How Technology Shows Communications Service Providers Which Customers Are About To Leave

Communications service providers work hard to keep their customer base strong. They spend 15-20% of their revenue on acquisition and retention. But despite their best efforts, customers still leave. In fact, some providers are seeing churn rates of up to 75%. 

If service providers knew which customers were about to cancel, they could take proactive steps to lower their likelihood of leaving. New technology not only makes it possible to see a customer’s risk of stopping their service, but, more importantly, also shows why. That allows agents to take immediate action. Here’s how carriers can use these tools for reducing customer churn. 

Identify customers who are likely to cancel 

Providers understand the main reasons customers abandon service. It’s no surprise that customer dissatisfaction is high on the list. This often manifests in repeated calls to customer service to complain about things like outages or slow speeds.

But other factors that predict churn are more subtle. Customers with contracts may be upset by higher-than-expected bills, and leave at the end of their term. Meanwhile, customers who prepay might stop topping up as they try out competitors. 

 Here are more indicators of a customer’s satisfaction with your service:

• Tenure: Customers who have used the service for a long time are less likely to leave. 
• Lifetime value: Customers who have used the service longer and taken advantage of more offerings are more likely to stay. 
• Churned subscriptions: These suggest a customer who likes to try out new services but often cancels.
• Quality of interactions: Better service experiences increase customer satisfaction, which correlates to lower churn rates.
• Average call handling time: Long call times decrease customer satisfaction. 
• Last 30-day data usage: Heavy usage suggests the customer relies on it. 

Many communications service providers collect this information, but store it away in siloed systems. It’s not easy for anyone at the company to get a full view of the customer’s history. While a service agent sees ongoing service issues, the sales agent or field service technician does not. 

Technology solves this problem by pulling data in from different systems and aggregating it into a churn score. Higher scores indicate a higher likelihood to disconnect service. 

Agents accessing customer accounts can see the churn score. They get a 360-degree view of the customer, including their data history with the company. They also see a list of the top factors that led to that churn score, and next best actions to take based on those factors.  This helps agents personalize their engagement with customers and keep them happy — resulting in reducing customer churn. 

Take action to increase customer satisfaction 

Companies decide how to encourage customers to stay by determining the next best actions agents can take. Artificial intelligence (AI) analyzes the customer data to serve up the best offer or action that will increase their churn score. For example, if a customer with a high churn score calls to complain that their service is down, the agent can offer them a discount on their next month’s bill. Or if a customer’s churn score is high due to handset-related connectivity issues, the technology may recommend an attractive early renewal offer with a new handset incentive.  

However, if the churn score is low, the next best action may be an upsell opportunity, or to take no action at all. 

Providers can also integrate their loyalty program into the next best actions. Agents might recommend the customer join the program, or offer current members access to new benefits. 

Along with reacting to individual customers who reach out, companies can use churn scores to proactively send offers to groups of customers. The marketing department might segment customers based on churn scores and the factors that create them, and send special offers. They might offer bonus datapacks to customers who have called to complain about charges for exceeding their current limits. Or they might try to engage satisfied customers with a thank-you message in their preferred channels. 

Make the model smarter over time

Tracking the progress of the churn prediction tool helps service providers refine the scoring and effective next best actions over time. In other words, they can help make it smarter the more they use it. 

Based on data pulled into the tool, AI may learn that a particular factor is having an outsized impact on churn and weigh it more heavily in future churn scores. Or, it may ignore a factor that doesn’t actually predict churn. For example, analytics in an agent’s dashboard may show temporarily suspending service is not an indicator that the customer will cancel it completely. It may be a seasonality issue  — perhaps because they live elsewhere during that time. 

Service providers can use clicks, not code, to customize the company’s churn score with additional factors, which minimizes heavy lifting by IT teams. 

The service provider can also track the impact of actions. If they aren’t successful, more effective recommendations take their place, tuning the model along the way.

Since the churn score and recommended actions come from actual and evolving customer data, agents can feel confident that their offers are personalized and useful for reducing customer churn. They never have to guess. 

Get proactive about reducing customer churn

With an industry average net promoter score of 17, communications service providers have opportunities to improve their experiences and reduce customer churn. New approaches to customer retention, like visibility into churn risk and the ability to act quickly to reduce it, will help define success in this highly competitive space.