Mobile app personalization uses machine learning algorithms to boost user engagement -- but only if you use it...
Poorly implemented mobile app personalization can result in too many pop-up notifications or an onslaught of irrelevant content that can drive users away from a mobile app. You must carefully plan and develop personalization based on the best available data.
Make a plan
First, you must understand what your mobile app users want. To do this, perform the analytics necessary to understand their typical behaviors. If they use a previous version of the app, determine how they navigate the app's interface and use its features.
Take advantage of any demographic data you've collected or any other available sources of data, such as social media platforms.
Then, develop a solid plan that takes into account your data and the long-term goals of personalization. Determine which strategies you'll use to deliver personalization and how to implement those strategies.
Find a platform
Mobile app personalization requires a platform that will support the app, which you can either build from scratch or purchase. This platform will maintain the back-end infrastructure that will collect and analyze the data and then make recommendations based on the data. The platform should help manage and automate much of the personalization process and offer services such as user segmentation, automated recommendations, behavioral targeting and A/B testing.
If you build the platform from scratch, you'll need to develop the machine learning algorithms that analyze the data and predict outcomes, which will require specialized skills from data scientists and other experts. These skills are costly and difficult to find, so some organizations turn to personalization platforms such as Dynamic Yield, Evergage, Qubit or Yusp. Ensure that the personalization platform you choose integrates with your mobile app development platform.
You can use third-party tools for other operations, such as the Appsee platform for mobile app analytics or Localytics Profiles to collect customer data.
Test and optimize
Next, build or update the app and incorporate the personalization platform. During this process, you'll need to write code and integrate the app with existing systems.
Review and test the app and use the personalization features to their fullest. Use tools such as prototyping or A/B testing platforms whenever possible. A/B testing lets you try out different variants of the app to determine the most effective way to deliver features.
After you release the app, monitor its operations and user behavior and then analyze the collected data. Incorporate user feedback into this process whenever possible.
Optimize and update both the app and personalization platform based on your analysis. Run the app through a full quality assurance cycle and release the updated version.
Part one of this two-part series covered the benefits and use cases of mobile app personalization.
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