Churn prediction model for account representatives at a large paint company
A large paint company was facing difficulty knowing about their high-value accounts who were likely to churn. It can cost five times more to attract a new customer than it does to retain an existing one. Without any visibility into the churn probability of these accounts, the account executives had some difficulties in prioritizing and deciding the next best actions for their at-risk accounts. The solution would require implementing SAP to pull and integrate account data.
Neal Analytics has built a modern cloud architecture that enabled data sharing across their parent company units.
We helped them unlock and pull SAP data to integrate with other data sources and process data using Azure Databricks.
Neal Analytics then built a churn model using the data to help them predict the likelihood of accounts leaving or discontinuing purchases. This churn prediction model was helpful for account representatives to know about their customers.
The modern architecture developed by Neal Analytics allowed data sharing between business units that enabled cross-sell opportunities and enriched customer experience. The unlocked SAP data and churn prediction models used in an application helped display the churn likelihood score on the Salesforce account page. Now, account representatives can easily prioritize accounts at risk of churning and decide the next best action accordingly.