How Do Brands Use Predictive Analytics for Mobile?

Author: Maura Canavan / Source: Business 2 Community Predictive analytics, at the most basic level, is about having actionable data about

Author: Maura Canavan / Source: Business 2 Community

Predictive analytics, at the most basic level, is about having actionable data about what customers do in real-time via their phones. It’s actually somewhat amazing to consider how far we’ve come in this regard.

Consider a marketing team in 2000, in an era we’ll call “Pre-Predictive Analytics.” (Honestly, many marketing teams are still in this era, but we’ll gloss that over for now.) In order to get the best customer insights possible, a marketing team would (in collaboration with other departments) need to create dozens (hundreds?) of spreadsheets and then pore over them for insights and patterns.

Now flash forward 16-17 years. Marketing teams who have embraced mobile can use machine-driven technology to target specific customers via their phone, which is almost universally in their pocket. One of the golden rules of sales has always been that the more you know about a lead, the better your chance to convert.

If we were going to sum up predictive analytics in 3 steps, it would look something like this:

1. Know a lot about a lead;

2. Tailor content to that lead;

3. Adjust the end user experience for that lead, not universally.

It’s incredibly powerful for marketers.

Here’s an example: OneFootball, who works with us, wanted to identify users with the highest risk of churning (turning over) — and then proactively target them with individualized messages during the football (soccer to U.S. fans) offseason. They used a predictive analytics platform and reduced churn by 7.5% in the offseason. EyeEm, similarly, reactivated 67.3% of customers at high risk of churn with predictive analytics.