Gone are the days of spreadsheet data analysis and “blanket” marketing, or so we hope. In are the days of more individualized and more timely marketing through predictive analytics.
US Cellular found out what users of their mobile app liked and didn’t like to see. Naturally, the company gave the users more of the good stuff and less of the bad. Happier users means better retention and, ultimately, more $ for the company.
A professional photo market service provider figured out which customers were likely to stop using the mobile app. They had a clear “stopped using the app” definition, or churn – 30 days of no app activity. This made it possible to look for various app usage patterns that suggested possible churn. High churn risk correlated to a high number of certain types of app usage events – in other words, if I did “this,” “that,” and “the other,” I am highly likely to churn. But if I only did “this” and “that” then I am medium risk, and just “this” alone is a low risk. The service provider then decided to target high and medium risk customers by sending them information about photo competitions to their mobile phones. It worked – plenty of customers re-engaged with the mobile app and, eventually, continued to spend $.
Data these days can reveal wants and un-wants of individual customers. That means I get my very own marketing material tailored to me instead of the generic stuff, or I am stimulated to re-engage sooner rather than later. The likelihood of me responding is higher, and that’s what you want as a marketer.
Mobile phones are everywhere, including my pocket, and, therefore, it’s an important channel. Mobile app usage data can tell a lot, and a smart marketing team can make good $ from it. The challenge is to understand the data.
How Do Brands Use Predictive Analytics for Mobile?