- The solution is provided based on the customer’s data collected from across the company and social media.
- The churn prediction capability helps providers gain additional value and helps them be much more proactive and action-oriented.
Salesforce introduced a new AI-powered feature with a vision to help cable and telecom companies minimize customer cancellations or churn data by predicting their level of unhappiness with the service.
The tool is incorporated into Tableau Customer Relationship Management (CRM) for Communications, an industry-specific application that Salesforce introduced in 2021. The tool extracts a customer’s data collected from across the company and even social media to give a unified view of the individual.
This includes data about how an individual uses the service, among other information. The tool then generates a churn score; this score can easily predict how likely the customer will leave in the next three months.
“This churn prediction capability provides significant additional value and helps providers to be much more proactive and action-oriented,” said Dan Ford, Salesforce General Manager for communications cloud, in an interview with IoT World Today’s sister publication AI Business.
“It has the potential to dramatically drive ROI improvements,” he added.
Also, the new AI-powered feature can help legacy cable and telecom companies keep hold of younger customers. A recent Salesforce study reported that 66% of Gen Zers and millennials would shift to a tech company to fulfill their communications services needs.
For example, when a customer calls a mobile service provider and a customer service agent receives it. The agent’s screen will display the following information about the customer – his/her profile, the reason for the call, and other information – along with the churn prediction.
The tool is also capable of incorporating the customer’s social media content. So, if the user may have complained about the service on any social media platform in recent times, his/her dissatisfaction is also accounted for by the churn-prediction algorithm.
With the help of the tool, the agent can simply find the reasons why the customers may leave. Perhaps the user reported more service outages in recent times than in the past, reflecting dissatisfaction. This may also imply that the customer has not used the service for a while.
If the churn prediction turns out to be high – the responsible company will have to track what high means based on historical patterns – the agent is given some paths for action. In such cases, the customer can also get a promotional offer.
Ford expressed that all the gathered information about the customer will be unified and presented to the agent. With this, the agent will not have to toggle among several software programs to obtain the required information. Instead, the agent can use the time to listen to the customer’s needs or queries.