
Cohort analysis is a powerful tool for improving user onboarding and retention. By grouping users based on shared characteristics – like signup date, acquisition source, or specific actions – they took, you can uncover patterns and identify where your onboarding process is falling short. This helps you make data-driven changes to improve engagement and retention.
Key Takeaways:
- What is Cohort Analysis? Group users into smaller segments (e.g., by signup date or behavior) to track their actions over time.
- Why Use It? Identify friction points in onboarding, improve retention, and tailor experiences for different user groups.
- How to Start: Use tools like Mixpanel or Amplitude, track key milestones (e.g., completing a tutorial), and create cohorts based on time, acquisition source, or behavior.
- Examples: Companies like BukuKas and Calm used cohort analysis to refine onboarding, leading to significant retention and engagement improvements.
- Next Steps: Analyze retention curves, test changes with A/B testing, and iterate based on insights.
Cohort analysis transforms raw data into actionable insights, helping you refine your onboarding process and keep users engaged. Start small, focus on key milestones, and continuously improve based on your findings.
How to track and measure user onboarding
Setting Up Cohort Analysis for Onboarding
Cohort analysis might sound complex, but getting started doesn’t require a heavy-duty technical setup. What you do need is a solid foundation: proper tracking and tools that let you segment users effectively. Let’s break down how to set this up for your startup.
What You Need for Cohort Analysis
To conduct effective cohort analysis, you’ll need analytics tools that can handle detailed user segmentation and behavior tracking. While Google Analytics is a common go-to, it often falls short when it comes to advanced cohort analysis. Tools like Mixpanel and Amplitude are better suited for creating and comparing user cohorts based on behavior.
Look for platforms that can pull in data from multiple sources, store historical records, and provide clear, visual reports. Integration with your existing systems is key – tools that connect seamlessly with data warehouses will save you a lot of headaches.
Next, define specific onboarding milestones within your product. These are the key actions that reflect progress, such as completing a profile, finishing a tutorial, or using a core feature for the first time. Tracking these milestones – like clicks or form completions – will help you understand how users navigate the onboarding process.
Take Ticketmaster as an example. They used Mixpanel to segment their B2B users into cohorts like venues, artists, and promoters. By tailoring messaging and running A/B tests for each group, they significantly boosted their marketing ROI.
Once you’ve established your tools and milestones, it’s time to create meaningful cohorts for deeper insights.
How to Create Onboarding Cohorts
Start by grouping users based on shared characteristics. One of the most common methods is time-based cohorts, which organize users by when they signed up – daily, weekly, or monthly. This approach helps you compare onboarding performance across different groups over time.
You can also form acquisition-based cohorts, grouping users by how they discovered your product, such as through organic search, social media, referrals, or paid ads. Another option is behavioral cohorts, which focus on specific actions during onboarding, like completing a tutorial or connecting an integration.
Here are some practical cohort types to consider for onboarding analysis:
- Acquisition cohorts: Group users by their signup source or campaign.
- Completion cohorts: Focus on users who finish key onboarding steps.
- Engagement cohorts: Track users who perform certain actions within their first few days.
- Demographic cohorts: Segment users by factors like location, company size, or job role.
- Technographic cohorts: Divide users by app versions or device types.
For example, CodeSpark, a children’s education software company, improved retention by analyzing cohorts based on acquisition sources. They discovered that users who joined through their Hour of Code program behaved differently than those from school programs. By tailoring features to each group, they were able to keep students engaged longer. This shows how targeted cohort segmentation can directly improve onboarding success.
When defining cohorts, make sure each group is large and consistent enough to reveal reliable trends. Once your cohorts are set, implement precise tracking to monitor their progress.
Data Collection and Tracking Best Practices
Accurate tracking is the backbone of cohort analysis. Start by defining two key events: the "start" event (e.g., when a user signs up or first opens your app) and "return" events (e.g., logging back in or using key features).
Track both user-level and company-level behavior, as needed. Filter events by relevant dimensions like user properties or company attributes. Choose time intervals that align with your product’s usage patterns – daily cohorts work well for apps with frequent use, while weekly or monthly cohorts may suit products with longer cycles.
Focus on meaningful metrics tied to your business goals, such as retention, revenue, or feature adoption. Avoid getting distracted by vanity metrics. Use visual tools like charts and retention curves to identify trends and pinpoint drop-off points. Keep your data up to date and supplement it with qualitative feedback from customer support or user research.
A great example is Hypercell Games, which used Adjust’s cohort analysis to track retention by campaign and install window. By identifying underperforming cohorts and combining insights with predictive LTV modeling, they achieved a 30% revenue increase.
Lastly, always ensure your tracking complies with data privacy regulations to protect user information.
Finding Onboarding Problems Using Cohort Data
Cohort data is like a magnifying glass for your onboarding process – it helps you spot exactly where things go wrong. By diving into these insights, you can uncover why users abandon your product during onboarding and take steps to fix it. Let’s break down how retention metrics highlight these trouble spots.
Analyzing Retention Rates Across Cohorts
Retention metrics tied to specific cohorts – groups of users based on their start date – offer a detailed view of onboarding performance. Unlike general retention stats, cohort analysis focuses on user behavior over time, giving you a clearer picture of patterns and areas for improvement.
Start by looking at your retention curves over various timeframes. Research from Amplitude shows that most apps lose over 80% of users within the first month. While this might sound alarming, the timing and severity of drop-offs tell you where onboarding issues might exist.
Pay close attention to these key periods:
- Days 1–3: A steep decline here often signals friction or a delay in delivering value.
- Day 7: A sharp drop after strong Day 1 retention suggests weak engagement after onboarding.
- Session Frequency: If users return sporadically or without much interaction, it’s a sign they’re not finding value.
Review these metrics weekly to reduce noise from daily fluctuations. Also, compare retention rates across acquisition channels to see which sources bring in users who stick around longer.
The stakes are high. Even a 5% boost in customer retention can increase revenue by 25–95%. Plus, keeping existing users is far cheaper than acquiring new ones – it costs 5–6 times more to bring in a new customer.
Spotting Patterns and Trends
Retention stats give you a starting point, but the real gold lies in identifying patterns that reveal weak spots in the user journey. By segmenting users based on how they found your product or what actions they took, you can pinpoint consistent drop-off points.
Here’s what to look for:
- Acquisition Source Variations: Group users by how they discovered your product – organic search, social media, paid ads, or referrals. This helps you see if your onboarding effectively communicates your product’s value.
- Behavioral Differences: Create cohorts based on specific actions users take during their first sessions. For example, which onboarding steps lead to higher retention? Behavioral segmentation can answer that.
A great example is Calm, the meditation app. They experimented with daily reminders and found that users who set them had 3x higher retention rates. Initially, this feature was hard to find, so Calm updated their onboarding to encourage new users to set reminders. The result? Retention tripled, and the change became a permanent part of their app.
"Cohort analysis is an extremely useful tool for understanding what happens to the behavior of users in your app when you’ve made a change or an optimization, whether that’s on the product side or in your marketing outreach." – Prashansa Shrestha, Content Writer, Adjust
If you notice users consistently dropping off after the same onboarding step, it’s a clear sign that part of the process needs reworking. Supplement your data analysis with qualitative feedback from user surveys, support tickets, and interviews to understand the "why" behind the numbers.
Using Charts to Read Your Data
Once you’ve spotted trends, visualizing the data makes it easier to understand and act on. Tools like cohort tables, line charts, and heatmaps can help you clearly map out retention patterns.
- Cohort Tables: These tables organize data by user groups (based on when they started) and track their behavior over specific time periods. Adding color coding can highlight churn levels at a glance.
Here’s an example of a simple cohort table:
Cohort/Days After Install | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|---|
Week 1 Users | 100% | 45% | 32% | 28% | 25% | 23% | 21% | 20% |
Week 2 Users | 100% | 42% | 29% | 25% | 22% | 20% | 18% | – |
Week 3 Users | 100% | 48% | 35% | 31% | 28% | 26% | – | – |
- Retention Curves: These line charts let you visualize retention over time and compare different cohorts side by side.
- Interactive Dashboards: Dashboards make it easy to dive into specific segments and uncover key trends. Focus on one segment at a time to keep insights actionable.
Take BukuKas, for example. This startup used CleverTap to track user behavior and improved new user activation by 60%. Cohort analysis revealed key insights into retention and engagement, which helped them refine their approach.
One thing to keep in mind: 30% of marketers admit they don’t trust their data due to inaccuracies. Always validate your visualizations with raw data and user feedback. Each insight should lead to specific actions – whether it’s tweaking a push notification, refining onboarding steps, or reallocating your ad budget. Ultimately, your charts should tell a story that drives meaningful improvements.
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Improving Onboarding Based on Cohort Insights
Once your cohort analysis pinpoints onboarding challenges, it’s time to act. The insights you’ve gathered serve as a guide for making precise adjustments to improve user retention.
Testing and Improving Onboarding Steps
A/B testing is a powerful tool for refining onboarding processes. By testing changes with specific user cohorts, you can measure how these adjustments impact completion rates and overall retention.
Focus on areas where users experience friction. For example, if your analysis shows users dropping out during a lengthy tutorial, try testing a shorter, more streamlined version against the original. On the other hand, if users who complete third-party integrations tend to stick around longer, consider moving that step earlier in the onboarding flow.
Take DocuSign, for instance. Through A/B testing, they increased Signer account creation by 15% and improved the transition from Signer to Sender by 10% with guided onboarding. Similarly, the food delivery app SuperEats discovered through cohort analysis that user retention plummeted after their promotional period ended. To counter this, they introduced a points and rewards system, which kept users engaged beyond the critical four-week mark.
When refining your onboarding process, prioritize these key strategies:
- Simplify steps where users drop off. For instance, reduce the number of required fields during account setup or break complex tasks into smaller, manageable steps.
- Personalize the experience. Tailor onboarding based on user behavior patterns or acquisition channels, as different groups may need distinct approaches.
- Highlight retention-driving features early. If specific features boost long-term engagement, ensure new users encounter them early in the process.
These iterative changes pave the way for continuous retention improvements.
Making Ongoing Improvements for Retention
Onboarding isn’t a one-and-done process. It requires regular updates to keep pace with your product and user base. Treat it as an evolving system that adapts over time.
Establish regular review cycles to monitor how changes affect cohort retention metrics. Weekly check-ins can catch short-term trends, while monthly deep dives can uncover longer-term patterns. Research shows that structured onboarding programs can increase retention by 50% and engagement by 60%.
For example, Batelco implemented event-triggered, data-driven campaigns that resulted in a 35% increase in app usage and a 77% boost in monthly active users.
Set clear goals and key performance indicators (KPIs) for your onboarding process. Track metrics like completion rates, time to first value, and Net Promoter Score (NPS). If cohort performance dips, analyze whether the changes stem from product updates, shifts in acquisition channels, or seasonal trends.
Automating cohort analysis can make continuous monitoring easier, helping you quickly identify and address issues. Regular check-ins – at 30, 60, and 90 days – provide valuable feedback and help you track progress. As your product evolves, revisit and refine cohort definitions to ensure they remain relevant.
Recording What You Learn
Documenting your findings and adjustments is essential for ongoing success. A centralized repository for test results, insights, and strategies helps build institutional knowledge and aligns teams on best practices.
Here’s what to include in your documentation:
- Test results and measurable outcomes. Record what was tested, which user segments were involved, and the impact on specific cohorts.
- Seasonal patterns and trends. Identify recurring user behavior shifts to better prepare for cyclical changes.
- Effective strategies and context. Note which approaches worked, while understanding that success with one segment might not translate to another.
For example, a financial institution discovered that only 60% of new users understood compliance protocols after initial training. By documenting this insight and refining their training materials with clear benchmarks, they raised the understanding rate to 85% within six months.
Don’t just rely on numbers – qualitative feedback matters too. Gather insights from user surveys, support tickets, and interviews to understand the “why” behind user behavior. This combination of quantitative and qualitative data ensures alignment across teams, from product development to customer success, and helps drive meaningful retention improvements.
Using M Accelerator‘s Framework for Cohort-Driven Optimization
Cohort analysis, when done right, turns raw data into practical strategies for improving user retention. M Accelerator’s framework bridges the gap between insights and action, ensuring that these optimizations align with your company’s larger goals. This approach lays the groundwork for tailored coaching and strategic support, as outlined below.
How M Accelerator Connects Strategy and Execution
Many startups struggle to translate cohort insights into better onboarding processes. M Accelerator’s framework brings together strategy, execution, and communication to tackle this issue. By focusing on key metrics, founders gain a clearer picture of the factors influencing user behavior. This targeted method addresses specific retention challenges while keeping efforts aligned with the company’s larger growth objectives.
Personal Coaching for Cohort Analysis
Interpreting cohort data requires both technical know-how and strategic thinking. That’s where M Accelerator steps in, offering expert-led workshops and personalized coaching. Their program includes one-on-one sessions with a success coach and weekly progress check-ins to help startups interpret data correctly and act on it promptly.
"M Accelerator is a great starting point for anyone who is considering taking the leap to start a company. It provides mentorship, support from the community, and networking opportunities. And the support doesn’t stop when the startup program ends. They are always there to support the founders through their journey."
– Ellen Deng, Founder at Vinofy
M Accelerator’s selective admissions process – accepting just 25–30 startups out of over 2,000 applicants each year – ensures that each participant receives focused attention. This approach has led to 87% of participants recovering their initial investment within just 12 weeks [33, 34]. Beyond cohort analysis, the coaching extends to business strategy, validation, and MVP development, helping startups use data insights to improve both their products and user experiences. This seamless integration makes cohort analysis a key part of a broader retention strategy.
Adding Cohort Analysis to Retention Strategies
M Accelerator helps startups embed cohort analysis into well-rounded retention strategies. Their program provides hands-on implementation support and assigns a dedicated Customer Success Manager to guide companies in adopting and applying cohort-driven enhancements.
Their strategy consulting services go a step further, ensuring teams not only analyze data but also execute plans effectively. This includes fostering collaboration, improving communication, and distributing decision-making responsibilities.
"M Accelerator has helped a lot in making a pitch deck from scratch by helping show the problem from various angles. Sessions vary from different topics such as marketing, presentation, speech which syncs into the pitch creation. In addition, one-on-one sessions help to ask any questions or help you need. Thank you."
– Jemal Meredova, Co-Founder at PinChef
Conclusion: Improving Retention Through Cohort Analysis
Taking the insights and strategies we’ve discussed, the next step is turning cohort analysis into tangible improvements in user retention.
Cohort analysis transforms raw data into meaningful insights that help refine onboarding and keep users engaged. Consider this: more than 80% of app users drop off within the first month. This statistic highlights just how critical it is to implement cohort-driven strategies to combat this trend.
But success isn’t a one-and-done deal – it requires ongoing refinement. Ramli John, Founder of Delight Path, puts it best:
"Segmentation is the single most impactful thing product teams can do to improve their onboarding experience. And the only way to know that changes in the user onboarding are working is through cohort analysis".
Start by identifying where users disengage, pinpoint behaviors linked to long-term retention, and test targeted changes to guide users to their "aha moment" faster. Tailoring the experience to reflect what works for successful cohorts is key.
However, insights alone won’t move the needle. The most successful companies don’t just analyze data – they act on it. They experiment, iterate, and fine-tune their onboarding processes based on what their cohort analysis reveals. This cycle of analysis and action ensures that insights lead to measurable retention improvements and lasting growth.
Don’t wait – start using cohort analysis now to transform your onboarding process from a hurdle into a driver of retention. Tools like M Accelerator’s frameworks can help you turn these insights into real, impactful growth.
FAQs
How can cohort analysis help identify and resolve issues in the onboarding process?
Cohort analysis is a powerful tool for tracking and comparing how different groups of users behave over time. It helps pinpoint where users might face challenges during onboarding. For instance, it can highlight patterns like a noticeable drop in engagement on certain days or during specific steps, such as completing tutorials or setting up accounts.
Once you identify these sticking points, you can zero in on improving them – whether that means simplifying instructions, refining the user experience, or providing more effective support. This focused approach doesn’t just make onboarding smoother; it also increases user retention and overall satisfaction.
What are the best practices for setting up and using cohort analysis to improve onboarding?
To set up a solid cohort analysis, the first step is to make sure your data is accurate, consistent, and current. Without reliable data, it’s tough to identify meaningful patterns or trends. Start by defining the key milestones you want to track during onboarding – this could include when users sign up, take their first action, or hit an activation event. These milestones will help you measure progress over time.
Next, group users into cohorts based on shared traits, such as their signup date or specific behaviors. This segmentation allows you to spot patterns and trends more effectively. To make it easier to track progress, use visualization tools to monitor these milestones and pinpoint areas that need attention.
Keep in mind that your tracking parameters aren’t set in stone. Regularly revisit and adjust them to stay aligned with your business goals – especially if you’re in a fast-moving startup environment. By fine-tuning your approach, you’ll be better equipped to tackle onboarding challenges and improve retention rates.
How can businesses use cohort analysis to improve onboarding and boost retention?
Cohort analysis allows businesses to uncover patterns in user behavior by grouping individuals based on shared traits or actions within a certain time frame. By studying these groups, companies can identify when users disengage during onboarding and determine which actions contribute to higher retention rates.
Armed with this information, businesses can take actionable steps to improve the onboarding experience. This might include streamlining workflows, tailoring communication to specific needs, or improving essential features. Over time, this focused strategy helps boost user engagement, minimize churn, and cultivate lasting loyalty.