SaaS businesses have no lack of data about their customers. The challenge is making use of this data to identify customers who are at risk of churning, ready to upgrade or buy more, as well as those that are ripe to convert from a freemium or trial to a paid account.
In this excerpt from our eBook, Predictive Analytics for Customer Success, we explain how predictive analytics can help Customer Success teams become more effective and productive in their day-to-day activities.
The advent of SaaS and its subscription-based business model has shifted power to the customer, who no longer has to make a significant up-front investment in software and can more easily change vendors if they perceive advantages.
Software vendors need to work smarter in order to respond to this new reality. Increasingly, SaaS vendors are relying on their Customer Success teams to retain, engage and grow their existing accounts.
A key difference between Customer Success and Customer Support is that the former strives to be proactive in reaching out to customers, whereas the latter is a reactive response to customers who are having issues.
But the traditional Customer Success approach of periodically contacting each customer is time consuming, inefficient, and doesn’t scale as the number of accounts grow.
Moreover, the tendency to focus on the loudest or largest accounts leaves other customers to fend for themselves just when a helping hand might make the difference between success and distress.
Customer Success Management (CSM) solutions can monitor accounts for specific behaviors based on rules set by the SaaS vendor, and notify the customer success team when they detect potential issues like too many support tickets or a drop-off in usage.
This is helpful, but is still reactive and dependent on the SaaS vendor's intuition in picking the right signals. Not every customer who ultimately churns exhibits the behavior being tracked by a particular set of rules. Some just conclude they aren’t getting the value they sought from a solution and the next thing you know, there’s a cancellation request in someone’s inbox.
What if you could be alerted more accurately and earlier to accounts that are likely to churn? Or easily identify those customers who are most likely to convert from trial or expand their use of your product?
You’d be in a better position to turn around struggling customers, as well as help those who are ready to purchase more from your company.
If the Customer Success team had the ability to target the right customer at just the right moment, their productivity would improve, as would key metrics like retention, expansion revenue, customer satisfaction and Customer Lifetime Value.
Fortunately, emerging Customer Success software with advanced analytics can leverage the vast troves of customer data to make this wish a reality.
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In the next installment, we'll explain predictive analytics and discuss how big data and predictive analytics fit into the world of customer success.
Want to learn more? Download our eBook: Predictive Analytics for Customer Success.