Digital analytics for mobile
Some of the challenges with mobile analytics
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.
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.
If you want to see Snowplow BDP in action for yourself, you can now try it for free; set up takes around 30 minutes.