Wouldn't it be great if you had better insight into your customers?
Their behaviour, their habits... Knowing where they'll click and even when they'll quit using an application for good... and why? Wouldn't it be a whole lot easier if you knew what features or elements your customers like... and what drove others away from your product?
Predikat allows you to drill down and cultivate this kind of insight, providing informed knowledge about predictable client actions.
The processes running in the background are complex, but still provide simple, easy-to-understand answers. Utilizing a Customer Churn Machine Learning module, Predikat offers in-depth analysis, helping predict why a customer will eventually stop using your program and when. This is possible through scrutiny of the user's former actions, such as what tasks they elected to perform with your software and how they manipulated it.
How does Customer Churn Machine Learning work? It was programmed with these points at the forefront:
Keep it Simple: Gather big data from large groups of small actions over long periods of time. By doing so, you'll be returned with more accurate analysis, leading to more precise churn prediction.
Cast a Wide Net: Versatile at its core, the Customer Churn Machine Learning model can analyze a range of different software services. From SaaS applications to gaming and entertainment software, churn likelihood can be predicted by placing these minor, simple actions under a magnifying lens.
What kind of data can be gathered? Predikat’s Smarttarget categorizes users, letting you know who is most likely to be influenced positively by ads, marketing prompts and rewards... and those who are more likely to sign off from your product completely if they have to watch yet another ad. This allows you to finely tailor your marketing approaches to each subgroup of customers, maximizing effectiveness, while minimizing user churn.
Want to learn more about how PreSmarttargetdikat can help you maintain your users? We invite you to Connect with us today.