Mobile analytics

In 2021, the worldwide market for mobile analytics was $4.72b. By 2031, this is projected to be $27.60b (Allied Market Research).

Mobile analytics can take place on mobile web applications, websites, and native mobile applications. The way users interact with content via mobile is often fundamentally different from laptop or desktop usage. For many organizations, deeply understanding how their users interact via mobile allows them to significantly improve the user experience.

A warehouse-first approach to mobile analytics

Traditionally, mobile analytics has often been siloed from web analytics, but unifying these data sources is now essential as users move seamlessly between devices.

Set-ups like Google’s Firebase, which separates mobile app data from Google Analytics, have traditionally created a nightmarish job for data teams to get two different data sets to tell one story.

The ideal warehouse-first approach to mobile analytics involves tracking activity across web and mobile, keeping this information in a single table, and then modeling this data into smaller tables for specific purposes – see the diagram below. This allows you full flexibility to describe a user’s journey.

The challenges with mobile analytics

Stitching users 

Creating user profiles is a significant challenge for data teams. Users may have different privacy settings on different devices, or can be logged into different accounts. Furthermore, getting data of sufficient quality to accurately create these profiles is difficult, partly due to the need for effective internal workflows and partly due to teams simply having the wrong tools.

Effective data analysis and customization

Another challenge with mobile analytics is that different mobile apps have distinct needs. For example, look at the differences between a dating app – with events like “swipe”, “match”, and a health tracking app, where you might track “miles run” or “minutes active”. This requires custom event grammar to suit your organization.

Just tracking unique events like this is a challenge with many tools, but there is also the issue of not having entities. Entities are like objects in our tracking – so the “liker” and “person liked” in our dating app example.

As the vast majority of tools lack these entities, analysts struggle to filter data to answer simple questions, such as “how many of the “liked” people are based in New York?”. It’s like trying to build a language with lots of verbs and no nouns!

What is a Data Product Accelerator for mobile analytics?

A data product is an actionable data set that you create through advanced user tracking.

A Data Product Accelerator, or DPA, is a guided recipe that helps data teams to undertake advanced analytics and set up a customizable dashboard by following a simple, step-by-step process.

Ultimately, you can build a deeper understanding of customer behavior on your mobile apps, so you can use data to influence business decisions. 

Try a Data Product Accelerator (DPA) for mobile analytics

Snowplow for advanced mobile analytics

Snowplow offers highly flexible data collection tools across a variety of mobile platforms (iOS, Android, React Native, Unity) that collect data in a consistent format. 

The trackers are fully configurable, which allows you to collect the custom events you need to fully describe the unique way your product works (we don’t believe in cramming square pegs into round holes).

Snowplow also pioneered the concept of entities for behavioral data, meaning analysts can easily slice and dice the data and answer questions more easily. The data is also highly reliable, so the final tables can used to create an accurate single-customer view, across web and mobile. In fact, all web and mobile information is initially stored in one atomic events table with the device type added as a context, making combining and filtering users by device as simple as a line of SQL.

Ultimately, Snowplow data allows organizations to drive revenue out of in-app purchases and subscriptions, as well as gain a better understanding of customer lifetime value and user retention.

To test this for yourself, try Snowplow for free