Underutilized features in SaaS are tools or functionalities users rarely use, overlook, or engage with only at a basic level. This wastes resources and impacts user retention and revenue. To address this, focus on:
Key Metrics: Track activation rates, time-to-adopt, usage frequency, and stickiness.
Data Insights: Use tools like heatmaps, session recordings, and cohort analysis to understand user behavior.
Prioritization: Apply an Impact Effort Matrix to decide which features to fix first.
Engagement Strategies: Use in-app guidance, email campaigns, and feature rollouts to boost adoption.
Measure Results: Monitor metrics like drop-off rates and retention to assess improvements.
The Feature Adoption Funnel: How to measure feature usage the right way
Setting Up Your Feature Usage Baseline
Before pinpointing underperforming features, it's crucial to establish baseline metrics for user interaction. These benchmarks serve as a guide for tracking trends and identifying areas for improvement. Let’s dive into the key metrics that form this foundation.
Key Metrics for Feature Adoption
Building a baseline starts with identifying the right metrics. These metrics help you measure how users interact with your product's features and reveal patterns in their behavior.
Activation rate is one of the most important metrics. It measures the percentage of users who try a feature after encountering it. On average, the adoption rate for key features hovers around 24.5%, with 20-30% being a reasonable target for many SaaS products.
Time-to-adopt tracks how quickly users engage with a feature after signing up or being introduced to it. This metric can highlight whether your onboarding process effectively directs users to valuable features.
Usage frequency measures how often users return to a feature after their initial interaction. A high adoption rate might not mean much if users don’t continue engaging with the feature, which could indicate the need for optimization.
Breadth of adoption examines how many features a user interacts with across your platform. Users who explore multiple features often show higher retention and lifetime value.
Depth of adoption goes beyond surface-level engagement. For example, instead of just viewing default charts in an analytics tool, meaningful engagement might involve users creating custom reports.
Here’s an example to illustrate these metrics:
Whatfix studied a group of 2,000 new users in March. Of those, 800 clicked on an in-app pop-up for a new feature (40% exposure rate), 700 activated the feature (35% activation rate), 400 used it in a meaningful way (20% usage rate), and 200 became repeat users (10% retention rate).
When defining these metrics, be clear on what qualifies as an “active feature user.” A single click might not be enough - you may want users to complete a workflow or achieve a specific outcome.
Stickiness measures the ratio of daily active users to monthly active users for a feature. High stickiness signals ongoing value, while low stickiness suggests users might try the feature once and never return.
Drop-off rate pinpoints where users abandon the feature adoption process, helping you identify friction points in the user experience.
Getting User Behavior Insights
While quantitative metrics reveal what’s happening, qualitative data uncovers the “why.” Combining these two perspectives gives you a well-rounded understanding of user behavior.
Tools like user session recordings can show exactly how users interact with your interface. For instance, if users hover over a feature button without clicking, it might indicate confusion about its purpose.
In-app surveys are another way to gather real-time feedback. Questions like “Did you know this feature existed?” or “What stopped you from using this feature?” can help identify barriers to adoption.
Your customer success team can also provide valuable insights by gathering direct feedback from users about pain points and feature requests.
Cohort analysis helps you compare feature adoption across different user groups. For example, new users might struggle with complex features, while enterprise clients could overlook tools designed for smaller teams.
Using a product analytics tool like Userlens simplifies the process of collecting and interpreting this data.
Event tracking is another essential tool. By monitoring actions like feature discovery, first-time use, and ongoing engagement, you can map out the user journey more effectively.
Finally, choose a timeframe for your analysis - daily, weekly, or monthly - based on how often users interact with your product. For instance, B2B tools may benefit from weekly or monthly tracking, while consumer apps often require daily monitoring.
With a strong baseline in place, you’ll be better equipped to measure the success of your optimization efforts and make informed decisions about where to focus your resources. This data lays the groundwork for using product analytics to identify and improve underutilized features.
Using Product Analytics to Find Underutilized Features
Once you've set a baseline for how your product's features are being used, the next step is diving into analytics to uncover patterns in user engagement. These tools turn raw data into actionable insights, helping you identify which features are being overlooked and where users might be running into trouble.
Product analytics tools are designed to break down user behavior by examining their interactions. They can show you which features are most popular, where users struggle, how they find value, and even which parts of the onboarding process might be causing drop-offs. Instead of drowning in unnecessary data, it’s crucial to focus on specific goals - like spotting features with low adoption despite being prominently displayed or identifying user groups that skip over certain functionalities.
Tools like Userlens offer pre-built dashboards tailored for B2B SaaS companies. These platforms provide instant insights into account-level usage patterns and feature adoption trends without requiring complex setups or technical expertise. With these insights, you can make targeted improvements to boost feature activation.
Heatmaps and Usage Trends
Heatmaps are a great way to visually map out user activity on your platform. By using colors to represent different levels of activity, they make it easy to see which parts of your interface are getting attention and which are being ignored.
Usage trend analysis takes it a step further by tracking feature adoption over time. This helps you understand not just what users are doing now, but how their behavior changes. For example, you might notice that a feature sparks initial interest but quickly loses traction, signaling a possible onboarding issue. On the flip side, some features might start slow but gain steady, long-term adoption. By combining these insights with targeted in-app guidance, you can address gaps. If, for instance, a heatmap shows users consistently skipping over a particular dashboard section, you can highlight its value to encourage engagement.
Cohort Analysis for Better Insights
Heatmaps are just the beginning. To dig deeper, cohort analysis helps explain why certain patterns occur by tracking groups of users over time. This method allows you to segment users by shared traits - like company size, onboarding date, or industry - and see how their behavior evolves. For example, you might find that enterprise clients gravitate toward advanced reporting tools, while smaller businesses prefer simpler features.
Time-based cohorts are particularly helpful for measuring the adoption of new features. For instance, you can track how many users from Week 1 try a feature versus those in Week 4. If adoption drops from 15% in Week 1 to 8% in Week 4, it might signal that initial interest isn’t being sustained. These insights can guide you in personalizing messages and tweaking onboarding flows to better suit different user segments.
In December 2019, BukuKas, a startup focused on digitizing SMEs, used cohort analysis to refine their onboarding and engagement strategies. This approach led to a 60% boost in conversion rates.
How to Fix Underutilized Features
Once you've analyzed your data, the next step is to make underused features more engaging. The goal is to identify practical fixes that balance potential impact with the effort required.
Choosing Which Features to Fix First
The Impact Effort Matrix is a great tool for prioritizing which features to tackle. It helps you categorize features based on their potential business impact and the amount of effort needed to improve them.
Here’s how features typically fall into four categories:
Quick Wins (High Impact, Low Effort): These are the low-hanging fruit. Features in this category can significantly improve user satisfaction or retention without needing extensive development work. Focus on these first.
Big Bets (High Impact, High Effort): These features have the potential to make a huge difference but require considerable resources and planning. Approach these with a clear strategy and allocate the necessary time and budget.
Fill-ins (Low Impact, Low Effort): These are minor fixes that can subtly improve the user experience. While they’re easy to implement, they don’t drastically affect overall engagement. Address them later.
Money Pits (Low Impact, High Effort): Features in this category are best avoided. They demand significant effort but offer little improvement to user satisfaction or key metrics. Redirect your resources elsewhere.
When estimating impact, think beyond just usage stats. Consider how improvements could enhance customer satisfaction or deliver added value. Use measurable benchmarks - like revenue growth, retention rates, or user satisfaction scores - to evaluate potential gains. Effort should also be assessed systematically, factoring in development time, complexity, and resource needs. It’s also smart to involve stakeholders early in the process to ensure alignment and secure buy-in.
Once you’ve prioritized, the next step is to launch targeted campaigns to encourage feature adoption.
Creating Targeted Activation Campaigns
After deciding which features to focus on, activation campaigns can help boost their usage. Tailor your efforts to guide users toward features that deliver the most value.
In-app guidance is one of the most effective methods. Features like tooltips, modals, and guided tours can direct users to new functionalities at just the right moment. For example, 3P Learning used a mobile modal to highlight a feature, resulting in a noticeable increase in engagement. Timing is crucial - show these prompts when users are already interacting with related features.
Trigger prompts when they’re contextually relevant. For instance, if a user frequently accesses your reporting dashboard but hasn’t tried advanced filtering, a tooltip explaining filtering options during their session can make a big difference.
Email campaigns can also be effective for educating users about features they haven’t explored yet. Segmentation is key here - customize your messages based on user roles, company size, or current feature usage patterns to ensure relevance.
A gradual rollout of features can prevent overwhelming users. Dropbox, for example, introduced new functionalities in stages during its period of rapid growth. This approach allowed users to adapt gradually and explore features at their own pace.
Milestone tracking is another great way to keep users engaged. Dashboards that showcase mastered features and suggest the next logical steps can tap into users’ natural desire to complete tasks and try new things.
Finally, collect feedback through micro-surveys after users interact with promoted features. This helps you evaluate the success of your campaigns and pinpoint any remaining hurdles. Tools like Userlens can track interactions and provide insights with dashboards and heatmaps.
Tracking Progress and Making Improvements
Once your activation campaigns are live, the next step is all about tracking their impact and refining your strategies. By closely monitoring results, you can make informed adjustments to ensure long-term success in improving feature adoption.
Measuring Your Optimization Results
Start by building on your baseline metrics and use A/B testing to validate your efforts. In these tests, half of your users experience the original setup while the other half interact with your updated features or prompts. This method eliminates guesswork and gives you clear evidence of what’s working.
Before implementing changes, establish a two-week baseline to understand normal adoption patterns. Without this groundwork, it’s impossible to distinguish real improvements from random fluctuations.
Key metrics to track include:
Activation rate
Time-to-adopt
Breadth and depth of adoption
Stickiness
Drop-off rate
For reference, the average adoption rate for core features sits at 24.5%.
Take PlaceMakers, for example. This retail app used funnel analysis and session recordings to uncover a major UX issue during checkout. By redesigning the problematic interface, they managed to double their in-app sales.
Segmentation is another powerful tool. Breaking down metrics by user role, company size, or feature usage patterns can reveal important trends. For instance, small businesses might adopt features differently than large enterprises, or certain user groups may need tailored activation strategies.
Combine quantitative data with qualitative insights for a fuller picture. Session recordings, for example, provide context that numbers alone can’t. JobNimbus used this approach to pinpoint pain points during a major app redesign, ultimately boosting their app store rating from 2.5 stars to 4.8 stars.
It’s also important to remember that revenue is a lagging indicator. As Jason Cohen, Founder of WP Engine, explains:
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.
Once you’ve identified which changes drive adoption, summarize your findings in clear, actionable reports to share with your team.
Creating Feature Adoption Reports
To communicate the impact of your optimization efforts, create executive dashboards that connect feature adoption to business outcomes like retention and revenue growth. The goal isn’t to overwhelm stakeholders with data but to tell a compelling story.
Focus on one impactful report rather than multiple dashboards. Tailor the metrics to your audience: executives care about retention and revenue, while product managers need detailed adoption funnels and user behavior insights.
Make your dashboards accessible. Embed them where teams work and enable custom views to ensure they’re easy to find and use. Hard-to-access reports are often ignored.
For project-based companies, consider using Row-Level Security (RLS). This ensures that different teams only see the data relevant to their work. For example, company-wide KPIs like average completion rates can be shared broadly, while detailed project breakdowns are restricted to the appropriate teams.
Keep data fresh by updating it at strategic intervals. One client found success refreshing their data twice daily - once overnight and again at 6 PM - allowing managers to evaluate performance throughout the day. Another client added stock alerts to notify their purchasing team when immediate action was needed, enabling real-time responses.
To avoid confusion, provide context and guidance within your reports. Use tooltips, introductory tabs, and simple documentation to help stakeholders understand the data. Highlight key visuals and celebrate wins by showcasing how insights have driven better decisions.
Set up automated alerts for critical metrics. For example, notify team members if feature adoption drops below a certain percentage or if time-to-adopt spikes unexpectedly. This ensures quick action without waiting for scheduled reviews.
Monitor report usage with built-in analytics. Track which dashboards are viewed most, which visuals generate engagement, and where users spend their time. Use this information to refine your reporting strategy and focus on what drives decisions.
Tools like Userlens can simplify this process by automatically tracking feature interactions and linking usage patterns to business outcomes. It can help identify churn risks or upsell opportunities, making it easier to demonstrate the impact of your optimization efforts.
Finally, encourage feedback on your reports. Invite stakeholders to comment on visuals and share insights. This collaborative approach keeps everyone engaged and ensures that improving feature adoption remains a shared priority across teams. These reports not only support ongoing optimization but also strengthen your overall adoption and retention strategies.
Conclusion: Improving Success Through Feature Optimization
Focusing on underutilized features is a continuous process that can transform your SaaS platform. The secret? Starting with the right questions. As Yali Sassoon, Co-founder of Snowplow, explains:
"You always want to start with: what is the question we want to be answering? 80% of the value is unlocked if you just ask the right questions. The best product managers are the ones that use the data to ask the most interesting questions."
Building a strong analytics foundation is crucial. It enables you to identify areas for improvement and focus your efforts where they’ll make the biggest difference. These insights fuel the strategic adjustments discussed earlier, ensuring your optimizations are targeted and effective.
Top-performing companies recognize that refining existing features often delivers more growth than constantly launching new ones. HubSpot is a great example. To tackle customer churn, they used detailed analytics to uncover pain points and engagement trends. Armed with this data, they reached out to at-risk users with personalized messages and targeted offers. Their strategy also incorporated sentiment analysis and predictive analytics, helping them anticipate customer behavior and craft tailored retention plans.
Long-term success hinges on consistent monitoring. Feature adoption metrics reveal what users truly value and how you can better meet their needs. This user-first approach boosts engagement, satisfaction, and sustainable growth. Inspire Fitness exemplifies this: by leveraging session recordings, event analytics, and heatmaps, they achieved a staggering 460% increase in in-app time.
For B2B SaaS, aim to keep monthly churn under 5% while maintaining a lifetime value (LTV) at least three times your customer acquisition cost. These benchmarks help gauge the real impact of your optimization efforts. Tools like Userlens simplify this process, enabling you to track feature usage, identify churn risks, uncover upsell opportunities, and tie user behavior directly to business outcomes.
Striking a balance between user needs and business goals requires clear prioritization frameworks. Regularly revisit and refine your strategies using data-driven insights. Those underutilized features you’ve been overlooking? They’re packed with potential. By consistently improving them with precise analytics, your platform can achieve sustained and meaningful growth.
FAQs
How do I decide which underutilized features to improve first in my SaaS product?
How to Decide Which Features to Improve First
When deciding which underused features to enhance, focus on those that show low user engagement but have high potential value. Start by diving into your product usage data to pinpoint these features. Metrics like active users, retention rates, and user feedback can provide valuable insights.
Once you've identified the features, assess how improving them could impact both the user experience and your business goals. Features that address common user pain points or contribute directly to customer success should be at the top of your list. For instance, if a feature is often ignored, you might increase its visibility by adding in-app tutorials or notifications to encourage users to give it a try.
By prioritizing updates that align with what users need and what your business aims to achieve, you can make your product more valuable and improve feature adoption.
What are the best ways to understand how users interact with features in a B2B SaaS platform?
To gain a deeper understanding of how users interact with features in a B2B SaaS platform, it's helpful to combine qualitative and quantitative research methods.
Contextual inquiries are a great starting point. By observing users in their actual work environments, you can uncover hidden challenges and see how they naturally navigate workflows. This approach often highlights pain points or habits that might not surface in traditional feedback sessions. Similarly, diary studies allow users to document their experiences over time. These studies are particularly useful for spotting recurring patterns and understanding emotional responses that might not emerge in one-off sessions.
On the quantitative side, product analytics tools provide hard data on how users engage with your platform. Metrics like feature usage frequency and click paths can reveal which features are being overlooked and pinpoint areas where users tend to drop off.
By combining these methods, you can build a comprehensive picture of user behavior. This balanced approach helps you make smarter decisions to improve feature adoption and enhance the overall user experience.
How do I evaluate if my strategies for boosting feature adoption and user engagement are working?
To measure how well your strategies for boosting feature adoption and engagement are working, use product analytics to keep an eye on key trends. Pay attention to metrics like how often specific features are being used, whether engagement rates are climbing, and if there are fewer signs of user inactivity.
Also, analyze which features are connecting most with your audience. This information can guide you in fine-tuning your approach and concentrating on what delivers the most value to your customers. By staying ahead of the curve, you can ensure your efforts translate into noticeable gains in user adoption and satisfaction.