It’s unlike most SaaS B2B businesses not addressing customer retention. But the problem lies in which data to look for. There is a treasure trove of data on how customers interact with your organization yet most organizations are unable to act on these data points. Most often the Customer Success Managers are acting when it is already too late or don’t know which action of theirs will lead to retention. If you can’t keep the customers you have, you certainly can’t sell them anything more. In our last blog, we discussed the 7 data points that you need to monitor to reduce customer churn, here we will talk about the 3 kinds of early warning systems to drive b2b saas customer retention.
Make the data available to customer success managers to dig in and explore. Very old school and time-consuming. You can even call this a fake early warning system. Since it gives you all the data but no insights and no warnings. CSMs are lost in digging data all the time vs acting on it. Too much data and visuals can be overwhelming to make sense and prioritize. Key items slip through the cracks all the time. Good data, but CSMs are under load to analyze and make sense out of it.
[popup_trigger id=”4257″ tag=”span” class=”speak-expert-NRR”]
Account health and Risk Alerts
Data is analyzed to be meeting certain predefined health scoring risk criteria. As and when the criteria have met a Health or a Risk Alert is raised and CSM can act on it or an automated action can also be triggered, such as offering training or request for a meeting. This is the current state of customer success technology. Works perfectly IF configured correctly. But that is exactly where the challenge starts. With hundreds of data points and with so many overlapping complicated correlations with retention. It is simply beyond humans to comprehend all and have all scenarios be captured as rules to trigger various alerts.
Next-generation of using data in CS to drive b2b retention. Given the problems of configuring health and alerts, it is rather strange that no one built this sooner. AI in Customer Success can consume all the data, look at historic churns, upsells, and renewals to figure out what it takes to drive that retention. And then make the right intervention recommendation, for the right account, at the right time, for the right reasons. And all this is done for (not by) the CSM. CSMs job is to act on what s/he is good at that i.e, working with the customer on the recommendation, not to waste days digging into the data. AI is always learning and incorporating any user behavior changes as the customer matures or your product gets new features and updates. Never requiring CSMs or CS Ops time for reconfiguring any alert criteria.
With hundreds of data points, having a rule or criteria-based early warning system is broken and obsolete. Despite all the data and all the computing power, it leaves gaps and blind spots which are large enough to make the entire Customer Success retention program fail.
Suggested Read: The ultimate guide to customer retention