WHY ARE Metrics and Measurement IMPORTANT?
While Customer Success management is NOT Customer Success measurement, there’s no getting around the importance of measurement and assessment to know what you need to do to retain and grow healthy customers. Some metrics are more informative of customer health than others. We have the capabilities of extracting voluminous data about our customers, but more isn’t always better. It’s important to know which data to track and, perhaps more importantly, to know what to do with the information you do glean about your customers.
WHO NEEDS TO KNOW HOW TO USE Metrics and Measurements?
Determining which metrics to track and aggregate, and how to use the data you have is a management decision and should be made based upon profiling what your ideal loyal customers look like. Then, those who work directly with customers (CSMs) should be trained to respond properly to relevant metrics.
PUTTING Metrics and Measurements IN CONTEXT
Customer data is ubiquitous. More than ever, it’s readily accessible through most customer success platforms, and it can be seen and analyzed at any time throughout the customer lifecycle. Whether it’s usage, survey, or other types of data, metrics that require attention can (and should) be tracked throughout the life of the customer.
GUIDELINES FOR Metrics and Measurements
Not all data is created equally. And not all healthscores are accurate indicators of customer health. In order to not get bogged down with voluminous and confusing metrics, you’ll benefit from some guidelines about data aggregation, as well as direction for responding to certain metrics. Here are some guidelines:
- Develop health scores by looking at and benchmarking behaviors and characteristics of your ideal/best customers. Take time to look at your best customers. What makes them healthy? What are their usage trends? How often do they engage with your CSMs? How are they structured for success (e.g. strong internal sponsorship? Executive support? Well-defined business case, etc.)? Benchmark these characteristics and behaviors, and build health scoring around them.
- Usage data is not the end-all-be-all. It’s not the quantity or frequency of use, but the quality that matters most. Consider usage trends more granularly. For instance, study customers’ breadth of use, change in their usage patterns after updates and changes in usage after service engagements. These metrics are more telling than simple logins or time spent in product.
- Surveying is a legitimate approach to garnering information about customer satisfaction, but must be employed judiciously. Here are two considerations for the use of surveying as part of understanding customer health:
- Whether you’re using NPS and/or other surveys, the scores must be aggregated across respondents within an organization. A single response from a user (or executive) does not accurately reflect the state of health of the entire account.
- Surveys should be administered and analyzed in the context of specific life stages, and then analyzed for benchmarking and to inform internal decision-making. In other words, for survey responses to be most meaningful, they must be viewed through the context of lifecycle so that you can properly address and operationalize delivery and response.
- Don’t ignore engagement data. Do your customers attend trainings? Do they respond to requests for meetings or references? These data points are significant. Customers who regularly engage with material you make available to them (and are responsive to your requests) are good customers. They may be frustrated by adoption issues, but their engagement indicates their desire to overcome those frustrations.
- Not every support ticket reflects churn risk. Support tickets, in and of themselves, indicate usage first and foremost. Frequency and types of tickets can provide insight into usage patterns and potential frustrations. It’s not the need for support that’s problematic. It’s the failure to properly respond that could get you in trouble with a customer.
- Always seek to learn more. Metrics and aggregated health scores are meaningful data points about customers. However, it’s potentially dangerous to have automated responses to some metrics. Even in low-touch Customer Success models, automatic triggers that directly respond to customer behavior without at least double clicking into the data can be risky. Consider having alerts sent to CSMs when data points reflect a customer is behaving less than ideally. Create and operationalize workflows based upon the analyses of these data points. (Note: Having a customer success platform that helps you standardize responsive workflows is vital to responding properly and efficiently so that negative metrics don’t result in churn.)
WHAT DO WE LEARN THROUGH Metrics and Measurements?
We have the opportunity to know so much about our customers. The challenge isn’t accessing data. It’s parsing through it to extract and measure meaningful data. Identifying relevant and actionable metrics is best done by benchmarking the characteristics of your ideal customers and then using the most significant data about them to track in other customers. Equally important is not being too reactive to metrics, but analytical and responsive. Take time to unpack the data and understand the root causes for changes. This will better inform your response and result in more appropriate interactions with customers.