Snowplow for mobile analytics
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In previous chapters, we have discussed the opportunities around effectively engaging with customers and users on mobile devices. At the same time, we have also highlighted some of the challenges companies face in building out the necessary analytics, and leveraging that to drive value. In this chapter, we will introduce Snowplow’s approach to mobile analytics and how it addresses these challenges, empowering you to take full advantage of the huge opportunity that this shift in user activity towards mobile represents.
Snowplow is a behavioral data platform. With Snowplow, you can develop a deep understanding of how your customers interact with your products by collecting complete, accurate and well-structured event data across all platforms and channels. Snowplow provides you with a unified, high-quality raw data set that can be deployed and modeled in multiple ways to serve endless use-cases.
Let’s take a look at some of the key reasons why market-leading mobile first companies like Strava, N26 and Peak use Snowplow to better understand how your customers and users interact with your mobile applications, and more broadly, their entire customer journey.
Data that accurately reflects user behaviour
One thing that makes Snowplow technology so powerful is that it is entirely event based. More specifically, Snowplow allows you to define your own custom events (and entities) so that you collect data that accurately reflects the user experience, rather than following some rigid predefined schema dictated by your tools. This is especially important on mobile, as there is much more variation in the way mobile applications are designed and built.
For example, the analysis of every traditional website is concerned with page views and sessions. On mobile, these concepts aren’t as clear cut. In some apps, screen views are a good description of user engagement, in others they are not: imagine scrolling through your newsfeed on Instagram, or texting your friends and family on Signal. To learn more about Snowplow’s unique approach to structuring data, check out our post on rethinking the structure of event data.
Snowplow’s own schema technology also makes it easy for you to iterate your tracking design over time. This is crucial, as it allows you to keep your data collection up to date with your mobile applications as they develop and improve, and also enables your teams to evolve their tracking as they learn more about the users and their behavior.
Full ownership and control over how data is modeled
Flexibility is also important when it comes to modeling and visualising the data. However, preparing event data for reports and insights is hard. Event data is high volume in its nature, so data models need to be performant to keep query costs in check. Event data, and more specifically, behavioral data needs to be aggregated at different levels for different use cases.
Snowplow aims to provide you with full flexibility while at the same time supporting you with the technical challenges modeling event data poses. Therefore, Snowplow has developed incremental, modular data models that take care of the heavy lifting for you, but allow you to easily extend our standard aggregations with your custom business logic. These models automatically determine what events need to be processed with every run of the model, how user activity spanning multiple model runs should be reconciled, and more. You can then easily apply your logic to those pre-selected, deduplicated and reconciled events. And all of this can be configured and monitored through a convenient user interface.
A single source of truth
Snowplow captures data from all platforms and channels into a single, unified table in your data warehouse, data lake or event stream. This not only includes all events from within your mobile applications (tracked client or server side), but also events capturing the user’s wider journey: engagement with your website(s), email marketing, display advertising, install and attribution data from your mobile marketing solutions. This essentially provides you with a 360° view of your customers, and ensures you are looking at their activity holistically and delivering a consistent and engaging experience across all touchpoints.
The ability to model and analyze data from your mobile marketing solutions (such as Branch, Appsflyer or Adjust) alongside your in-app tracking is particularly important in today’s competitive landscape. Because you have full control over how data is modeled, you can easily adapt your approach as the platforms and advertisers make changes to what data is available; for example, switching from attribution at the user level to attribution for cohorts or micro batches as Apple and Google roll out more and more privacy measures.
Furthermore, this unified view of all user activity enables you to power all your data use cases off a single source of truth, whether its marketing attribution, product analytics or churn prevention. No more data silos. And since all teams are leveraging the same data, the business can align on strategy and direction and become truly data informed in their decision making.
Leverage the latest tracking technologies
Snowplow is designed from the ground up to help modern companies gain a deep understanding of their users on mobile. Our iOS, Android and React Native SDKs (Flutter is coming soon) are easy to implement, with consistent APIs across the two platforms. They automatically track key events and entities (screen views, timing events, user and session context, etc.), as well as enabling you to easily track custom events and entities to ensure you have the most granular understanding of how your users behave in your applications. Furthermore, as Snowplow is a first-party tracking solution, there is no reliance on recently deprecated user identifiers such as the IDFA, and our mobile engineers continually work on ensuring you can reliably identify your users on mobile.
A key challenge with deploying tracking changes on mobile is the requirement for an app release and subsequent user app upgrades to propagate the changes. To alleviate this challenge, Snowplow has introduced the possibility of remote configuration, enabling you to configure your tracking implementation from outside the applications. More information on this feature can be found in the release post.
In this chapter, we have discussed some of the key advantages of using Snowplow for mobile analytics. In the next chapter, we will review how the number one app for runners and cyclists, Strava, uses Snowplow to empower analysts and product teams to drive a culture of continuous product optimization without running into analytics ‘blind spots’.