So How Do These Metrics Provide Help Discover ROI for a Customer Success Team?
Customer success teams are most efficient and effective if they intervene only when needed—i.e., if a customer is at risk. Interventions with customers not at risk are not only an unwarranted expense, but also represent opportunity costs of assisting the actual at-risk customers. Therefore, low predictive accuracy actually lowers a customer success organization's efficiency and effectiveness. And low customer coverage means a missed opportunity.
The dynamics of customer coverage and predictive accuracy are shown in the graphic. If the metric used has low customer coverage and predictive accuracy, customer success teams will be constantly reacting to fire drills in order to save customers. If the metric has high coverage and low accuracy, the customer success team will have lots of false alarms resulting in wasted effort and opportunity costs. Likewise, high accuracy and low coverage will result in unanticipated churn. Only with high accuracy and high coverage can you get to efficient and effective churn prevention.
The takeaway is that predictive analytics is a powerful tool for creating an efficient and effective customer success organization. With that said, predictive analytics developed from experience and intuition without validation can actually have the opposite effect.
Matt Shanahan is the CMO at Seattle, Wash.-based Azuqua. He has nearly 30 years of experience in the technology industry, ranging from Accenture to startups. He is a proven entrepreneur as the VP of product marketing and management for Documentum from startup through initial public offering and most recently as co-founder and SVP of strategy for Scout Analytics.