Sellers often tell you their users are engaged. But what does the data actually say? Here is how to use analytics platforms to distinguish between healthy, growing products and those on the verge of collapse before you sign the purchase agreement.
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Buying a SaaS business is rarely about the code. The code is just a means to an end. The real value lies in the users who pay for it, the retention those users demonstrate, and the predictability of the revenue stream. However, sitting across the table from a founder who claims their product has "sticky users" or "high monthly recurring revenue" is not enough to close a deal with confidence. You need proof. Specifically, you need to verify the user engagement claims that underpin the seller's price markup.
In recent years, two platforms have become the industry standard for behavioral analytics: Mixpanel and Amplitude. Most serious SaaS founders use one of these to track how users interact with their product. If a seller refuses to share access to their analytics dashboard, that is a massive red flag. But even when they do share the data, most buyers lack the skills to interpret it. They look at total active users and feel good, ignoring the warning signs buried in the retention curves and cohort analysis.
At Deal Alert AI, I have seen too many buyers walk away from promising deals because the engagement metrics did not hold up under scrutiny. The following guide is designed to help you move beyond surface-level metrics. We will break down exactly which dashboards to request, how to interpret the data, and how to identify the specific patterns that indicate a healthy user base versus a product that is bleeding at the neck.
The most common mistake buyers make is relying on vanity metrics. Total cumulative users, lifetime revenue, or overall page views are the first numbers a seller will throw at you. These numbers are cumulative. They only go up. A user who signed up three years ago and never logged in again is still counted in your "total users" metric. This creates a false sense of security. It makes the business look larger and more established than it actually is. You must shift your focus from cumulative counts to active, unique, and recurring behaviors.
Engagement in a SaaS context is dynamic. It is about the frequency and consistency of use. A subscription service is only valuable if the user logs in, performs the core action, and feels satisfied enough to pay again. If your analytics show a massive spike in sign-ups from a marketing campaign followed by a sharp decline in daily active users, the seller has failed to convert marketing spend into product value. Verifying this relationship is the core of due diligence on the user experience side.
Furthermore, standard reports often aggregate data in ways that hide user segmentation. If your product serves both enterprise clients and free tier users, the aggregate engagement rate might look healthy because the enterprise users are heavily engaged. However, if 90% of your revenue relies on the free tier users who are actually disengaged and likely to churn, the business model is fragile. You need to segment the data to see the truth. This is where platforms like Mixpanel allow for deep, custom filtering that spreadsheets simply cannot match.
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Many buyers expect to do all their discovery during the Conf Letter (LOI) stage, which is far too late. By the time you are in data room access, you are signing a binding agreement to review the company. You need visibility into analytics earlier. When you first speak with the seller, state clearly that you require read-only access to their Mixpanel or Amplitude account during the initial evaluation phase. This is a standard request for any serious investor. If they refuse to provide read-only access before the NDA is fully executed, or if they claim they "manage it all in Excel," pause. That is a major warning sign.
Once you have access, do not just look at the default dashboards the seller has built. Sellers often curate their dashboards to highlight success metrics and hide the ones that are underperforming. You need to have the raw data access or, at minimum, the ability to build your own event queries. You are looking for the raw event logs. You want to see the raw stream of user actions: login, create transaction, invite team member, etc. From there, you can build the funnels and retention tables you need to make your own independent determination about product health.
It is also crucial to verify the time period of the data you are looking at. Ensure the retention data covers at least the last 12 months. Shorter periods can be manipulated by seasonal fluctuations or specific marketing pushes. A 12-month view allows you to see year-over-year trends and identify if the engagement is improving or deteriorating. If the seller only allows you to see the last 90 days, ask why. Often, there is a declining trend they are trying to hide by showing only a short, smoothed-out window.
The single most important visualization for SaaS due diligence is the retention cohort chart. Both Amplitude and Mixpanel offer robust tools to generate this. A retention cohort chart groups users by the month they first used the product and then tracks how many of those users remain active in subsequent months. This is the ultimate truth-teller. It strips away the noise of new signups and shows you the core stickiness of the product. If you cannot read a retention cohort chart, you are not ready to buy a software product.
When analyzing these charts, look at the diagonal. In a healthy SaaS business, the retention rate should flatten out rather than drop to zero. If you look at the "Week 1" or "Month 1" column, you want to see a high percentage of initial users returning. More importantly, you want to see that the retention rate stabilizes. For example, if 40% of new users return the next month, and then 30% the month after, and then it stays at 30% for the next six months, that is a healthy "stickiness" floor. It means those users have adopted the tool into their workflow.
Conversely, a failing product shows a "cliff." Users log in once, twice, perhaps three times, and then they are gone. If your retention chart shows that 50% of users churn in the first week, and nothing stabilizes, the seller is likely relying on constant, expensive acquisition to keep the revenue line flat. They are swimming upstream. When they stop buying ads, the revenue will collapse. This pattern is a deal-breaker for most rational buyers because the Customer Acquisition Cost (CAC) will always exceed the Lifetime Value (LTV) in this scenario.
Engagement data is useless if you do not connect it to revenue. You need to merge your analytics data with your billing data. In a due diligence context, you often have to do this manually or via export. You need to identify the specific user IDs in Mixpanel or Amplitude that correspond to the customer IDs in your billing statements (Stripe, Chargebee, or Bill.com). This allows you to calculate the actual Lifetime Value (LTV) based on real usage behavior, not just average contract value.
If the seller claims a high LTV, but your analytics show that the average user who generates revenue only keeps their subscription for 6 months, there is a discrepancy. Perhaps the high LTV is driven by a small number of enterprise whales, while the median user churns quickly. This is a dangerous concentration risk. If one whale leaves, your revenue drops disproportionately. When verifying engagement, you must ask: "What percentage of revenue comes from users who have been active for less than 3 months?" If that number is high, your revenue base is unstable.
Furthermore, look at the feature adoption of paying users versus free users. In Amplitude's Property Graph feature, you can correlate specific in-app actions with payment events. For example, does a user who invites 3 colleagues have a 20% higher chance of converting to paid? If so, that is your North Star Metric. If the seller claims engagement is high, but the actions that drive conversion are rarely performed by free users, the funnel is broken. You are paying for a business that has a leak at the top of the funnel. Verify that the engagement metrics actually correlate with the bottom line before you assume you are buying a profit center.
Not all actions are created equal. In data-driven due diligence, we look for the "Moat" actions—interactions that indicate a user has deeply integrated the software into their business processes. For a CRM, this might be updating contact records or running reports. For an HR tool, it might be processing payroll. You need to identify 2-3 specific events in the seller's Mixpanel that correlate with high retention and high revenue.
Once identified, build a funnel for these specific actions. What percentage of active users perform this "Moat" action every month? If only 10% of your active users perform the core value-exchange action, the product is likely a utility with low barriers to switching. Users don't "love" it; they just haven't switched yet. This is fragile. A truly sticky product will have a high penetration rate for these core actions. For instance, if your product is a project management tool, does the user move tasks to "Done"? If 80% of active users do this weekly, you have a healthy workflow. If it is 20%, the product is just a list that they occasionally check.
Use the "Next Steps" feature in Amplitude or the "Funnel Analytics" in Mixpanel to see what happens after a user performs the core action. Do they invite a teammate? Do they purchase an add-on? Do they book a call? A healthy engagement loop should look like a flywheel. Action A leads to Action B, which leads to Action C, which reinforces the subscription. If the flow stops after the second step, the seller is missing a crucial growth and retention lever. Your job is to spot this break in the chain before you buy the asset.
Sellers are experienced. They know which charts look good. Consequently, they may manipulate the data presentation to make themselves appear more attractive. One common tactic is "mixing" trial users with paying users in your retention cohorts. If a seller defines "Active User" as someone who logs in, they might include a massive influx of trial users trying the product for a week. This skews the retention chart to look like a sharp spike and sharp drop, which can be misread as high initial engagement. Always clarify the definition of "Active" before building your reports.
Another tactic is filtering out failed transactions or error logs. In a SaaS product, errors are inevitable. However, if the seller has set their Mixpanel dashboard to only track "Success" events, you will not see the friction. You need to see the full event stream, including failed logins, 404s, and payment failures. High friction in the user interface is a direct driver of churn. If you cannot see the errors, you cannot assess the need for technical remediation. This could be a capex requirement that is not included in the seller's financial projections.
Finally, watch for "double-counting" of users. If the product has multi-tenant architecture, ensure that a single company account with 10 employees is not being counted as 10 unique "users" for retention purposes if the business model is per-seat. Or conversely, if the model is per-company but the analytics track individual behavior, make sure you are aggregating correctly. A discrepancy in how "User" is defined in the analytics platform versus how "Customer" is defined in the billing statement can throw off your LTV calculations by 50% or more. Verify the schema definitions with the technical team.
Before you send your wire transfer or sign the final Purchase Agreement, you must have completed this list. This is not a "nice to have" list; these are binary pass/fail criteria for due diligence on user engagement. If you cannot verify these points, you are buying a business blind, and the risk of price protection requests or immediate post-close revenue drops is high. Systematize your review process to ensure you are not relying on the seller’s narrative.
Your analysis of the engagement data is not just for your own risk assessment; it is your biggest negotiation weapon. If you uncover that the seller's retention rates are lower than industry standards, you have a factual basis to reduce your offer. Do not just say "I think it will be hard to grow." Say "The cohort data shows a 40% drop-off in retention by Month 2, which suggests a high churn rate that will make it difficult to sustain EBITDA if you pause paid acquisition. Given this, I am adjusting my valuation multiple to 2.5x rather than 3.5x to account for the growth risk."
This approach changes the dynamic. It stops the conversation from being a debate of opinions ("I think my product is amazing") and moves it to a debate of facts ("The data shows X"). Most sellers do not want to be challenged on the technical details of their own Mixpanel dashboards. If you can speak the language of their data, you gain respect and authority. You signal that you are a sophisticated buyer who is not being fooled by surface-level metrics. This confidence often leads to sellers conceding on price or offering more favorable terms, such as higher earn-out structures tied to retention targets.
Moreover, specific data gaps can be used to request escrow holds. If you find that a significant portion of revenue comes from users who have not been active in 60 days (but are still paying), you can argue for a portion of the sale price to be held in escrow for 6-12 months. You would agree to release those funds only if the retention of that specific cohort hits a certain threshold. This protects your downside while showing the seller that you are fair and grounded in the business reality. It turns the data from a source of anxiety into a structured risk management tool.
Buying the business is only the beginning. Your analysis of the engagement data in the first 30 days should directly inform your "100-Day Plan." Because you have already identified the "North Star" metric and the friction points, you can hit the ground running. Instead of spending the first quarter learning the product, you spend it fixing the specific leakage points you identified in the analytics.
For example, if your data showed high churn after the first week, your immediate priority might be improving the onboarding flow. If you found that mobile users have low retention, you might prioritize a mobile update. You are not guessing; you are executing against data-proven hypotheses. This speed to value is what separates successful acquirers from enthusiasts. You are buying a machine, and you are looking at the maintenance logs. You know which parts need oiling. A visit to Deal Alert AI can provide you with templates for these 100-day plans based on seller-provided analytics, saving you weeks of strategic thinking in the critical early phase.
Finally, remember that data is a living entity. The trends you see today may have been influenced by a specific client win or a bug that has since been fixed. Always take a snapshot of the analytics "health" on different days of the week to ensure you are not being swayed by anomalies. By combining rigorous verification with a clear strategy for growth, you transform a potentially risky purchase into a calculated opportunity. The sellers who respect data will build businesses that are resilient. The buyers who understand data will acquire businesses that are profitable. The goal is to fall into the latter camp, armed with the truth that only Mixpanel and Amplitude can provide.
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