A customer health score is a metric about how your customers are interacting with your organization, and it is used to determine accounts at risk as well as those posed for expansion opportunities. However, even with the use of many standard health score measures such as outcomes and experiences, it’s easy to get blind-sided by a change in customer behavior.
First, let’s explore typical measures included in a health score. Measures that many organizations are using today include various outcomes such as deployment or adoption levels, return on investment such as milestones achieved during a specific number of days, customer engagement with your organization over a specific timeframe, and of course, product usage data.
A subjective area that may be one of the most important measures used today is experience metrics. Most Customer Success organizations have their people on the front line add manual measures into their health scores about how they personally feel a customer is doing. Customer sentiment is also collected by asking each customer to fill out surveys on a regular basis or after the completion of a particular milestone.
But what if you could gauge the customer’s experience without surveys or subjective CSM measurements? With new AI technology, you can do just that. It is now possible to deliver customer sentiment and intent directly into your customer success system from regular communications you have with customers every day.
There are hundreds, if not thousands of customer signals locked away in communication tools that you use with your customers. Intent can be pulled from Slack, Zoom calls, support tickets and even email communications. The AI tools gauge the cadence, inflection, tone, emotion and other items in a way that individuals just cannot do. Then, predictive models analyze this deep sentiment and loyalty data to accurately trend each account’s propensity to expand or renew.
After collecting and analyzing these signals, it’s possible for customer success teams to uncover the customer’s intent and what their next best step should be. This can assist your team in prioritizing how time is spent on each account.
What is amazing about AI is that now sentiment can be predicted with 90% accuracy with an initial data load - and this accuracy increases over time. This information can be used to review customer interactions and educate your team on learning styles, emotional responses and personality profiles. Training your CSMs just got easier because now you can use this information to discuss how to have effective conversations with your customers by enhancing this emotional intelligence skill set.
Bottom line: Now you (and your CSMs) can stop guessing about how your customers feel about you and your products - and you can get your entire team on the same page with predictive action plans and customer data to keep accounts on the right track.
The author, Mike Ryan, is a Business Development Manager at nCloud Integrators, an industry leader in customer success strategy and implementation, with a track record of improving customer journeys at hundreds of customers every year.