How to turn signals into strategy with mobile data and AI

In this edition of mobile done right, a series of articles where I explore how to unlock more value from mobile, I’m exploring how to better use mobile customer data to feed your strategy and drive revenue. 

Mobile apps generate a wealth of behavioural, contextual, and transactional information - all of which can (and should) be used to inform your customer strategy. When you add AI into the mix, this data can become the foundation on which propensity models are built, behaviours influenced, and segmentation and messages can be personalised. 

So how do you identify intent with mobile data?

The first, and most essential step, is to implement the right data structure. This means instrumenting your mobile app properly, respecting privacy, and linking your mobile insights with broader systems. Mobile customer data should be a live contributor to your overall strategy, rather than an isolated bystander. 

To lay this groundwork, you need to assess where you are, what’s missing, and what to do next when it comes to your product data. Below is a step-by-step guide to help you understand where to start. It’s by no means an exhaustive list, but I’d be happy to take you through this in more detail over a coffee.

1. Audit what you already capture

Look at your list of current mobile events, attributes and user properties from your analytics and tagging systems. Compare that list to a simple inventory: screens, user journey steps, conversion points, error states and key micro-interactions (e.g. add to cart, wishlist, search, view product, start checkout, location check-in). Mark each event with: how often it fires, who owns it, and where the data lands.

2. Map events to business outcomes

Choose 3–5 core outcomes you care about (e.g. trial to paid account conversion, repeat purchase, retention at 30 days). For each outcome, list the upstream behaviours that likely signal intent (e.g. frequent price checks, wishlist additions, repeated searches). Tag the events in your inventory that map to those behaviours to make it clear which signals you already have and which you still need.

3. Check data quality and consistency

Look for gaps or spikes in event volumes that suggest broken or duplicated instrumentation. Verify event definitions are consistent across platforms and SDK versions (names, schemas, attribute types). Don’t forget to spot-check timestamps, user identifiers and session stitching to ensure signals can be connected to users over time. Inconsistent naming is a common cause of blind spots on dashboards - so stamping this out is crucial. 

4. Do a privacy and consent review

Confirm the events you collect respect consent flows and regulations and ensure personally identifiable information is not sent where it shouldn’t be. Decide which signals can be persisted for modelling and which must be anonymised or aggregated. Above all else, legal compliance and trust come before model accuracy.

5. Perform a gap analysis against your dashboards

Open each dashboard and ask: “Which business question does this answer?” and “Which questions are missing?” For missing questions, trace back which events would be required to answer them, prioritising fixes by commercial impact and ease of implementation.

Once this foundation is in place, you can begin to detect the early indicators that matter and micro-behaviours that signal interest or intent. These are often the moments that define whether a customer will go on to engage, make a purchase, or drop-off. Getting control of your mobile customer data allows you to more effectively influence these key moments.

When data meets AI 

Leading brands and businesses are already utilising AI to supercharge their data funnels and better understand customer signals. 

Take loyalty for example - with AI, we can swiftly identify which users are likely to lapse and take action by delivering a personalised re-engagement journey through the app, via push, or even in-store. The commercial upside of being able to leverage this kind of intelligence is significant.

Remember, this is about supercharging your data and insights capabilities. You still need mobile specialists in your team that can get those all important first steps right in creating the right data structure, and you need a team that knows what to look for, and how to use findings. 

Is mobile an underused data source for you?

Mobile is the best data source most brands have, the question is – are you using it intelligently? 

At Apadmi, we help brands understand and leverage their mobile customer data whilst implementing AI in order to detect intent, predict churn, personalise experiences, and optimise performance in real time. 

If you’d like to learn more about turning signals into strategy, reach out for a coffee and a chat - we’ll be happy to hear more about your goals and ambitions.