Dear all,

We're excited to announce a couple of major enhancements to the Zia Churn Prediction feature: descriptive reasoning and the inclusion of data usage.

Before we discuss the improvements, here's a quick recap of what Zia Churn Prediction is all about in the first place.

Zia Churn Prediction is an essential tool that enables businesses to forecast customer and revenue churn. It assists organizations in identifying potential customer loss by examining variations in customer count and transaction patterns. The Churn Prediction widget on the record detail page displays the contact's churn score, which indicates its likelihood of churning. A higher score indicates a higher possibility of churn, and this information enables businesses to concentrate their sales efforts on customers with higher churn scores.

As mentioned, we've now improved our churn prediction feature in the following ways:

Instead of simply providing a churn score that indicates which customers might leave, Zia will now provide reasons behind the prediction. That is, if Zia predicts a high churn risk for a particular customer, it will now also reveal which factors have contributed to this conclusion.

The churn prediction widget now has a Learn More button. Upon clicking it, a pop-up will present detailed reasons behind the customer's likelihood of churning

The pop-up also displays the proportionality of each reason's contribution to the prediction score. For instance, if poor service quality, competitive offers from competitors, and billing issues are the main reasons a customer might churn—the churn-inducing factors—the pop-up will indicate how much each of these factors has contributed to the predicted score. This adds a layer of transparency and enables you to understand the why behind churn predictions.

Additionally, Zia also lists churn-preventing factors, which are factors that keep the customer engaged and avoid or delay possible churn, along with how much value each factor holds.

These reasons will be determined by considering all customer module touch points, including data points from related modules and VOC data.

Moving on to our second enhancement for Zia Churn: You can now direct Zia to analyze data from Google Analytics, and soon from other platforms like Mixpanel.

Zia can use this feature to observe customer engagement via events from third-party analytics platforms like Google Analytics. By analyzing metrics like login frequency and product usage, Zia can enhance the accuracy and timeliness of customer churn predictions.

This integration is managed within the CRM, where you can establish a connection between Zia and Google Analytics and specify which customer data Zia should monitor.

That's all for these enhancements. Please share your feedback in the comments below.

Thanks