Measure where users drop off instead of guessing what they need.
With a digital product you can build features endlessly. The harder question is which change actually makes a difference. That is why at nummi we combine product development with analytics, cohort analyses, experiments and business data. Sometimes the data confirms an idea. Sometimes it shows that we were about to work on the wrong problem.
The case in brief
Only 54% got past signup/login
Retention data led to guest accounts
Onboarding with control/variant and its own funnel
Backend, iOS, Android, analytics and monetisation
Where was the challenge?
Technically, nummi is an extensive product: multiplayer games, friends, chat, rankings, bots, puzzles, notifications, ads and subscriptions. But building more functionality does not automatically make the product better. To improve in a targeted way, product behaviour, technical events and business results had to be connected.
See the approachA feature can feel logical and still deliver little
Leaderboards, badges, streaks, bots, onboarding, ads and subscriptions can all sound interesting. Without data, though, you do not know which investment solves the biggest problem.
The problem can be somewhere other than you think
Our retention analysis showed, for example, that users who actually started were already playing quite actively. The biggest drop-off happened before that: only 54% of new users got past signup and login.
Product, technology and business affect each other
A change in onboarding can affect retention. Acquisition affects the quality of new users. Ad revenue differs per platform and subscriptions call for different product choices again. You cannot make those decisions in isolation.
How did we approach it?
- 01
First measure where it really goes wrong
We analysed not just totals, but the whole user journey: first open, signup, first actions, engagement, D1 and D30 retention, acquisition source, social interaction, platform and user value. That let us tell bottlenecks apart from symptoms.
- 02
From analysis to a concrete product choice
The retention data showed that the registration wall lost many users, while players showed healthy playing behaviour after signup. Instead of building more gameplay features first, we added guest accounts so new players can start right away.
- 03
Roll out new ideas measurably
New onboarding is not simply switched on for everyone. The apps support a control and treatment variant via remote configuration and log separate milestones throughout the onboarding. That way we can assess afterwards where behaviour actually changes.
- 04
Link product data to business data
Besides gameplay and retention we look at subscriptions, ad revenue, acquisition and attribution. This lets us assess, for example per platform, which revenue model and which product investment make the most sense.
What did we build?
Data-driven product development takes more than an analytics script. Measurability has to run through the entire technical architecture, without the product itself becoming dependent on one dashboard or tool.
A complete production backend
The Rails backend manages accounts, games, moves, friends, rankings, ratings, chat, bots, puzzles, subscriptions, notifications and a growing number of individual user statistics. Background work and recurring processes run via job queues.
Product analytics in both mobile apps
Firebase Analytics is the primary product analytics layer. The apps record events around, among other things, signup, gameplay, invitations, puzzles, ads, purchases, streaks and onboarding. Relevant user properties are also tracked so behaviour can be compared between groups.
Experiments and acquisition made measurable
Onboarding has its own measurable funnel with milestones from exposure and the first practice action to guest account creation, invitations, practice games and completion. Android also uses AppsFlyer for specific acquisition and purchase attribution.
More than one dashboard
We only combine data that helps make a product decision.
Retention and activation
We look at where users first drop off, when they come back and which behaviour is associated with longer use.
Engagement and social effects
The analysis showed, for example, that 25.3% of users started games with friends and that active chatters sent almost thirty messages on average. Social interaction therefore turned out to be a more interesting retention signal than simply more solo functionality.
Revenue and acquisition
Ads, subscriptions, attribution and user value are compared with product behaviour. That way you can tell whether growth comes from more users, better retention, better monetisation or merely from more expensive paid traffic.
What changed?
Data pointed us to signup, not to yet another feature
In the analysis, only 54% of new users got past signup and login, and about 44 to 45% came back on day 1. At the same time, users who did start already took many actions on average on their first day. That made it clear that the first product threshold mattered more than extra gameplay behind it.
Guest accounts came straight from that analysis
New players can now start right away without first creating a full account. After the introduction we already saw fewer drop-offs. This decision started with behaviour we could point to in the data, not with a brainstorm.
We can actually compare the next onboarding
A more extensive onboarding is now in the product as an experiment. Control and variant stay consistent per installation and every important step has its own analytics. So soon we will not only be able to say that a flow 'feels better', but see whether more users actually become activated.
Data changes the conversation
We learned the most from the assumptions that turned out right or wrong. That makes product development much more concrete.
View casesThe registration wall became visible
Only 54% got past signup/login. Of the people who did start, 68% already reached the first important gameplay threshold of 30 points. So the problem sat much earlier in the funnel than we could have seen from feature usage alone.
Not every new user is equally valuable
In the retention analysis, D1 retention of CPC users was around 21%, compared with about 39% for organic users. That was an important signal not to simply buy more paid traffic while the onboarding itself still had friction.
Platform data shapes the business model
In the same analysis, Android and iOS had comparable engagement, but a clearly different measured 120-day user value. That gave a concrete reason to approach monetisation differently per platform: more ad optimisation where that works and more attention to subscriptions where ad revenue lags behind.
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- Result
- Production-ready software with a measurable product flow
- Duration
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- First we determine which decisions the software and data actually need to support