The value of a mobile application has been clear to businesses for many years, acting as both a brand asset and a way of reaching 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 advanced digital brands benefit from this, and increasingly, as we’ve seen customer servicing move away from call centers and towards self-serve content and chat communication, apps have also been seen as a great way of reducing costs while giving customers timely and relevant support.
Over the last couple of years, finally the role of the app is being leveraged by a wider range of brands as a way of maintaining personalized and relevant communication with customers. This is especially significant as websites suffer from the effects of a crumbling cookie – apps that require a sign-in as part of the functionality have a significantly more reliable identifier and, with the appropriate user consent, these apps enable personalized content based on previous activity and preferences.
So, how should brands that have traditionally relied on customer acquisition via a company website start the process of adopting an app-focused or cross-screen marketing strategy. The first step is to develop a robust data strategy.
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Understanding the challenges
As any mobile marketer would agree, the mobile app measurement landscape is vastly different to that of the web. Concepts such as mobile measurement partners (MMPs) and the SKAd network are unique to app measurement, and the value proposition is often different to web analytics.
And there are technical considerations around activation on mobile devises, which we can define as the infrastructure to reach a target audience with the right message at the right time. There are many activation scenarios for a brand to enable, from website browsing on a mobile device to push notifications, and deep linking through an email.
With these core activation techniques comes the question of how best they are achieved from a technical point of view. Using a third-party push notification or deep-link provider can speed up implementation of solutions but introduces new services and dependencies for the user’s device. With responsiveness and speed being such a key benefit of an app-focused approach, there is a complex balancing act between ease of development and user experience.
Finally, IT infrastructure for a mobile app differs from that of a website. Implementing a best-in-class data strategy for apps requires a fundamental appreciation of the way apps are built, how data is collected and sent from a mobile device, and how to test and analyze data quality once the infrastructure is built.
Building a robust app strategy
Every app is unique, so there is no one-size-fits-all solution when considering app strategies. However, to form a strong foundation, these four recommended steps will help the process.
1. Define the use cases for your data
By putting yourself in the shoes of prospective customers, you can identify the most appropriate methods to orchestrate and measure customer interactions with your app. Defining this journey and prioritizing by commercial value avoids over-investment of time and resources in complex use cases which achieve fewer valuable results.
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 in the app’s codebase to facilitate the journey and measure user experience. If data from the same interaction is required for multiple data endpoints, consider platforms and services that aid this scale and decrease the requirement from a development perspective. If your audience targeting use case benefits from the use of 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, put together the specific interactions to measure use cases, and the criteria which determines activation of them. Bringing together all these requirements, mapping against behavioral triggers, and understanding the similarities and differences between data endpoints enables data teams to build a comprehensive tracking plan. Once this is briefed into development teams, a focus on UAT testing and quality assurance maximizes the chances of success once changes are pushed to production. Finally, once tracking has been launched, comprehensive analysis on the data helps to iron out final issues.
4. Test and scale
With accurate and reliable data foundations, mobile-specialist data analysts can help to drive continued value through the insights this provides. Customer journey analysis helps teams to produce data driven hypotheses. These can be turned into a prioritised set of activation tests. Test conclusions can then be used to scale results and generate new hypotheses.
What next?
With a solid plan in place for a mobile app data strategy, 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 becoming ever more ambitious, this journey can take months and even years to execute to completion. For businesses on this journey, consider the following steps when implementing the next steps:
1. Planning and leadership
Prioritising data strategy within a product roadmap, and clearly articulating the benefits of the mobile app, will set any programme up for success. Data plays a key role in the measurement and improvement of customer experience, which ultimately dictates the success of an app program.
2. Invest in the right expertise
Digital transformation can be a challenging process, especially if it requires new skill sets and expertise. Analysts and data architects well versed in web concepts will still need a comprehensive and well-structured training program to deliver on data strategy that caters to the needs of a mobile app.
3. Be pragmatic
Bear in mind that the ‘shiny new platform’ is not always the ‘right solution’. Before launching into a complex new developmental workstream to achieve a sophisticated use case, consider if there are quicker wins using an existing architecture.
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