In your first ninety days of product analytics, track a small set of things: how many people sign up, how many reach their first meaningful action (activation), how many come back (retention), which features they use, where they drop off in key flows, and what they say in feedback. Define events carefully and review them weekly.
- Start with questions, then decide which events to track to answer them.
- Activation and retention tell you more than raw sign-up or visit counts.
- A short, well-named event list is better than tracking every click.
- Pair numbers with watching sessions and talking to users to learn the why.
What is product analytics and how is it different from web analytics?
Web analytics mainly describes traffic: where visitors come from, which pages they view and how long they stay. Product analytics focuses on what people do inside a product after they arrive: signing up, creating a first project, placing a first order, using a feature, coming back. It follows individual users over time, not only page views.
This lets you answer questions such as which actions predict that a user will stay, which features nobody touches, and where new users get stuck. These are the questions that guide product decisions, as opposed to marketing decisions about traffic.
Which questions should you start with?
Write down five or six questions before choosing any tool settings. For example: do new users reach the first valuable action? How long does it take? Which channel brings users who stay? Which features are used weekly? Where do people abandon sign-up or checkout? What do returning users do that churned users did not?
Each question implies the data you must collect. If you cannot say what decision a metric would change, leave it out for now. Teams that track everything often struggle to find anything useful, because the signal is buried in clutter.
- Do new users reach their first valuable action, and how fast?
- Which acquisition channels bring users who return?
- Which features are used regularly and which are ignored?
- Where do users abandon key flows?
- What separates retained users from those who leave?
What are the essential metrics for the first ninety days?
Begin with acquisition, activation, retention and a revenue or value measure. Acquisition counts new sign-ups or installs by source. Activation is the share of those who complete the action that shows they have understood the value. Retention asks how many return after a day, a week or a month. For revenue, track conversion to paid or repeat orders.
Define each in plain words with exact rules, for example that activation means adding the first product and sending the first invoice within seven days. Write the definitions down, since arguments about what a metric means can eat more time than the analysis itself.
How should you plan events and naming?
Events are the recorded actions, such as signed up, viewed item, added to cart, paid. Create a tracking plan in a spreadsheet: event name, when it fires, properties attached such as plan or source, and who owns it. Use a consistent naming style, like verb plus object, to avoid a mess of near-duplicates.
Keep personal data minimal and follow current privacy rules. Identify users by an internal ID rather than storing unnecessary details in analytics. Test events in a staging environment before release, because missing or doubled events produce wrong numbers that look believable.
- One tracking plan that everyone can read
- Consistent event names and properties
- Test events before release
- Limit personal data sent to analytics tools
What should the ninety-day rhythm look like?
In the first month, set up tracking and validate it. Spend time making sure the numbers match reality, such as comparing sign-ups in analytics with your database. In the second month, build the main reports: a funnel for sign-up to activation, retention by sign-up week, and feature usage. Look for the big gaps first.
In the third month, run experiments on what you found, such as simplifying onboarding or sending a nudge on WhatsApp or email to users who stalled, and measure the effect. Hold a weekly short review with product, support and sales so insights spread and decisions follow.
What mistakes should you avoid?
The first is vanity metrics: total sign-ups or page views that rise without telling you whether anyone benefits. The second is averages that hide different behaviours, so segment by source, plan or user type. The third is drawing strong conclusions from tiny numbers; with few users, treat patterns as hints and talk to the people.
Numbers say what happened, not why. Combine them with session recordings, interviews and support tickets. A Plus Solution helps teams set up product analytics and dashboards, but the habit that pays most is a weekly review that ends with one product change to try.
Frequently asked questions
Which tools can we use?
Several product analytics tools exist, as well as options built on your own database and dashboards. Pick one that suits your team size, privacy needs and skills, and check current terms.
How many events should we track?
Start with a few dozen at most, focused on key flows. You can add more when a new question requires it.
Does this apply to B2B and internal tools?
Yes. Activation, feature use and retention matter for any product, including internal systems whose adoption you want to understand.
How is it different from a BI dashboard?
BI dashboards usually report business totals from databases, while product analytics focuses on user behaviour sequences. They complement each other.
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