion, Google Drive, etc.)
• Nifty — a hybrid collaboration workspace that brings different teams together
• Monday.com — project management software offering customizable templates for collaborative product development

Include your customers in the process

Customers are as valuable to your collaborative development process as cross-functional teams. In fact, bringing your target audience into the design and development process is a new trend in software development called co-creation.

The following are some basic steps you can take to involve your customers in the collaborative development process:

• Identify a group of customers representing various target user personas to bring different perspectives into your product plan
• Set up a collaborative workspace where customers can interact with your team 
• Conduct interviews, surveys, and focus groups with selected customers to understand their needs. Use these insights as a foundation for co-creation
• Allow them to interact with wireframes, test product prototypes, and provide user feedback
• Establish a continuous feedback mechanism with your co-creation group. Keep them informed regarding the progress of their product ideas, including how their input influences the development process
• Recognize and reward customers who actively participate in the co-creation process. This includes early access to new features, exclusive access to beta versions, and other incentives

Co-creation results in SaaS platforms that meet customer expectations and foster a sense of ownership and loyalty among your user base. 

Fostering data-driven collaboration

We’ve finally arrived at the biggest trend in collaborative product development — integrating data insights into collaborative decision-making.

You already know that one of the biggest advantages of cross-functional teams is reduced data silos. But how exactly does it help in collaborative SaaS software development?

When data silos are eliminated, team members can access a centralized pool of data, giving them a 360-degree view of product performance, including user behavior and market dynamics. In other words, developers can incorporate critical customer data into product roadmaps by sharing a data hub with customer support, marketing teams, and other sectors.

To foster data-driven collaboration, you need technology that makes it easy for different teams to access insights. It’s crucial that your software features collaboration capabilities so cross-functional teams can work together.

Smartlook allows you to create shared and private product analytics dashboards to control who sees what so you can avoid overloading staff from different sectors with unnecessary data.

Shared and private dashboards in Smartlook

How to build a data-driven product development process as a cross-functional team

Easier said than done, huh? 

You can’t just give everyone in your organization access to the available data and let them sort it out. So what should you do to foster data-driven decision-making across your cross-functional team? Here are five steps to get you started.

Define project objectives

When we say “collaborative software development,” we don’t mean an entire organization should come together to develop each aspect of a product. We recommend that you focus on project-based collaboration.

What task do you want your team to focus on? This could be developing a new feature, a UI update, funnel optimization, or anything that improves the user experience (UX).

Once you’ve defined a goal, you need to specify the milestones you’ll be working toward. If your objective is to release a new product tour, a simple action plan may look something like this:

• Customer support: suggest pain points new customers experience during the onboarding process
• Product manager: define the scope and features of the guided product tour
• Executives: determine whether or not the project is aligned with company goals
• UI/UX designers: develop wireframes, prototypes, and visual design elements, and conduct usability testing to refine the design
• Developers: build and deploy the guided product tour feature. Closely collaborate with designers to implement the user interface
• QA testers: develop test cases, perform testing, and provide feedback for improvement
• Business analysts: implement data collection, analyze user behavior, and provide insights for optimization

Define and monitor cross-functional KPIs

Next, identify the specific data points and metrics that will help you measure the success of your project. These metrics should align with your project objectives. For a guided tour project, key data points may include user engagement, feature adoption, and time-to-value metrics:

• “Launch the guided product tour, including design, development, testing, and adoption, within four months from today”
• “Engage at least 50% of new users in a product tour”
• “Increase the user activation rate by 25% within three months of launching the guided product tour”
• “Reduce the average time-to-value for new users by 20%”
• “Reduce support tickets by 10%”

Set up a system for data collection

When you have an idea of your goals and KPIs, you’ll better understand the tools you’ll need to measure them. The next step is setting up a robust system for data collection.

Determine the primary data sources required to track and measure your chosen KPIs. These sources may include user interactions, customer feedback, and other relevant data points. For instance, setting up a guided tour will require the following analytics tech stack:

• A product management platform like ProductPlan to monitor project progress and track the contribution of everyone involved
• A product analytics tool like Smartlook where team members can see real-time statistics regarding user interactions, watch session replays, and collect critical metrics in private or shared dashboards
• A customer support analytics suite like Intercom that collects data regarding customer requests before and after a product tour release 

A Smartlook funnel representing the user flow through a product tour

It’s also a good idea to create a centralized data integration hub or platform where data from various sources can be aggregated in real time. This hub will serve as a single source of truth for cross-functional teams, eliminating data silos. 

Schedule regular data reviews

Regular data reviews are essential for keeping cross-functional teams aligned with project objectives and ensuring that data-driven insights are actively incorporated into decision-making. 

Here’s how to establish effective data review sessions:

• Set a recurring schedule for data review meetings. These meetings can be weekly, bi-weekly, or monthly, depending on the project’s timeline and complexity
• During these meetings, present key data metrics and insights relevant to your project objectives and KPIs. Use data visualization tools and dashboards to make data more accessible and understandable for all team members
• Identify trends, patterns, and areas where adjustments can be made based on the data. Use this information to prioritize project tasks and strategies
• Assign action items and responsibilities to team members based on insights derived from the data 

Iterate based on data insights

Data-driven collaboration is an ongoing process that involves continuous improvement based on project insights.

Encourage a culture of experimentation and empower cross-functional teams to propose and test adjustments based on data. Remain open to tweaking strategies and making further iterations based on the outcomes of implemented changes. 

Data-driven decision-making is iterative. As such, learning from success and failure is essential.

3 Collaborative development challenges in the SaaS environment and how to overcome them

No matter how professional your team members are, they will have to learn how to effectively collaborate. You can shorten this adaptation period and avoid conflict situations by anticipating the following challenges.

Misalignment 

Misalignment can occur anywhere in cross-functional teams — in communication, goal setting, daily procedures, etc.

Team members from different departments have varying communication styles, terminologies, and habits, so you’ll need to find a way to bridge the gap between them.

Solution: Start by aligning cross-functional teams around objectives. Beginning with your organizational goals (a.k.a. primary goals), move down the milestones of each department. Make sure people in different roles understand the goals of their colleagues, including their own.

Next, establish clear communication protocols. These include preferred channels, meeting schedules, and expectations for responsiveness.

Data silos

Data silos occur when information is isolated within specific departments or teams. This usually happens when an organization lacks a standard data management approach.

Solution: Develop a consistent data governance strategy. Define data ownership, access permissions, and data quality standards. Schedule regular data reviews to keep everyone involved updated regarding the state of the project (from different perspectives) and align collaborators around a shared strategy.

Lack of accountability

When many people are involved in a project, tasks and responsibilities can fall through the cracks, leading to project delays and decreased overall efficiency.

Solution: Use project management software that allows you to assign tasks, set deadlines, and track progress. These tools allow you to automatically route tasks, set reminders, and create KPIs to hold team members accountable.

Improve your SaaS solution with better cross-functional collaboration

Just like a top pit stop crew, your SaaS app development journey depends on how well your teams collaborate.

The trends and best practices we’ve covered (digital teamwork tools and co-creation) bring data to the table and give people outside the development team more say.

Incorporate Smartlook into your collaborative arsenal to break down data silos and put customer experience-related data at the center of your collaborative decision-making strategy. Smartlook’s real-time statistics and session replays provide cross-functional teams with product analytics insights, allowing everyone to focus on what matters most — the end-user experience.

Book a demo or start your free 30-day trial today!

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Collaborative product development: Improving SaaS products with data-driven insights appeared first on Smartlook Blog.

Say you are reviewing a session recording of a client and notice that the bottom of your page is not loading properly. In this case, click on the page analytics and check if you can see clicks on the bottom page. No clicks? That’s a sign that the problem may be with your webpage and you need to further analyze the situation with developers. 

Let’s examine how this tool will help you with product analysis:

Quick solution for evaluating product changes

Page analytics are more than suitable for evaluating product changes. Define the period you’re interested in, and heatmaps will be generated strictly from the data collected during this time. 

Changed your UI recently? Select a time range that covers the before and after and immediately compare heatmaps.

Different heatmaps for different pages

Adjust the clustering level of a heatmap by clicking on the top right button. 

By sliding to MIN, your heatmaps will show points where users clicked directly, including how many times. These heatmaps are an ideal solution for static pages where elements remain in the same place. 

On the other side, when sliding to MAX, your heatmaps will show you an approximation of all clicks on each element. This comes in handy for dynamic pages or elements that may appear in slightly different positions. A typical example is a floating window or a play button on a video. 

Choose a BALANCED clustering level if your page combines static and dynamic elements. 

Distinctive visually, exact in numbers

At first glance, the colors show you which element is most clicked on. If you’re looking for the exact number of clicks, move your mouse to the specific element, and the number will appear automatically. 

Easy to navigate

Locate the most clicked on element immediately using the Elements section on the right side of the recording. Click on the element and use it as a filter to find other recordings with the same element or use it to create a new event.

Page analytics is suitable for anyone working on product performance and analysis. It helps product managers determine weak points, and allows UX designers to access heatmaps of any pages in record time.   

Like our page analytics, or see room for improvement? Keep in mind, this is the beta version – we’re still working on it. Leave us feedback so we can improve and make it everything you wished for.

The post Introducing Page analytics appeared first on Smartlook Blog.

Read on to learn how to set up customer experience analytics that gauge user happiness and give you the necessary tools to optimize your product for maximum customer satisfaction. 

Here’s everything we’ll cover in this guide:

• What is digital experience analytics?
• The correlation between user happiness and business impact
• User happiness metrics
• How to set up digital experience analytics to understand user happiness
• Using data analytics to improve customer experience

What is digital experience analytics?

Digital experience analytics is the practice of monitoring and interpreting customer behavior to drive informed decisions regarding the further direction of product development and customer experience strategies. 

To paint a complete picture of the UX, product teams collect and analyze both quantitative and qualitative data on user behavior within a website or app. You can turn to any means of data collection to obtain the necessary information — from running customer surveys to monitoring user activity with a product analytics solution.

This in-depth analysis helps to identify user pain points, friction points in the product experience, and opportunities for enhancing user satisfaction and engagement. 

“Data is the means by which the consumer shares their story. As we read their story, we will probably see something that makes us uncomfortable.

It could be a bad website experience, email, or customer service interaction. Whatever it is, it’s our obligation to listen, respond, and optimize.

[…] Not just for leads. Not just for sales. But for the relationship.

If we focus on defining and seeking such a relationship, we will have more than a portfolio of satisfied customers.”

Joseph Parker

VP of Digital Experience at JDA Worldwide

The correlation between user happiness and business impact

In the realm of digital experience analytics, user happiness isn’t just another aspect of product performance. It has a profound impact on user engagement and, therefore, the overall success of your product.

“What if, and I know this might sound crazy, instead of thinking about adoption plans, we actually tried to have good ideas💡 and lean towards global end users’ happiness 😍”

Philippe Jost

Head of Customer Obsession at Computacenter

Engaged users are more likely to:

• Convert and upsell: satisfied users willingly convert from free users to paying customers. They are also receptive to upsell offers because they trust the value your product provides

• Stay longer: users who are happy and find value in your product are more inclined to stick to it (and spend more with your business) 

• Advocate and refer: happy users are your best brand advocates. They share their positive experiences with others, contributing to word-of-mouth marketing and driving organic growth

The longer your users stay with you, the higher your ROI. Just a 5% increase in customer retention can lead to a 25% growth in profit. 

So how do you create happy customers?

The first step toward happier users is assessing the current levels of satisfaction with your product to create a baseline for improvement. This involves building a digital experience analytics system to capture valuable insights into how users perceive your product and what they like and dislike about it. By doing so, you can craft a strategic approach to enhance user happiness and drive tangible business outcomes.

Let’s begin with the user happiness metrics you need to monitor.

User happiness metrics 

These quantitative metrics serve as the initial touchstone, helping you gauge user satisfaction and setting the stage for comprehensive root cause analysis and subsequent UX improvements.

You’ll also be using them to track the results of your product optimization efforts. By monitoring changes in quantitative data over time, you’ll be able to assess your progress and gauge the impact of your initiatives. 

Customer Satisfaction Score (CSAT)

CSAT measures the satisfaction level of customers based on a specific interaction or experience.

To calculate CSAT, you need to run a CSAT survey asking users to rate their satisfaction with your product — it’s best to focus on a particular aspect of the product experience, such as the onboarding process or a recent feature update. When you have the answers, you’ll divide the number of satisfied customers by the number of total respondents. 

Net Promoter Score (NPS)

NPS gauges customer loyalty and the likelihood of your users recommending your product to others. 

To measure it, ask your users a simple question: “On a scale from 0 to 10, how likely are you to recommend our product to a friend or colleague?” 

NPS categorizes respondents into Promoters (9-10), Passives (7-8), and Detractors (0-6), providing a snapshot of overall customer sentiment.

Customer Effort Score (CES)

CES measures the ease with which customers can complete specific tasks or interactions within your product. The goal is to assess the level of effort customers feel they need to make to achieve their goals.

To gather CES data, ask a question like: “How easy was it for you to [complete a specific action]?” Respondents typically provide answers on a scale such as “Very easy,” “Somewhat easy,” “Neutral,” “Somewhat difficult,” and “Very difficult.”

CES is particularly important for ensuring long-term user happiness. Products that consistently require low effort will keep users engaged and loyal over time. High-effort interactions, on the other hand, can lead to user frustration, decreased engagement, and, eventually, churn.

User Retention Rate

User retention rate is the percentage of customers who continue to use your product over a specific period. This metric reflects how well your product meets ongoing user needs. 

To calculate the user retention rate, use the formula:

User Retention Rate = ((Number of Users at the End of a Period – New Users Acquired During the Period) / Number of Users at the Start of the Period) * 100

Customer Churn Rate

The churn rate represents the percentage of customers who stop using your product over a specific period. It’s the inverse of the user retention rate and provides insights into how many users are leaving your product. 

Here’s how you calculate the churn rate:

Churn Rate = (Number of Customers Lost During a Period / Number of Customers at the Start of the Period) * 100

How to set up digital experience analytics to understand user happiness

Pursuing user happiness goes beyond measuring satisfaction metrics. You need to establish a consistent digital experience analytics system that not only provides quantitative UX data but also pinpoints the user emotions tied to those metrics and uncovers the “why” behind the numbers.

Choose the right analytics methods

You need to select analytics methods that align with your objective. And since your objective is to enhance UX by quantifying and interpreting user happiness, your chosen methods should focus specifically on monitoring user frustrations and sentiments.

Get ready to use a mix of quantitative and qualitative data: 

Quantitative data collection

• User surveys: design surveys that capture specific aspects of the user experience, such as CSAT, NPS, and CES. These surveys can be distributed through various digital channels, including email, in-app prompts, or after specific interactions

• User behavior tracking: use analytics tools to collect quantitative use behavior data like page views, clicks, navigation paths, and feature usage 

Qualitative data collection

To understand what user experiences affect those happiness metrics you’ve just collected,  incorporate qualitative data collection methods:

• Feedback forms: encourage users to share their suggestions and challenges directly within your app. Place feedback forms at various touchpoints to gather customer insights at relevant moments

• User interviews: conduct one-on-one interviews with selected users to delve deeper into their experiences. These interviews allow you to explore user perspectives and pain points in greater detail

“Here, I’d like to highlight something: sometimes, customers don’t really know what they want/need, even if they come with a specific feature request. That’s why it’s essential to get to the bottom of their business processes, deconstruct the challenges they face, and ask additional questions that help to understand their expectations from the product. That way, there will be significantly less friction later in the process, and the customers will reach the “aha!” moment much quicker.“

Anastasia Tatsenko

Chief Customer Officer at NetHunt CRM

• Session recordings and heatmaps: Visualize real user experiences through session recordings and heatmaps. These tools offer visual insights into how users navigate, interact, and engage with different elements of your product

Only by combining both quantitative and qualitative methods will you gain a holistic understanding of user happiness. 

Map out user journeys

To help you fully understand your customers’ digital experience, your analytics tools should capture data at every touchpoint users encounter during their journey.

Start by creating visual maps that outline the steps users take along these journeys. Document all the touchpoints, interactions, and decision points users face. 

When you’ve outlined user journey maps, define milestones that will indicate users have achieved success in a particular stage. For instance, in the early stages of the customer journey, a milestone could be completing the onboarding flow and reaching the “aha!” moment.

Configure your analytics tools to gather user happiness metrics data the moment a user completes a milestone. This could involve displaying a survey or feedback prompt immediately after the milestone is achieved.

As users progress through their journey and reach different milestones, you’ll see how user happiness metrics vary. Look for trends, patterns, and fluctuations in satisfaction scores. Do certain milestones consistently correlate with higher or lower satisfaction scores? These are the areas you should focus on for now.

Watch users interact with your app to spot points of frustration

You can’t collect real-time feedback on each and every interaction between your users and your app. Well, technically, you can, but it will create unnecessary friction. 

Instead, set up a digital customer experience monitoring system that allows you to observe user behavior unobtrusively. We’re talking about session recordings and heatmaps.

Session recordings capture real-time user interactions, allowing you to replay sessions and see exactly how users navigate, click, scroll, and interact. When watching session recordings, look for:

• Repetitive actions: users attempting the same action multiple times could indicate difficulty understanding a feature
• Abandoned interactions: abruptly abandoned actions might reveal points where users encounter challenges or lose interest
• UI confusion: detect instances where users seem uncertain about the purpose or functionality of certain elements

To make it easier to analyze session recordings, Smartlook allows you to highlight events in your session replays that can then be filtered, segmented, and quantized.

Heatmaps visually represent aggregated user interaction data. They showcase which areas of your app receive the most engagement (hotspots) and which areas receive less attention (cold spots).  

There are three types of heatmaps:

• Click heatmaps point out non-clickable elements that users are trying to click
• Scroll heatmaps show how many people make it to certain points on a page, making it easier to decide where to place critical elements
• Mouse movement heatmaps identify areas that distract or confuse visitors

Smartlook collects heatmap data and presents it in your preferred format — all you need is to know what you’re looking for.

Identify recurring patterns of frustration and focus on them. These patterns will guide your efforts to improve specific aspects of your product that are causing user discomfort.

Capitalize on positive user experiences

While identifying and addressing points of friction is essential, it’s equally important to learn from positive user interactions and experiences. Positive data can provide insight into what’s working well and help you leverage those aspects to enhance product growth.

Look for instances where users achieve their goals seamlessly and exhibit positive emotions. This could be when they complete a task, navigate smoothly, or express satisfaction. These moments are valuable because they signify that your product is delivering value as intended.

Examine the features or touchpoints that consistently lead to positive user experiences: 

• What elements are contributing to their happiness? 
• What aspects of your product are resonating with them?
• Are there specific user segments or personas that tend to exhibit high satisfaction?
• How could you replicate those experiences in other areas of your product?

Use these insights to incorporate elements contributing to positive user experiences throughout the entire user journey. 

Interpret data to drive actionable insights

No matter how deep the data you collect — if you don’t act on it, it’s useless. To connect your user happiness insights to business outcomes, you need to start interpreting your data. 

Look for connections between quantitative and qualitative findings. For instance, if users are consistently giving low scores for a particular aspect of your product (quantitative insight), delve into qualitative data to see if there are recurring issues or pain points related to that aspect (qualitative insight).

Next, develop hypotheses about the potential solutions to the identified issues. These hypotheses will guide your experimentation and optimization efforts. Whether it’s tweaking a feature, improving onboarding, or refining a specific interaction, use data-driven hypotheses to guide your changes.

Using data analytics to improve customer experience

There are no limits to what you can achieve with the help of digital experience analytics. These are the most common use cases illustrating how data helps to enhance user journeys.

Addressing pain points based on rage clicks

Rage clicks occur when users repeatedly click on the same element of an app out of frustration, usually because they expect it to perform a specific action that it doesn’t. These issues usually lead to customer frustration and low satisfaction levels. So if you spot your CSAT dropping abnormally, follow this process:

• Set up tracking for rage clicks in Smartlook
• Use heatmap and session replay tools to identify instances of rage clicks
• Dig deeper into the elements that are causing rage clicks. Is the design misleading? Are there broken links? Are users trying to access information that’s hard to find?
• Based on your findings, take action to optimize the user experience and eliminate rage clicks. This could involve making clickable elements more prominent, fixing broken links, or enhancing error messages to provide clear guidance and assistance

Analyzing rage clicks can provide valuable insight into areas of your digital product that need immediate improvement.

Driving personalization with cohort analysis

Different user segments may require a tailored approach to satisfy their unique needs. This simple strategy helps gauge the happiness of separate customer cohorts and craft experiences that resonate with them most:

• Group users based on attributes like sign-up date, behavior, or demographics
• Monitor how each cohort engages with your product or service, including how their behavior evolves
• Implement in-app surveys targeting specific cohorts to learn about the pains of particular user groups
• Use insights to create personalized user flows, offers, or features for different cohorts

Tip: Use Smartlook to run a cohort analysis. You can group users based on common characteristics or events and track engagement statistics for different cohorts in Retention Tables. When you spot engagement anomalies, set up targeted in-app surveys with Survicate — the platform integrates with Smartlook enabling you to connect survey responses to user sessions later.

Understanding user emotions with sentiment analysis

Say you receive hundreds of reviews across various online channels — how do you make sense of the piles of customer feedback? Sentiment analysis is the answer.

Sentiment analysis involves analyzing text data, such as customer reviews or social media posts, to determine user sentiment (positive, negative, neutral). It helps to identify trends in user feedback and address issues more effectively. 

• Collect user-generated content like reviews, comments, and support tickets
• Use natural language processing (NLP) tools to analyze and categorize text data by sentiment
• Identify recurring themes, positive and negative sentiments, and areas of concern

“What we must not forget in trying to increase end-user global happiness through transformation and changes is “sentiment data”, gathered from end users themselves telling us about how they feel.”

Philippe Jost

Head of Customer Obsession at Computacenter

Using positive user experiences to develop new features

Another powerful strategy is using positive experience data to develop new features or enhance existing ones. This approach involves using the data you’ve collected from users who have had positive interactions with your product to shape the direction of your development efforts. 

Here’s how you can do it:

• Analyze user feedback, surveys, session recordings, and other qualitative and quantitative data to identify instances where users have had positive experiences
• Identify the elements that resonate well with users and contribute to their happiness
• Think of how these enhancements can further amplify the positive experiences for a broader user base
• Focus on the ones that address pain points or provide value to multiple user segments

Proactively addressing churn with predictive analytics

Churn is a direct indicator of user happiness. When users choose to discontinue using your product, it’s a sign that they’re not finding the value they expected. But the problem isn’t always in the product itself — it could as well be caused by poor personalization or lack of user guidance. 

If the churn issue isn’t related to product functionality, you can address it by:

• Collecting relevant data on user behavior, interactions, and engagement with your product
• Setting up algorithms to identify common characteristics, behaviors, or actions associated with users who have churned in the past. No need to be a data analytics pro. Just use a predictive analytics tool like Churn360 — it will do all the heavy lifting for you
• Keep track of customer health scores and spot early signs of customer churn in your predictive analytics platform
• For users in the high-risk segment, proactively implement personalized interventions aimed at improving their experience and addressing potential pain points

Customer experience analytics tools

You don’t need a massive analytics kit to measure and enhance user happiness. In fact, these three digital experience analytics tools will help you gain a comprehensive view of your user experience, enough to build smooth product journeys and achieve customer loyalty.

Smartlook

Smartlook is a digital experience analytics platform that connects quantitative (revenue insights, UX metrics, etc.) and qualitative (session recordings) data that’s easy to interpret and act on. Its funnels, session recordings, heatmaps, and other features help product managers understand how users interact with their products so they can make informed decisions and enhance product experiences.

With Smartlook, you can uncover user behavior patterns or dive into individual user experiences — whatever you need to build stronger relationships with your customers. 

Survicate

Survicate is a survey and feedback platform that enables you to gather qualitative insights directly from users. 

It integrates seamlessly with other analytics tools like Smartlook, allowing you to connect survey responses to specific user sessions. This synergy between quantitative and qualitative data helps you better understand user emotions and create more satisfying user interactions.

Lexalytics 

Lexalytics is a leading sentiment analysis tool that focuses on understanding user emotions from qualitative data sources like customer feedback, reviews, and social media conversations.

Aside from sentiment analysis, Lexalytics can help you automatically categorize outstanding tickets and reviews, allowing you to focus on the most pressing topics.

Gauge product experience and maximize user happiness with Smartlook

Digital experience analytics help you understand user emotions, experiences, and frustration behind your product performance data. Only by uncovering and acting on these insights can you go beyond your hypotheses and create a product that meets the needs of the target audience.

User happiness is an asset you can convert into tangible business outcomes with the right tools in your tech stack. 

Smartlook empowers you to gain deep insight into user behavior by capturing and visualizing their interactions in real time. Through session recordings and heatmaps, you can see how users navigate, click, scroll, and interact with your product. This level of visibility allows you to identify pain points, areas of frustration, and moments of joy within the user journey.

Book a product demo to see Smartlook in action, or start your free, full-featured 30-day trial now.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Quantifying User Happiness: The Science of Digital Experience Analytics appeared first on Smartlook Blog.

When users interact with your SaaS product, they leave behind a wealth of data that you can harness to lead your product development strategy. 

The problem is that this wealth of data is just too wealthy to easily make sense of. At least not until you establish a reliable SaaS analytics system where each piece of data has its own purpose. And this is exactly what we’re going to help you with.

In this guide, you’ll learn how to turn SaaS analytics into a constant flow of insights to inform product optimization and foster relationships with users. 

• What is SaaS analytics?
• Types of SaaS analytics
• How to use data analytics to drive SaaS growth
• Best SaaS analytics software to collect meaningful data

What is SaaS analytics?

SaaS analytics is the act of collecting, processing, and analyzing product performance data (in any vertical) to gain meaningful insights and make strategic decisions regarding further product development.

It involves using analytics tools to collect and visualize data relating to user interactions, application usage, business processes, and more — the type of data depends on your area of focus and the outcomes you wish to achieve.

Types of SaaS data analytics

The first step toward successful SaaS analytics is revealing what kind of data you need to collect to draw the right conclusions (including identifying the means of data collection). 

It’s important to clearly distinguish between types of data analytics to avoid getting lost in piles of data. 

Journey analysis

A journey analysis, or funnel analysis, involves tracking and analyzing the paths that users take as they interact with your SaaS product. A funnel is a quantitative representation of the user journey, showing the statistics on how many users make it through various stages along the way.

By visualizing this journey, you can identify areas where users drop off or struggle, allowing you to enhance the user experience.

To create a reliable system for journey analytics, you’ll need to map out your ideal customer journeys first. 

When developing your product’s functionality and user interface (UI), there’s a good chance you designed intuitive, logical flows that take users toward their goals. Now, it’s time to map those flows out in your product analytics tool to verify whether your users are following the paths you’ve designed for them. 

To build a funnel, you’ll need to define the key actions or milestones that have successfully helped users reach their goals. These can be onboarding completion, profile creation, report export, and other milestones that indicate users are getting closer to realizing the value of your product. 

Mind that every key action within your product will require a separate funnel. 

Once you’re done, the next step is to identify the touchpoints where users interact with your app to reach a destination. If the target action is completing onboarding, the touchpoints might be: (1) filling in the required information about the user’s business, (2) starting a product tour, and (3) checking off all the items on the onboarding checklist.

And where do you define all these touchpoints and build funnels? Your product analytics solution should handle it. For instance, Smartlook builds event-based funnels. All you need to do is define the touchpoints, a.k.a. events, and add them to a new funnel.

The resulting funnels will look like this:

What if journey analytics discover that too many users aren’t following the pre-defined paths?

In this case, you’ll need to switch to Behavior Flows to see the real paths users take. This is where you’ll be able to spot deviations from intended paths and explore why they occur.

User experience analytics

A user experience (UX) analysis evaluates how users feel while using your SaaS product. UX insights must power your customer retention strategy to ensure continuous growth. 

Through granular customer experience analysis, you can spot pain points that hinder seamless interaction with your product. These pain points often contribute to user dissatisfaction and, if left unaddressed, can lead to higher churn rates. 

There are various methods of performing a UX analysis, each serving different research needs:

Usability testing 

This method involves observing users as they interact with your product to identify usability issues and areas of confusion. Session recordings provide an unfiltered view of how users navigate, click, scroll, and engage with your product.

Session recordings in Smartlook

Best for: Identifying specific usability issues and gathering qualitative insights.

User surveys and interviews

Collecting feedback directly from users through surveys helps you understand their perceptions and capture opportunities for improvement.

In-app surveys by Survicate

Best for: Gaining an understanding of user satisfaction.

Heatmaps

Heatmaps help you visualize user interactions on particular pages and understand where users engage the most.

Heatmaps in Smartlook

Best for: Detecting and resolving UI issues.

Revenue analytics

Revenue analytics in the SaaS environment involves the systematic analysis of financial data and performance metrics to gain insight into the financial health of your SaaS business. It goes beyond just tracking revenue figures, delving into understanding the drivers of revenue, optimizing pricing strategies, and maximizing customer lifetime value.

Depending on your objectives, you may turn to various aspects of revenue analytics:

1. Subscription metrics: analyzing subscription-based metrics such as Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), and Average Revenue Per User (ARPU) to get a clear picture of your recurring revenue streams.

2. Churn analysis: examining churn rates to understand how many customers are canceling their subscriptions over time. From here, you can identify reasons for customer churn (just turn to UX analysis) to implement strategies to minimize it.

3. Expansion and contraction revenue: monitoring the revenue generated from upselling (expansion) and downsizing (contraction) activities to gain insight into the effectiveness of your cross-selling and retention efforts.

4. CLV analysis: estimating the average value a customer brings to your business throughout their engagement to measure the real impact of your customer retention efforts.

5. Cohort analysis: segmenting customers based on their sign-up dates to analyze how revenue and churn behavior differs among different cohorts. 

Let’s pay particular attention to cohort analysis as it’s the most granular analytics method. It gives you deep insight into how different customer groups engage with your SaaS product, including how their revenue contributions evolve.

Are customers acquired during a specific period more likely to churn than others? Are there cohorts that demonstrate higher engagement and revenue growth?

You cannot access those insights in top-level reports and metrics alone. Say your MRR has plummeted, and you’re digging into churn analytics and CLV data only to see your key metrics provide no apparent trends. Only by running cohort analysis can you uncover the hidden patterns that impact your top-level SaaS metrics. 

With Smartlook, you can run a cohort analysis using retention tables. You just need to create customer segments and define the events you want to track for those segments. 

Predictive analytics

Reactive analytics is good, but preventing issues before they become problems is better. 

Predictive analytics uses historical data and statistical algorithms to forecast future outcomes and trends. In the SaaS environment, predictive analytics help to anticipate customer behavior, identify potential opportunities, and make proactive decisions that drive growth.

You can use predictive analytics to:

• Identify customers who are at risk of churning based on their historical behavior
• Predict which customers are most likely to respond positively to upselling or cross-selling efforts
• Forecast future demand for your SaaS product, aiding in resource planning, capacity management, and meeting customer needs more effectively
• Estimate the potential lifetime value of new customers, helping you prioritize acquisition efforts and tailor retention strategies

The good news is you don’t have to be a data analytics pro to benefit from predictive insights. There are predictive analytics tools that collect, process, and interpret historical data to detect early troubling signs in user behaviors. For example, SubcriptionFlow offers retention management functionality that uses historic behavioral data to estimate customer health scores and identify users that are most likely to churn.

How to use data analytics to drive SaaS growth

Not only does analytics help product teams spot gaps in product experience, but it also sets the trajectory for product growth. Let’s walk through some scenarios where SaaS analytics drives immense value. 

Building data-driven product roadmaps

A product roadmap without data is just a hypothesis. 

“Do you really know…

– What’s going on in the market?

– What worldviews your buyers share?

– What they believe? What they don’t believe?

– The inflection points?

– Where the urgency is today?

– Why customers are churning?

– The problems, signals, and roadblocks?

– The strongest drivers today? Why?

You can’t guess this stuff.

Otherwise, you’re just living in the company bubble. And that’s a terrible place to be.

There needs to be constant dialogue between sales, marketing, success, and product so that you can reprioritize the roadmap.”

Danny Asling

SaaS Marketing Leader

Are you moving in the right direction? Are customers ready for new features? You can only answer these and other similar questions if you incorporate SaaS user analytics into your decision-making process. This is how to do it:

• Prioritize features — use data regarding feature adoption, user behavior, and satisfaction to prioritize roadmap items. Features that align with user preferences and have higher adoption rates are more likely to drive growth

• Validate ideas — turn to customer feedback to predict the potential impact of feature enhancements. Simply ask users whether they see value in new features or if there are other areas that require your attention. Always A/B test changes and closely monitor product performance after releasing new features

• Iterate based on feedback — user surveys and feedback provide valuable insights into feature requests and pain points. Prioritize the most frequent requests to make iterative improvements

• Adjust based on performance — continuously monitor the impact of roadmap initiatives. If a feature doesn’t drive expected growth, consider adjusting it or rolling back

Cohort analysis plays a significant role in prioritizing product roadmap. It’s no secret that startups make a good effort to meet the needs of the most “expensive” customers, namely high-ticket user cohorts. 

So if you aren’t sure where to start with feature prioritization, dive into the behavior of high-value cohorts. Watch them interact with your product, including which features they use most and what pain points they encounter. Use those insights to inform your roadmap, and you’re all set.

Fostering product and feature adoption

Even if your SaaS product is filled with groundbreaking features, they won’t contribute to growth if they’re not adopted by your users. This is where SaaS product analytics comes to the rescue, ensuring that your offerings are not only built but also embraced by your user base.

There are many ways to measure feature adoption — the right one depends on the context:

• Method #1: tracking events (i.e. feature interactions) in your product analytics tool and measuring how many active users have adopted specific features
• Method #2: mapping out the user journey that leads to adopting a new feature and analyzing the drop-off rates
• Method #3: running a cohort analysis to track feature adoption among specific user groups
• Method #4: tracking time to value (TTV) to understand how much time it takes for users (or even specific cohorts) to adopt a feature

With cohort analysis in Smartlook, you can see how many new users interact with specific features within the first days, allowing you to gain insight into their initial adoption rate. This is particularly valuable as it helps you understand if a feature immediately resonates with users or if you need to make adjustments to enhance its appeal.

Nurturing customer relationships

SaaS analytics indirectly drives startup growth by helping you build stronger customer relationships and increase retention. 

How?

By giving you the tools to understand your customers better and deliver an experience that makes them stick.

UX analytics and predictive insights are your must-have tools here. User behavior tracking gives you invaluable insight into what engages users most and what pain points they face. You can use this data to tailor your product to the needs of your customers.

With SaaS predictive analytics, you can analyze historical data to forecast user behavior. This means you can proactively offer solutions, whether it’s suggesting relevant features, addressing potential issues, or creating targeted in-app messaging. By proactively addressing user needs, you’ll anticipate churn and build long-lasting customer relationships.

Informing product optimization and continuous enhancement

Your SaaS strategy isn’t set in stone. You need to consistently enhance and refine it to sustain growth and keep your users engaged.

Successful startups make changes before users ask about them. Sometimes they roll back, but changes that stick drive innovation and help companies keep their position in the competitive market. 

Take Instagram, for example. Users hadn’t been bombarding developers with requests to release Instagram Stories back in 2016. Yet the feature has brought immense success to the company. 

You may suggest that any update brought by such a huge company is destined for success, but that’s not the case.

In 2022, Instagram made another major release — the full-screen home feed. After getting loads of negative feedback, the company rolled back the new feature. 

Innovation involves risks, but data analytics helps you test and validate your ideas with confidence. Before a full-scale rollout, conduct A/B tests on a subset of users. Analyze how different versions of a feature perform, track engagement levels, and gather feedback. This validation process minimizes the chances of releasing unsuccessful changes.

“Let users inform your ideas, but don’t let them dictate them. Here’s how you can do that:

𝗗𝗶𝗴 𝗗𝗲𝗲𝗽𝗲𝗿: Don’t just ask what users want; find out why they want it. Understanding the underlying problem helps you craft a solution that actually works.

𝗨𝘀𝗲 𝗬𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: You know your product better than anyone else. Use that knowledge to interpret what users are really asking for.

𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹𝗹𝘆: Before making big changes, try out new ideas on a small scale. See how they resonate with users without committing too many resources.”

Carl Vellotti

Senior Product Manager at GoodRx

Best SaaS analytics software to collect meaningful data

With these SaaS analytics tools, you’ll get a complete picture of your product performance, user experience, revenue insights, and future forecasts.

Smartlook for product analytics and user behavior insights

We’ve mentioned Smartlook a lot in this guide, and for good reason. 

Smartlook is a versatile product analytics platform that offers session recording tools, heatmaps, event tracking, and funnel analysis. It combines quantitative and qualitative data analytics to give you a 360-degree view of your product experience.

From top-level dashboards to individual session replays, Smartlook provides a comprehensive perspective on how users interact with your SaaS product and helps you spot opportunities for SaaS growth.

Price: Free for up to 3,000 monthly sessions. From $55/month for analyzing 5,000 monthly user sessions and more. 

Adobe Experience Cloud for customer journey analytics

Adobe Analytics is a part of the Adobe Experience Platform designed to help organizations track, measure, analyze, and optimize their digital marketing efforts and online user experiences.

Not only does the platform uncover insights into your customer journeys, but also provides revenue analytics and predictive intelligence insights.

Price: Available on request.

Profitwell for revenue analytics

Profitwell specializes in subscription analytics, making it an excellent choice for SaaS businesses focused on revenue optimization. 

It provides insight into key subscription metrics like MRR, Churn Rate, and CLV. The platform also offers revenue optimization tools that help automate retention and billing efforts.

Price: Free for tracking financial metrics. Advanced features like automated customer recovery, pricing optimization, and billing solutions are available in custom plans.

Survicate for customer feedback

Survicate is a user feedback and survey tool that enables you to gather insights directly from your users. It offers in-app surveys, NPS (Net Promoter Score) surveys, and user feedback forms.

Survicate integrates with Smartlook, enabling you to connect your customer feedback to user behavior insights. You can filter events or session recordings by negative feedback and see what might have triggered such a negative experience.

Price: Free for tracking financial metrics. Advanced automation features like customer recovery, pricing optimization, and billing solutions are available in custom plans.

SubscriptionFlow for predictive insights

SubscriptionFlow assists subscription-based businesses with predictive insights and subscription management.

It offers retention management functionality, allowing you to estimate customer health scores and predict churn. By analyzing user behavior and subscription data, it identifies at-risk customers and suggests retention strategies to drive growth.

Price: Starts from $99/month for up to 3 users.

Make strategic decisions with Smartlook’s PX insights

SaaS analytics isn’t just about solving problems; it’s about guiding startups toward future success. It has a lot to unveil, and you need to find a way to make those insights work for you.

Smartlook deciphers user behavior and empowers startups with actionable insights to make data-driven decisions and drive SaaS growth.

SaaS analytics starts with Smartlook. Book your free demo or sign up for a free, full-featured 30-day trial today.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post SaaS analytics insights unveiled: The key role of analytics in startup growth appeared first on Smartlook Blog.

But that’s only the tip of the iceberg—interpreting and incorporating them into your product strategy is where the real challenge begins. 

Thankfully, we’re here to help product teams along the way. Be sure to continue reading to learn more about:

• Key product metrics in the B2B SaaS environment
• Vanity metrics product teams should avoid
• How to incorporate product metrics into the product development process
• How to use metrics to inform product development: 3 use cases

Key product metrics in the B2B SaaS environment

As Jason Cohen of WP Engine pointed out, choosing which metrics to track depends on who you’re presenting them to:

“Executives want financial outcomes, strategists want systemic impact, managers want team accountability, teams want credit for executing work, planners want to track progress, ops wants to know that systems are stable and secure.

They’re all correct, so how do we select metrics that satisfy everyone?”

JASON COHEN

Founder at WP Engine

In this article, we’re focusing on the metrics product teams should rely on to inform their roadmaps. Below is a list of metrics that should be included in every product dashboard.

Acquisition Metrics 

You don’t have to wait around for product usage statistics to accumulate before you can evaluate the performance of your product. Acquisition metrics will give you a good idea of how an audience perceives your product, including whether it meets their needs (or whether you’ve chosen the right audience).

What acquisition metrics say about your PX: While typically used to assess the effectiveness of marketing and sales activities, these metrics are valuable assets for young product teams as they’re good indicators of product-market fit — whether or not your product is aligned with the needs and expectations of your target market.

Conversion Rate

The conversion rate is a metric that product teams, in addition to marketers and salespeople, should pay attention to. It’s an early indicator of whether your SaaS product resonates with your target audience or not. 

Note: What you define as a ‘conversion’ depends on your customer journey and business strategy. Any key action to be a conversion, from a free trial sign-up to upgrading to a premium package. 

How to measure it: Divide the number of conversions (e.g., sign-ups, free trials, demos, etc.) by the total number of visitors to your website or landing page, and multiply it by 100 to get a percentage.

Customer Acquisition Cost

The Customer Acquisition Cost (CAC) is a financial indication of whether your product is product/market fit.

How to measure it: Add up all sales and marketing expenses involved in acquiring new customers and divide the sum by the number of new customers acquired over a specific period.

Activation and Engagement Metrics

While valuable early in the product cycle, acquisition metrics don’t say much to product teams that have already found their product-market fit—activation and engagement metrics do.

But these metrics can be tricky as every company has its own definition of product value. This is why it’s paramount that you define your own. 

“Activation isn’t the goal. It’s the leading indicator of the goal. Conversion, $$, retention, PQLs, etc. — that’s what you really care about. Your growth team might be laser-focused on improving activation only to find out that all of their work didn’t improve business outcomes.

As you scale, start to segment users and personalize activation paths to different audiences.

Not all of your users are trying to achieve the same thing. So why do you push them down the same onboarding path?

Folks often focus on simplifying onboarding, reducing friction, etc., but can’t crack the code on improving their activation rate. The problem: you shouldn’t have only had one definition of activation.”

KYLE POYAR

Operating Partner at OpenView

What activation and engagement metrics say about your PX: These metrics show how well users engage with your product features. They usually correlate with the quality of your onboarding process and your ability to understand and meet your customers’ needs in regard to product functionality.

You should use activation and engagement metrics to identify friction points users face early in their product journey.

Activation Rate

The activation rate measures the percentage of users who take a specific action or reach a milestone early in their product journey. Depending on your product, actions can include completing a tutorial, setting up a profile, or performing a key task.

How to measure it: Divide the number of activated users by the number of registered users. It’s also a good idea to calculate the activation rate by separating users into different cohorts (e.g. by registration date, customer demographics, etc.) to gain insight into the behavior of specific segments. 

“Our activation moment is when a new user sends their first document. We’ve taken three key steps to define it:

1. Identify actions/events with the ability to predict when users will convert to paid members. 

2. Conduct interviews to understand when users truly grasp the value of PandaDoc.

3. Summarize findings to select a metric related to a predictive event supported by user interviews.”

KONSTANTIN VALIOTTI

Product Director at PandaDoc

What it says about your PX: The activation rate measures how well your onboarding process guides users toward success.

Free-to-Paid Conversion Rate

A free-to-paid conversion rate represents the percentage of free users who transitioned to paid customers.

How to measure it: Divide the number of users who transition from the free version of your product to the paid version by the total number of free users, then multiply that figure by 100 to get a percentage.

A low free-to-paid conversion rate may indicate a range of issues:

• Your product hasn’t delivered on your marketing/sales messaging promise
• The free version doesn’t fully showcase the value of your paid features
• Users face friction or barriers during the upgrade process

Onboarding Completion Rate

The onboarding completion rate measures the percentage of people who have successfully gone through your onboarding flow (and thus are most likely to become loyal users).

How to measure it: Divide the number of users who have successfully completed the onboarding flow by the total number of new users over a specified time.

A low onboarding completion rate may indicate several issues:

• The flow is too long and time-consuming
• Your content is irrelevant to the needs of users
• It’s displayed at the wrong time in the wrong place
• Users are proficient enough to use your product without basic guidance

Time to Value

Time to Value (TTV) measures how fast users can get value from your product after registration.

How to measure it: First, define an action that indicates a user has realized the core value of your product. Next, create an event for this action in your product analytics software and start tracking it specifically for new users. Do this with Smartlook by creating a cohort of users who registered on the same day, then turning to Retention tables to see the statistics for the specified event day by day.

New Customer Churn Rate

New customer churn rate is a metric that focuses on understanding the churn rate as it relates to new SaaS customers.

How to measure it: Divide the number of customers who canceled or stopped using your product during a period by the total number of customers at the beginning of the said period, then multiply it by 100 to get a percentage.

Feature Adoption Rate

Feature adoption rate measures the percentage of users that actively engage with specific product features.

How to measure it: Divide the number of users who interacted with the feature over a specific period by the total number of users who interacted with your app over the same period.

Retention Metrics

Retention metrics help you assess the long-term health of your B2B SaaS product. 

What retention metrics say about your PX: These are direct indicators of how satisfied customers are in the long term. 

Customer Retention Rate

The customer retention rate gauges the ability of your product to gain long-term users.

How to measure it: To calculate the customer retention rate, you’ll need to collect data on the number of customers at the beginning of a specific period (let’s say a month) and the number of customers at the end of that period. Divide the number of customers who remained active by the total number of customers, then multiply by 100 to get a percentage.

Customer Lifetime Value

Customer Lifetime Value (CLV) is a crucial metric that quantifies the total value a customer brings to your business throughout their entire relationship with your company. It assesses the long-term revenue potential of each customer.

How to measure it: Calculate the average revenue generated from a single customer throughout their entire relationship with your company.

Customer Satisfaction Score

A Customer Satisfaction Score (CSAT) measures how satisfied your customers are with your product after interacting with it for some time.

How to measure it: Run an in-app survey asking users to rate their satisfaction with your product on a scale (typically from 1 to 5 or 1 to 10).

Tip: You can build in-app CSAT surveys with Survicate. Target various customer segments for a comprehensive understanding of customer satisfaction across the entire customer journey. 

Survicate integrates with Smartlook allowing you to back customer feedback with real-time user behavior insights. 

Net Promoter Score

A Net Promoter Score (NPS) measures customer loyalty, including the likelihood of customers recommending your product or service to others. 

How to measure it: Ask your customers how likely they are to recommend your product or service to others on a scale from 0 to 10. Based on their response, you can segment customers into Promoters (score from 9-10), Passives (score from 7-8), and Detractors (score from 0-6).

It’s worth noting that, on their own, CSAT, NPS, and other metrics don’t offer much value to product teams. You’ll need to take these metrics in context and analyze them alongside other relevant data points to see their value. 

Vanity metrics product teams should avoid

So, why not just incorporate as many metrics as possible into your dashboard? Although it may sound like a good idea, there is a risk of falling into the trap of vanity metrics.

Vanity metrics are data points that appear significant at first but fail to provide valuable insight into the actual performance/health of your product. These metrics are not directly correlated with your objectives and therefore will only divert your attention from actionable insights.

The most common examples of vanity metrics for product teams include:

• Total sign-ups 
• Total conversions
• Session duration

Instead of fixating on vanity metrics, product teams should prioritize insights into user behavior and product performance. 

Here are some tips to avoid falling into the vanity metrics trap:

• Identify your product’s primary objectives 
• Select metrics that directly relate to those goals
• Group the metrics that relate to different stages of the customer journey to make it easier to analyze the data
• Ignore metrics that aren’t aligned with your objectives

That’s it. You’re ready to track and use your product metrics to inform your product map and optimize your product for maximum customer satisfaction. Just stick to the instruction below.

How to incorporate product metrics into the product development process

Choosing the right product metrics is only the beginning. You still need to incorporate them into your product management routine. 

Set up a monitoring system 

When setting up or revisiting your product tracking system, you should know what you’re looking for. As mentioned, compiling as many product metrics as possible into one dashboard won’t help you make smarter product decisions—it will only distract you from what really matters.

These are some rules that will help you keep your product data organized and aligned with your goals:

• Map out product user journeys (you may have different user journeys for different custom segments) and define key milestones throughout
• Set up events with your product analytics software to keep track of milestones. Smartlook can track any website or in-app event, from landing page visits to saving a project in your product
• Identify the key metrics that align with each dashboard’s goal
• Make sure to set up a system for tracking each metric. For instance, while you can track conversion rates and activation metrics using Smartlook, you’ll need to add Survicate into the mix to collect user feedback 
• Stick to the “one goal = one dashboard” rule. With Smartlook, you can create  granular dashboards that focus on specific user segments, product aspects, and teams

Understanding metric hierarchy

Understanding metric hierarchy will help you allocate your resources and efforts effectively so you can focus on the metrics that will have the most impact.

To make sense of the metrics you’re tracking, try splitting them into three levels:

• North Star—this is the ultimate measure of product success. It can be MRR, CLV, or any other revenue-related metric
• Key influencers of North Star—these are the secondary metrics that have a direct impact on the North Star metric 
• Levers—these are the individual initiatives you can employ to improve Key Influencer metrics

By organizing your metrics into the levels described, you’ll create a clear hierarchy to guide your decision-making process. Now, your product team can prioritize their efforts based on the impact each metric has on the North Star.

It also helps to understand the difference between leading and lagging indicators. Leading indicators, like activation and product adoption, predict future success. Lagging indicators, such as revenue or CLV, measure the outcome of past actions. 

“Revenue is a multi-input, lagging indicator of success.

So, even though it is the ultimate measure of success for a product, you have to put it in context with other metrics to run your product properly.”

JASON COHEN

Founder at WP Engine

Compare the metrics against your goals/benchmarks

All these metrics only make sense when viewed in the context of your business goals and benchmarks. 

Once you have your North Star, you’ll need to set goals for Key Influencer metrics and levers—the milestones you’ll need to reach to achieve your primary goal. These are the milestones you’ll be comparing your actual product performance against. 

Combine quantitative and qualitative insights

You’ll need to go beyond the numbers. While quantitative data provides numerical metrics and statistics, qualitative insights will show you the user behavior and experiences behind the numbers.

Use quantitative product metrics to spot potential issues in the customer experience. Instead of coming up with hypotheses regarding what may have caused an issue, turn to qualitative insights like funnel reports and session recordings to detect real customer struggles. 

Here are a few examples of quantitative and qualitative data working together:

• Activation. Your TTV metric is too high, and you don’t know why. You proceed to analyze other activation metrics only to find out that your onboarding completion rate is low. In Smartlook funnels, you review session replays from individuals who drop from the onboarding flow. It turns out they were closing guidance pop-ups and then struggling to find help content within your UI.

Session recordings in Smartlook

• Feature adoption. Quantitative data shows that a certain feature has a low adoption rate. To understand why users aren’t engaging with the feature, you perform a funnel analysis and discover that your users aren’t following the flow you’ve laid out for them. With help from in-app surveys, you ask users about their experience with the feature only to learn they’re unaware of it.

Funnel view in Smartlook

• Customer satisfaction. Recent surveys indicate poor customer satisfaction scores within a customer segment. Through contextual micro surveys, you discover that customers in this segment are experiencing issues with a new product update.

Survicate’s in-product surveys

Revisit your product development cycle regularly

Revisiting your product development cycle regularly is a critical practice that allows product teams to stay agile, responsive, and aligned with the ever-changing needs and expectations of their customers.

Create a schedule for periodic reviews and updates to your product roadmap. Depending on the complexity of your product and the pace of the market, you may choose to conduct these reviews monthly, quarterly, or on a more frequent basis.

Prioritize friction points

Friction points are areas where users encounter difficulties, obstacles, or frustrations while using your product. While developing new features is important, focusing on resolving existing issues will lead to more immediate and tangible results.

Here’s what you need to do:

• After identifying friction points using quantitative and qualitative product data, assess the impact of each issue on the UX
• Connect the identified friction points with your overall business goals and product strategy
• Evaluate the effort and resources required to address each friction point
• Prioritize the friction points that have a negative impact on the UX but are feasible to address quickly

Use the insights to forecast future performance

Product metrics not only help product teams address issues but they also allow them to gauge a product’s future performance and anticipate potential challenges.

Use product metrics to spot early signs of UX issues and address them proactively. The most insightful metrics in this instance are customer satisfaction scores and Net Promoter Score (NPS) over time. If these begin to decline, you can still proactively investigate and address the root cause before it impacts other product performance metrics.

Collecting historic product data will help you understand cause-and-effect relationships and forecast future performance with accuracy. By analyzing past trends and patterns in product metrics, you can identify the factors that have historically influenced your product’s success and failures and address them more effectively in the future.

Search for continuous opportunities for growth

Nothing is worse for product development than sticking to a static strategy. To achieve sustainable growth, you should not only focus on addressing immediate issues but also proactively identify and capitalize on growth opportunities.

Keep a direct line of communication open with your users. Customer feedback is the best way to unearth growth prospects aligned with user needs.

Use historic product insights to make hypotheses and develop experimentation plans. These insights unveil trends, behaviors, and patterns that lay the foundation for hypotheses. 

What worked before? What didn’t? 

Use this knowledge to predict how proposed changes might impact user experiences. 

Most importantly, before pursuing growth opportunities, be sure to check whether they align with your long-term business objectives. While it might be tempting to aim for quick victories, it’s smart to ensure your efforts guide your product toward long-term success.

How to use metrics to inform product development: 3 use cases

Product metrics serve a greater purpose than simply displaying team performance during routine reviews. They should inform product team decisions throughout the entire development lifecycle.

Here are the most common use cases for incorporating product metrics:

Validating hypotheses

When considering product changes, product teams speculate about how these updates will impact UX and key performance indicators (KPIs).

By setting up clear success criteria and defining the right product metrics, product teams can test hypotheses and determine whether proposed changes are likely to yield desired outcomes.

Prioritizing feature enhancements

In a dynamic product development environment, teams are often bombarded with ideas for new features and improvements. However, it’s essential to prioritize these ideas based on their potential impact on the user experience.

Product metrics provide objective data to evaluate the potential impact of feature adjustments. By analyzing metrics like customer feedback, feature adoption rate, and customer retention, product teams can identify which enhancements are likely to foster the most user value. 

Addressing product friction

By analyzing quantitative data like activation and engagement metrics and retention rates, as well as qualitative data from user feedback and support tickets, teams can pinpoint the specific pain points users face.

With this data in hand, product teams can develop targeted solutions to alleviate them. Whether it’s improving the onboarding process, simplifying complex features, or streamlining the checkout flow, data-driven insights help teams prioritize the most critical friction points and focus on improving the UX.

Back your product metrics with PX insights

With Smartlook, you’ll bridge the gap between quantitative product metrics and qualitative PX insights so you can:

• Collect the most important product metrics including conversions, retention, and feature adoption stats 
• Analyze funnels and review session recordings to understand user behavior, including the “why” behind the numbers
• Dig deeper into CSAT and NPS scores with Survicate & Smartlook integration
• Accumulate enough historic data to make informed decisions regarding product performance and anticipate potential challenges

Book a free demo or start your free, full-featured 30-day trial today!

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Product metrics that matter: Optimizing Product management in B2B SaaS appeared first on Smartlook Blog.

Competition in the B2B SaaS sphere is about to reach boiling point as customers are becoming increasingly discerning about the products they invest in. Providing a frictionless product experience (PX) is no longer an additional perk but a key factor in the success and sustainability of your B2B product.

But is it possible to eliminate all points of friction in your SaaS product, or is that more of a pipe dream?

In short, although you can’t (and shouldn’t) get rid of product friction completely, you can minimize it.

In this article, we’ll cover what constitutes ‘good’ and ‘bad’ product friction and equip you with a strategy to help you spot and eliminate harmful friction. In addition, we’ll also cover the following topics:

• What is product friction?
• 3 Types of friction in the SaaS environment
• How to identify friction points in your product
• 5 Examples of points of friction and how to overcome them

What is product friction?

Product friction refers to any obstacle that hinders a user’s experience while interacting with a product. This includes any points of resistance that make it difficult for prospects and customers to complete desired actions.

Product friction can be the result of various factors, including a slow or glitchy user interface (UI), complex navigation, excessive steps to complete actions, slow loading times, confusing terminology, a lack of clear instructions, and more.

3 Types of friction in the SaaS environment

Friction comes in several forms, including ‘good’ and ‘bad’ friction.

Wait, is product friction ever a good thing…?

In short—yes! Good friction refers to intentional design elements that are strategically implemented to achieve specific goals. Good friction should help you guide users, prevent errors, and deliberately create delays regarding specific actions.

Examples of good friction in the SaaS environment include:

• Confirmation dialog boxes. When a user is about to perform a crucial or irreversible action, such as deleting data, a confirmation dialogue box introduces friction by popping up and asking the user to confirm their decision

Source: Slack

• Progressive onboarding. By gradually introducing users to features and offering tooltips, guided tours, and contextual help, you’re introducing good friction by enabling users to navigate your product more effectively

Source: Wingspan

• Setting password complexity. Requiring users to create strong passwords with a combination of letters, numbers, and special characters is an example of good friction that enhances security

Source: Microsoft

Bad friction, on the other hand, is any unintended or excessive obstacle that negatively impacts the user experience. There are three major types:

• Emotional friction
• Cognitive friction
• Interaction friction

Emotional friction 

Emotional friction refers to obstacles that transform the users experience into an intimidating and discouraging ordeal.

This is the hardest type of friction to describe, grasp, and combat due to its complex and subjective nature. You can only address this type of product friction if you have a deep understanding of end-user emotions and struggles.

Here are some examples of emotional friction:

• Unmet expectations—i.e. misleading ads or promises that don’t align with a product’s actual capabilities
• Loss of control—i.e. unexpected changes or actions that users can’t oversee or undo, such as accidental data deletion
• Lack of empathy—i.e. robotic interactions, especially regarding customer support and error messages

Interestingly enough, it’s emotional friction that can give you the biggest competitive advantage. 

In order to gain traction in the competitive market, you should identify friction points in your competitors’ products that bother your target audience the most—this is what we refer to as emotional friction. Only then can you create a solution that appeals to them functionally and emotionally.

“In the context of product-market fit, friction is what’s in the way of a perfect product experience. Traction is the outcome of enough people adopting a product that it removes enough friction to be worth adoption costs (time+money).

A new product won’t gain traction unless the people that come before them experience friction.

You can’t get traction without friction.”

Kenny MacKenzie

Founder & CEO Acen.ai

Source: LinkedIn Post

Cognitive friction 

Cognitive friction occurs when excessive mental effort is required to complete a task within a product. Complex workflows, difficult-to-understand instructions, and overwhelming information all contribute to cognitive friction. 

Examples of cognitive friction in the SaaS environment include:

• Unclear guidance—i.e. complex or technical jargon that makes it hard for users to understand product features
• Over-complicated workflows—i.e. too many steps to complete a task
• Product not performing as expected—i.e. error messages appear in response to certain actions

Interaction friction

Interaction friction refers to the difficulties that arise when users interact with a product’s UI. It can be caused by: 

• Ambiguous navigation—i.e. a cluttered or poorly organized UI
• Unresponsive controls—i.e. buttons or links that don’t work as expected
• Performance issues—i.e. slow loading times for pages or features

How to identify friction points in your product

Let’s walk through a simple yet actionable strategy for identifying points of friction in your SaaS product.

1. Map out the user journey 

You know there are problems somewhere in the PX, but where? 

Your first step toward answering this question is identifying specific touchpoints that may cause friction throughout the customer journey. 

User journey mapping is the process of identifying the key stages and milestones users go through, from product discovery and the conversion stage to ongoing product adoption. 

Here’s what you need to do:

1. Define user personas. You already have user personas in place, so it’s time to revisit them. Each persona should represent a specific customer segment. This will make it easier for you to visualize customer journeys for users with various needs and pain points. 

2. Outline the key touchpoints for each persona. Make a list of the critical touchpoints where user personas interact with your product. These touchpoints can include visiting your website, signing up for a trial, using specific features, seeking customer support, and more.

3. Create user journey maps. Visualize user journeys with maps and flowcharts. The number of customer journeys should correlate with the number of customer personas.

4. Identify gaps and discrepancies. Surprise-surprise, your ideal customer journey and reality may fail to align. Analyze the differences between the outlined journey and the actual customer journey—this is where you’ll identify the biggest friction points. 

Once you’ve broken down the user journey(s) into smaller steps, it’s much easier to analyze your product usage data and identify trends in the PX.

2. Look at your product metrics at each stage of the journey

KPIs like conversion rates, bounce rates, feature adoption rates, churn rates, and others will indicate whether you’re dealing with product friction and whether you need to do anything about it.

To interpret the metrics, look for patterns, trends, and anomalies that indicate areas of concern, for instance:

• Low conversion rates combined with a high bounce rate suggest friction during the onboarding process
• A low feature adoption rate may be caused by different factors—from friction in the onboarding process to unclear communication regarding new features
• A low task completion rate indicates friction in the user interface, such as confusing workflows

Once you spot an alarming pattern, you can dive deeper into the root cause by looking at the user experience from a user’s perspective. 

3. Collect user feedback

You already have some quantitative insight into your product’s performance. Now it’s time to collect some user feedback to gain access to the qualitative insights that metrics alone don’t reveal.

By asking open-ended questions, running surveys, and analyzing customer support interactions, you’ll be able to see your product through your customers’ eyes and uncover the type of friction you’re dealing with. Here’s an action plan to help you on your journey:

• Create targeted surveys to gather specific feedback from users regarding their experiences with your product. Consider running in-app surveys triggered by specific user actions
• Conduct one-on-one interviews with loyal customers. What do they have to say?
• Analyze customer support tickets and interactions. What are the most frequent issues or common user frustrations?

A combination of product metrics and user feedback will give you a good idea of the pain points your users experience. That said, you’ll still lack the necessary context to come up with a solution to minimize product friction. 

4. Look into your product analytics data

First you used product metrics to detect signs of friction. Then you turned to user feedback to understand the customer’s point of view. Now, it’s time to look at the PX through your customers’ eyes. 

These tools will help you collect the PX insights that will provide you with more context and reveal underlying patterns: 

• Session recordings—by watching these recordings, you can see firsthand how users interact with your UI, including moments of hesitation and where they get stuck
• Heatmaps—use heatmaps to pinpoint elements that users attempt to interact with but are not clickable or functional
• User paths—also known as a funnel analysis, a user path analysis highlights common sequences of actions users take within your product, including if and where they deviate from the intended flow

This is where you’ll need the user journey maps you built earlier. Once you understand how different customer segments should behave inside your product, it’s easier to spot the behaviors that don’t align with your user journeys.

Combining the insights from session recordings, heatmaps, and user paths with quantitative data and user feedback will help you spot potential bad friction points in your SaaS product.

5. Translate the data into friction points

Now that you have gathered valuable insights from various sources, it’s time to turn it into product friction points—the good kind.

For best results, consider the following steps:

1. Look for common issues that users repeatedly face, such as difficulty with specific screens or interactions
2. Group friction points into emotional friction, cognitive friction, and interaction friction to understand their nature
3. Prioritize the friction points that have the most impact on the user experience and overall product success
4. Segment friction points based on user demographics or behavior to tailor solutions to specific user needs

With this data in hand, you’re ready to design and implement the solutions that will address your friction points.

5 Examples of friction points, including how to overcome them

Friction points come in all shapes and sizes. Here are the most common friction points SaaS customers face.

1. Overcomplicated sign-up flow

Problem: An overcomplicated sign-up process is the first major source of friction in the user experience. Often, it includes too many steps making it challenging for users to register.

“It doesn’t matter how great your marketing is if your buying process is a nightmare.”

Diego Oquendo

Performance Marketing Manager at Catalyst Consulting

Source: LinkedIn Post

Identifying the problem: Analyze drop-off rates along each step of the sign-up flow to identify where users are struggling. Perform a funnel analysis with Smartlook to spot friction points in your checkout funnel.

Pair a funnel analysis with screen recordings (also accessible in Smartlook) to see what the user experience looks like behind each drop-off.

Addressing the problem: If possible, remove all friction from the registration process. There’s no place for ‘good’ friction in the sign-up flow. Leading product experts recommend that SaaS companies minimize friction in the buying process by showcasing the product first:

“Spotify helps you experience the product’s value with minimal friction.

You don’t even need to sign up—their landing page is their product”

MATT HINDS

Co-Founder at Sauce

2. Lack of user guidance

Problem: A lack of user guidance can be a significant friction point for SaaS customers. 

Identifying the problem: There are quite a few indicators of poor onboarding. Start with measuring the time it takes for users to unlock the core value of your product. If it takes too long for users to achieve value, it’s a sign they’re lacking proper guidance.

In Smartlook, you can assess your product’s time-to-value (TTV) by running a cohort analysis. This involves setting up tracking for a specific group of people (e.g. users that signed up over the last month) to identify how many of them completed the first key action (e.g. save the project). All you need is to define a cohort, a.k.a. a customer segment, and define the event you want to track.

Once you’re all set, you can access detailed stats regarding the cohorts’ activity over a specified timeframe in the retention table:

In the Y-axis in the above table, you can see users split into groups by the day they signed up for a free trial. The X-axis refers to the number of users who took the specified action (in this case, file upload) nine days following their registration. To assess whether your new users are achieving the first milestone fast enough, compare the number of users taking action to the benchmark TTV (oh yea, you also need to set a benchmark).

“If you’re a product manager that keeps an eye on conversions and new business in regards to a product, cohorts will help you a lot.

Every time you put a cut in each of the steps your users make along their journey, you make the data set smaller and more controllable. It’s like freezing a group of users in time.

Cohorts also give you more influence. So the smaller the parts that you chop, the better.

If you look at users that convert and bring money to your business, every chop you make, the more control you have. Let’s say your business has 100 new users. They may convert. But, so what?

If you build a cohort and aggregate, let’s say, 100 users from this campaign on this channel, it gives you more control over what you can analyze. For example, you can dig deeper into:

The time to value (how much time it takes for your free users to turn into paying clients). Do we have the right amount of free features, including behind-the-paywall features? How is our product performing over a specific period of time?

So, cohorts make it possible to improve your product in a product-led growth framework. Cohorts are actually the only way to make an impact on a product and have better control over it.”

MICHIEL VERMEULEN

Growth & revenue manager

Addressing the problem: Unlike the conversion journey, an onboarding flow benefits from ‘good’ friction (adding a layer of educational content before letting new users explore your product).

3. A confusing user interface

Problem: A cluttered UI is a common point of product friction. It often happens when a company incorporates too many features or wishes to create a design that “stands out.”

Identifying the problem: Turn to heatmaps and session recordings. Heatmaps allow you to visualize quantitative data in regard to recurring patterns in user behavior, such as areas of interest and the ones that get ignored. From here, you can dig deeper into the exact reasons behind these trends by reviewing session replays.

Session recordings represent qualitative data that helps uncover the behavior of individual users (which make up patterns when put together). These recordings will show you how users interact with your UI and point out where visitors get angry, lost, or confused.

Addressing the problem: The solution depends on the exact problem you’ve identified during your analysis. But you’re likely to start by improving the visual hierarchy of your UI and creating a consistent layout. You should also consider simplifying the navigational flow by reducing the number of clicks required to reach essential features.

“Recently, we had a major UX challenge with our product: users were finding certain flows unintuitive. Here’s what we did to fix it:

We built and maintained a “core flows” library. Examples for us included setting up alerts, dashboards, investigating outages, etc.

User testing—we started internally since our product is used by engineers. Now every new employee is put into a pool for testing, and we actively recruit our users

We systematically grade and improve the UX towards A’s

A = new user can complete with no guidance 

B = users can complete with conceptual instructions 

C = users require step-by-step instructions 

This way, all major flows are tested, and friction is minimized before GA release.”

ROSS LAZEROWITZ

Head of Product at Observe, Inc.

4. Failure to reach the “aha!” moment

Problem: The “aha!” moment is the point in the user onboarding process where users experience the true value and benefits of your product. In other words, it’s the moment they decide to stick with you. Failing to reach this moment as early as possible in the user journey can lead to user disengagement and churn.

Identifying the problem: This is a difficult one. First off, you need to define when the “aha!” moment happens along the user journey. To validate it, you need to trace the correlation between this moment and user retention—do users that reach it stay with you longer?

Once you’ve defined the “aha!” moment, you can set it as an event in Smartlook and track how many people (if any) reach it. 

The funnel view will work best for uncovering insight into the user journey. Since the “aha!” moment is rarely just one event but rather a sequence of actions, a funnel analysis will help you understand whether users are completing the path you’ve outlined for them, including where they’re experiencing problems. 

Addressing the problem: If the data shows you need to get your users to reach the “aha!” moment faster, you should focus on optimizing the onboarding process. You can start by tailoring the onboarding experience based on user personas and their specific needs. Walk users through the most critical aspects of your product with interactive guides to ensure they understand and experience its value early on.

5. Too much information

Problem: While providing detailed onboarding guidance is helpful, logging into a new app and suddenly being bombarded with multiple instructions can be irritating. Failing to strike that balance can lead to information overload and visual noise.

Identifying the problem: Observe user behavior to see if visitors appear confused or spend excessive time trying to process information. Also, be sure to pay attention to how many users abandon certain screens or features quickly, as it may indicate information overload.

In Smartlook, you can create a funnel for tracking the onboarding process—just define the milestones, a.k.a. events, for your funnel and activate monitoring. 

Addressing the problem: Simplify the screen and trigger one experience at a time. Avoid bombarding users with multiple prompts or options at once, and give them time to absorb the information and take action.

“An example of product friction we recently overcame is overwhelming users with too many options. By breaking down our user onboarding checklist into two parts, we saw double-digit gains in our completion rate—from less than 2% to about 25%.

Initially, our onboarding checklist had several steps and was the same for all user groups. So we decided to split the list out across post-purchase and trial, creating separate lists for each phase of the experience. 

The first checklist now focuses on installing the Appcues Builder. Once the first checklist has been completed, it triggers the second checklist, which includes 3 items: create a flow, track an event, and create a goal. 

The idea is to daisy-chain the checklists together so folks don’t have a 45-page to-do list. They have just a couple of little setup tasks—truly bite-sized tasks, teeny little things. This makes each step more palatable and easy to complete.”

RAMLI JOHN

Content Director at Appcues and the author of Product-Led Onboarding

Identifying and reducing user friction with Smartlook

The goal of product designers and developers is to strike a balance between good and bad friction. While good friction can guide users and enhance PX, bad friction creates unnecessary barriers to the user experience. 

To draw a line between these two and minimize bad friction, turn Smartlook’s PX insights. The platform offers a set of product analytics tools to help you go as deep as possible into the points of friction in your PX and the context behind them. With event funnels, heatmaps, and session recordings, you’ll be able to access a visual representation of your product’s quantitative and qualitative data helping you understand and resolve user pain points.
Schedule a Smartlook demo or start your free, full-featured 30-day trial today.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Create a Seamless User Experience: 5 Ways to Overcome Product Friction in B2B Saas appeared first on Smartlook Blog.