For years, businesses have realized the value of a mobile application, which not only serves as a brand asset, but also as a way to reach a larger audience of potential customers.
Native content hosted on a user’s device can result in a much faster browsing experience, even compared to a mobile-optimized web page. More sophisticated digital brands are taking advantage of this and increasingly, as we see customer service move away from call centers and toward self-service content and chat communications, apps are also seen as a great way to reduce costs and provide customers with timely and relevant support.
In recent years, the role of the app has finally been embraced by a wider range of brands as a way to maintain personalized and relevant communications with customers. This is especially important as websites suffer from the effects of cookie erosion – apps that require a login as part of their functionality have a significantly more reliable identifier and, with the right user consent, these apps enable personalized content based on past activity and preferences.
So, how should brands that traditionally rely on customer acquisition through a company website begin the process of adopting an app-centric or cross-screen marketing strategy? The first step is to develop a robust data strategy.
Consulting Manager, fifty-five.
Understanding the challenges
As any mobile marketer would attest, the mobile app measurement landscape is vastly different than that of the web. Concepts like Mobile Measurement Partners (MMPs) and the SKAd Network are unique to app measurement, and the value proposition is often different than web analytics.
And there are technical considerations around mobile activation, which we can define as the infrastructure to reach an audience with the right message at the right time. There are many activation scenarios a brand can enable, from browsing a website on a mobile device to push notifications and deep linking via email.
With these core enablement techniques comes the question of how best to achieve them from a technical standpoint. Using a third-party push notification or deep link provider can speed up the deployment of solutions, but introduces new services and dependencies to the user’s device. Because responsiveness and speed are such a key benefit of an app-centric approach, there is a complex balancing act between ease of development and user experience.
Finally, the IT infrastructure for a mobile app is different from that of a website. Implementing a best-in-class data strategy for apps requires a fundamental understanding of how apps are built, how data is collected and transmitted from a mobile device, and how to test and analyze data quality after the infrastructure is built.
Building a robust app strategy
Every app is unique, so there’s no one-size-fits-all solution when considering app strategies. However, to build a strong foundation, these four recommended steps will help guide the process.
1. Define the use cases for your data
By putting yourself in the shoes of potential customers, you can identify the most appropriate methods to orchestrate and measure customer interactions with your app. By defining and prioritizing this journey based on commercial value, you avoid overinvesting time and resources in complex use cases that deliver less valuable outcomes.
2. Define your data architecture
With a clear understanding of the customer journey and commercial priorities, the next step is to understand what platforms and services are required within the app codebase to facilitate the journey and measure the user experience. If data from the same interaction is required for multiple data endpoints, consider platforms and services that support this scale and reduce the requirement from a development perspective. If your audience-centric use case benefits from using external databases outside of app interactions, consider how best to integrate this database and how frequent and fresh the data needs to be.
3. Focus on data collection best practices
With a well-defined architecture, you can put together the specific interactions to measure use cases and the criteria that determine their activation. By bringing all of these requirements together, mapping them against behavioral triggers, and understanding the similarities and differences between data endpoints, data teams can create a comprehensive tracking plan. Once this is presented to development teams, a focus on UAT testing and quality assurance maximizes the likelihood of success once changes are pushed to production. Finally, once tracking has been initiated, extensive analysis of the data helps to resolve any last-minute issues.
4. Testing and scaling
With accurate and reliable data foundations, mobile-savvy data analysts can help create lasting value through the insights it provides. Customer journey analysis helps teams produce data-driven hypotheses. These can be turned into a prioritized set of activation tests. Test conclusions can then be used to scale results and generate new hypotheses.
What now?
With a solid mobile app data strategy plan in place, the journey to scaling app capabilities can begin. In today’s industry, where highly specialized app development resources are in high demand and product roadmaps are increasingly ambitious, this journey can take months and even years to complete. For companies embarking on this journey, consider the following steps when implementing the next steps:
1. Planning and leadership
Prioritizing data strategy within a product roadmap and clearly articulating the benefits of the mobile app will make any program successful. Data plays a key role in measuring and improving the customer experience, which ultimately determines the success of an app program.
2. Invest in the right expertise
Digital transformation can be a challenging process, especially when it requires new skills and expertise. Analysts and data architects who are well-versed in web concepts still need a comprehensive and well-structured training program to deliver data strategy that fits the needs of a mobile app.
3. Be pragmatic
Keep in mind that the “shiny new platform” is not always the “right solution.” Before you start a complex new development workflow to achieve an advanced use case, consider whether there are faster wins with an existing architecture.
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