Taking Customer Success Management to the Next Level with Data Analytics

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Click to learn more about author Victor DeMarines.

The role of the customer success manager is both expanding and transforming and the reason for that shift is ….data. No longer is it enough to simply reduce customer churn. The best customer success managers or customer success data analysts – whatever their actual title may be – are the ones who are able to master customer data measurement and analysis.

To be successful in this data-driven age, customer success managers need to be able to pull together data to formulate new ways to measure satisfaction and drive profitability. By deeply understanding what makes their customers successful, how to best engage with existing and new ones, and how to come up with customer health scores that guide engagement, managers are able to drive profitability for both their company and their customers.

So, how can customer success managers reach that next level? Let’s consider some best practices for aggregating and analyzing anonymous software usage data.

Map customer success metrics and usage. This simple question is the most basic starting point for determining if a customer is successful with your product: What is the value proposition of your product, and how does that value proposition map to its actual use? Having clear metrics for what your customers should achieve with your product will enable your team to further correlate those metrics with how customers use it to find a host of new and useful insights. But what’s more, having easily consumable usage data can often help you determine where your customers are realizing their successes—or, importantly, where they’re not.

For instance, consider the broadly defined value proposition of increasing the speed and accuracy of creating a sales order. By looking at usage data across the base of customers conducting that process, customer success managers can drill into things that tell us a lot about whether they’re achieving speed and efficiency—and what’s holding them back or accelerating it. Trends in usage metrics like runtime and feature usage, further correlated with system attributes or geography, reveal potential roadblocks presented by either the way the user is leveraging the product or the system itself.

Maybe it becomes apparent that a majority of customers are bypassing a new feature and instead completing the process outside of the system. With this information, customer success managers can develop and communicate best practices and relevant educational content that both informs new users and empowers existing ones to use the product more efficiently and effectively. This has a host of benefits from determining and developing best practices, to finding and cultivating relationships with power users who can become advocates for your products.

Develop targeted customer engagement strategies. As we seek to fulfill the metrics by which we will be measured for success, sales professionals can tend to become reactive to every piece of feedback a customer relays. An angry phone call or email, or even simply a few tweets and retweets by customers unhappy with a bug or a new road map direction, can color all customers’ interactions. Having access to usage data can help us reliably determine the level of severity of the problem the user is presenting to us and help us to formulate the proper level and type of engagement. With access to data on use across the base, and the ability to break down that data by system attributes and geography, we can see that perhaps that bug is only affecting users on a certain operating system version, while users on a different OS or version aren’t seeing the issue at all. The customer success manager can reach out with both targeted information across communication channels that prevents a molehill from turning into a mountain, and, most importantly, lend their customer a short-term solution so that their business is not disrupted.

Determine relevant levels of customer health. Customer health can be a difficult—but important—metric to track, and it of course differs according to the product delivered. But at its basic level, it demands examining the frequency of use mapped to feature usage. Information on feature usage that can be broken down by targeted parameters like runtime by module gives us a strong foundation to communicate with customers about their needs and determine what defines a satisfied customer. One of the most innovative ways customer success managers do this is with targeted, in-application messaging. Customers that map to different usage profiles can be messaged with the same question—“How would you rate your level of satisfaction with the software?”—with the results being used to determine the characteristics of those who are highly satisfied, those who are not, and the many who fall somewhere in between. That information can help determine levels of satisfaction and the appropriate actions to take to boost engagement with each cohort.

While those are just a few of the best practices customer success managers can deploy, it is clear that by tuning deeply into data usage analysis, managers can use data insights to help define best practices, identify patterns for meaningful upselling and cross-selling, lend input on relevant content creation, and make customers advocates for products to drive new customer acquisition.

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