Predict and Minimize Customer Churn
Subscription businesses live and die by their customer retention. Prevent churn by seeing the risks ahead of time.
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Predict and Minimize Customer Churn
Churn Happens in Two Ways
The adage “customers come for your product but stay for your people” is a useful one in thinking about how to avoid customer churn. You clearly need to track product usage, and most organizations are doing well at this. However, equally important, is tracking the relationships that your team has with customers. Any customers who have not developed strong relationships with your team could be at risk for surprise attrition.
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Good Intentions, Faulty Memory
Customer success managers are among the most diligent and detail-oriented professionals in any organization. However, when they are managing upwards of 100 accounts, it’s very easy to forget to stay in touch with some of those accounts. The top account and the “squeaky wheels” will get attention, but the quiet accounts can be forgotten about and ultimately churn.
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Users and Buyers
Once a purchase is made, there is often a significant difference between the day-to-day users of the solution you offer and the executives who make decisions on renewal and expansion. It’s often more important to maintain relationships with the decision-making executives. However, the daily back-and-forth of customer success is usually with the front lines. Monitoring the number and strength of exec relationships is crucial to understanding whether you are set up for renewal and expansion.
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Once You Know it’s Often Too Late
Once a customer feels neglected it’s often too late. Keeping retention high means making sure that every customer feels engaged throughout their entire customer journey. That means your customer success team needs to be in front of any issues, not trying to fix problems when it’s already too late. Proactive alerts when any relationships drop or your team loses touch with customers keeps problems from happening and customer satisfaction high.
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Better Algorithms, Better Data
Many leading customer organizations use customer health scores and customer models to deeply analyze the customer base and their likelihood of churning. As with any data science and artificial intelligence efforts, better results are usually created by better data, not by better algorithms. Adding data on relationships, and where those relationships are in the customer’s organizational hierarchy is key to ensuring that the models accurate reflect churn risk and expansion opportunity.
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