Product analytics services
See where users drop off.
We offer product analytics services for apps, SaaS products and online services. We plan app event tracking with your developers, then show where users start, come back or give up, so product decisions rest on what people do rather than on the loudest opinion in the room.
What our product analytics services include
Product questions
We agree which user action matters most to you and what counts as “started using” and “stayed”. Each report gets the question it answers.
Events and tracking
We list the events, their details and user IDs, and who implements them. We check consent rules and that app and server events describe the same thing.
Funnels, cohorts and retention
We analyse complete paths and the user groups that matter, keeping sign-up, first use and repeat use apart, so every result leads to a decision.
Experiments
For each product change we agree what should improve, why, and how long to wait. Before calling it a success we check data quality and other explanations.
What the product team gets
- A product metric dictionary and a tracking specification for developers.
- Agreed funnel, cohort and retention reports with notes on data quality.
- A ranked list of product questions and a simple way to review experiments.
SaaS product analytics: the metrics worth tracking
Activation
The share of new users who reach the first valuable action, such as a finished project or a first invoice sent.
Retention by cohort
How many users from each sign-up week are still active after one, four and twelve weeks.
Feature adoption
Who uses a feature, how often, and whether those users stay longer.
Conversion to paid
Where trial users drop off before paying, and what paying users did differently.
App event tracking starts with a plan
A tracking plan is a short document developers can implement and analysts can trust. It fixes:
- a short list of events tied to product questions, not every click;
- consistent names and properties across web, iOS and Android;
- user IDs that link anonymous visits, sign-up and paid accounts;
- which events are sent from the server, where accuracy matters most.
What this can look like
Plenty of people sign up, but few reach their first finished project. We find the step where they leave and compare user groups to see what is worth changing in the product.
Who product analytics is for
A good fit
- SaaS products, apps and online services with enough users to see patterns.
- Product teams that argue about features and want behaviour data instead of opinions.
- Teams preparing to change onboarding or pricing that want a baseline first.
Not the right fit
- Marketing channels and campaign return: that is marketing analytics.
- A product before launch with no users: we can plan the tracking, but analysis has to wait for real usage.
What affects the cost of product analytics services
We quote cost and timing in EUR after a first look at your data. It depends on:
Platforms
One web app is simpler than web plus mobile apps that must share events and IDs.
Existing tracking
Clean events can be analysed straight away; inconsistent ones need a new plan first.
Developer time
Who implements the events and how quickly releases go out.
Depth of analysis
A baseline of key metrics or ongoing analysis of experiments.
We’ll quote cost and timing in EUR after a short brief.Discuss product analytics
Common product analytics mistakes
- Tracking everything, then finding nothing useful in thousands of events.
- Renaming events in a release without telling anyone, which breaks every report.
- No shared user ID between web and app, so one person looks like two.
- Calling an experiment a success after a few days of data.
How we go from events to product decisions
Each stage ends with something you can check before we continue.
Brief
We agree the product questions, the key user action and which tools and data you have today.
Data review
We review existing tracking, write the event plan and hand it to your developers or to Maxarium, a separate development company.
Build and check
Once events are verified, we build funnels, cohorts and retention reports and check them against raw data.
Handover and review
We review the findings with the team, pick the first product change to test and agree how to measure it.
What we need from you
Access to your product analytics or event data, a short description of the product and its users, the questions the team argues about and contact with a developer who can add or fix tracking.
One person on your side should own the product decisions the data is meant to support, so every tracked event answers a question someone will act on.
Product analytics: common questions
How many users do we need before analysis makes sense?
Funnels and retention start showing patterns with a few hundred active users a month. With fewer, we combine data with user interviews and avoid drawing conclusions from small differences.
Is this different from marketing analytics?
Yes. Marketing analytics looks at where users come from and which channels pay off. Product analytics looks at what people do inside the product. The two connect through shared IDs and definitions.
Can you work before we have tracking?
Yes. We write the measurement plan and the list of events for developers first. Analysis starts once the events are verified or existing data can answer the questions.
Which product analytics tools do you work with?
Common ones such as Google Analytics, Amplitude, Mixpanel or PostHog, as well as data straight from your database. We use what you already have unless it can’t answer the questions.
Read before you start
Short practical guides on the same subject.
- Since 2006
- Written scope before work starts
- One contact person
- Reply within 24 hours
Where do users give up?
Describe the product, the main user task and what you track today. We’ll reply with questions and a suggested first step.
The first conversation is free, with no obligation.