Reactive, Proactive, And Productive
Product Analytics transforms reactive Customer Success Management (CSM) into proactive and ultimately more productive work.
CSM traditionally lives by the rules of Net Recurring Revenue, Net Present Value, and First Response Times. However, all these performance indicators are reactive in nature. You can only learn from your mistakes, but it's too late to prevent the issue from happening when the horse has already bolted from the stable.
With Product Analytics and a Customer Health Score (CHS), you can see the storm brewing.
If the frequency of product use is dropping and feature adoption is consistently lower than during the prime days, then it’s easy to predict that something fishy is going on. While it might be hard to know, for the sake of privacy, which individuals are shying away from your product, you still might be able to react on the account level in b2b business.
What is the Customer Health Score?
A Customer Health Score is a metric that measures customer satisfaction with a product by correlating product usage and direct customer feedback, such as support requests.
The Customer Health Score is assumed to have a direct relationship to the likeliness of a customer churning, like switching to a competitor or stopping to use a particular solution altogether.
Customer Health Scores is often a weighted score of various input factors focusing on frequency, breadth, and depth of product usage. Frequency of use might include how much time customers spend with the product and how often. The breadth of usage might include how many potential users benefit from the product. And the depth of usage might focus on how many key features are being actively used. Product usage figures are often matched with other input data to get a more holistic view of customer health, such as the number of support requests or errors logged from devices used by a particular account.
However, somebody needs to collect all this data and make sense of it. This is where Customer Success Analysts come into play.
What is Customer Success Analytics?
Customer Success Analytics is the process of monitoring and interpreting key performance indicators related to long-term revenue generation and customer churn.
Customer Success Analytics collects, enriches, visualizes, and interprets customer behavior data.
For example, we at the Qt Company have a Customer Success Analyst (which does hide behind the mundane job title of business analyst) who follows all the critical customer data from Net Recurring Revenue to product activations. The fact that we at Qt sell UI and middleware software libraries, among many other things, illustrates that Customer Success Analytics always needs to adopt the metrics to the business. The utilization of the Qt framework software libraries is not being tracked itself because they are available as source code and can be modified anytime. Hence, our Customer Success Analyst tracks the next best thing, the downloads of the Qt framework software packages.
Top 5 Customer Success KPIs
While different businesses have different metrics on their radar, our suggestions for the top 5 customer success KPIs are below.
Net Revenue Retention
Net revenue retention is the total revenue gained from a particular customer, typically in a SaaS or subscription licensing relationship, over a period such as a month or a year. Net revenue retention calculates the total revenue (existing and new expansion revenue from cross- and up-selling) minus the churned revenue(expired, terminated, or downgraded subscriptions) of an enterprise account or, if multiple subsidiaries are in play, of the parent account.
Customer Churn Rate
The Customer Churn Rate measures the number of customers who discontinue their subscription or contract, either partially through a downgrade of their spending or completely, during a given period. Companies with a SaaS or subscription business model often set a target churn rate to manage innovation investments, support team capabilities, product differentiation, and pricing.
Customer Health Score
The Customer Health Score combines different product engagement metrics and support request trends into a single figure for each customer account.
Product Analytics solutions deliver most of CHS's input data, including usage frequency and depth values.
Average Time to Value
Time to Value defines the time for real value to be derived from your product: It is the time it takes for a real customer problem to be solved or the time a tangible delight has been delivered. It's not the perception of the value but the value itself.
While customers may have reached the "aha moment" quickly through an optimized onboarding experience, they still have not gained the product's actual value. End users might get an initial perception of the value that your product messaging promised, but the product has yet to deliver any real value. Therefore, your product or premium feature might not be sticky enough yet, and customers are walking through those revolving doors of customer retention.
Product Analytics solutions can help to cut down the Time to Value. Product Analytics support Time to Value enhancement with event-based and UI path tracking.
Average Issue Resolution Time
Average Incident Resolution Time indicates the speed of turning issues into workable resolutions. While many support teams measure their performance with First Response Rates, we suggest looking at the actual issue resolution times. The resolution of the issue (also called an incident by those in the IT Service Management industry if it refers to a product error) can be through a workaround or an actual fix to the problem. The focus should be on the resolution and not on the speed of the first response (which helps the customer only a little after an initial recognition that your company cares). The resolution of the problem means that the customer can continue to use the product as intended and is, therefore, an essential measure for the quality of Customer Success.
Product Analytics can help identify issues even before they are reported to the support team. Some Product Analytics solutions can catch when the software runs into an error and report back to the makers of the product. Imagine you as a customer; what is more satisfying than calling a support desk, and they already know that you have a problem and they have a workaround at hand?
Proactive Customer Success Management
Successful Customer Success Management depends on attitude and laser focus on customer centricity. Good Customer Success Managers react to financial and feedback indicators to serve customers better.
However, true mastery in Customer Success is only achieved through proactive service powered by product analytics solutions.
Customer Success Manager champions want to learn their customers. Customer Success Analysts are vital contributors to interpreting near real-time customer behavior data. Product Analytics solutions such as Qt Insight support HMI and embedded device makers in taking Customer Success to the next level. If you want to know more about Qt Insight, the only product analytics solution embedded in a C++ application development platform, then check out more here.
Interested in our eBook "Product Manager's Guide to Analytics"? Download your free copy here.
