A lot of customer service and customer support teams are making the shift to customer success or even customer experience. While the former teams are reactive, the latter help identify customer problems and offer potential solutions.
CLV can help you answer questions like:
- Who will be the next customer in need of assistance?
- When will this happen?
- What future value is at risk from a dissatisfied customer?
Defining customer success
First, let’s define what makes an interaction with a customer successful. Most customer service teams use metrics like SLA, CES, NPS, and CSAT to assess the quality and speed of their solutions. Instead of assessing how well the customer service reps performed, let’s shift the focus back to the customer. Did they make another purchase after interacting with customer support? Are they spending more money with the company?
In short, evaluate whether or not the interaction with the customer success team positively impacted the customer’s lifetime value.
Ranking support tickets
With individual customer lifetime value metrics, prioritizing high-value customers and measuring boosts in CLV are easy ways to optimize your success strategy (or rank your support tickets).
Instead of following the first in first out method to handle support tickets, consider rank ordering your tickets by CLV. This way, you’re providing speedy customer service to the customers that matter the most to your company. High CLV customers will continue to spend money with you over their lifetime as a customer, so it’s important to solve their concerns quickly.
After support tickets are resolved, measure the impact on individual CLV. For example, if solving incorrect orders quickly increases CLV, make sure to address those tickets on the same day. If providing shipping updates doesn’t boost CLV significantly, those tickets can fall further back in the queue.
Consider two customers: the first places large orders with your brand but abuses the generous return policy; the second makes smaller purchases but rarely makes a return. If you just consider average order value (AOV), you might offer larger appeasements to the first customer, even though they often net your business a loss after accounting for return shipping and restocking fees. In contrast, the second customer may spend less, but has a higher lifetime value for your brand.
Instead of setting a general appeasement limit for all customers, you can base the appeasement on each customer’s lifetime value. If a customer is very high value, you can spend more money resolving their problem. This way, you aren’t investing in a customer that won’t spend more (or enough) money on your products in the future.
Over time, you can measure how much appeasements increase customer lifetime value and make adjustments accordingly.
Once you have adjusted how you rank your incoming customer service based on individual customer lifetime value, you can take the next step to predict customer problems before they occur.
The best reps anticipate problems and reach out to individual customers proactively. This can’t be done without individual-level data.
One of the best ways to use individual CLV is to predict each customer’s anticipated churn date. Once you have this information, your customer success reps can reach out to customers proactively to prevent churn.
A sophisticated early CLV model uses customer attributes and behaviors to calculate lifetime value. So, you will know what products, channels, demographics, and more are associated with high-value customers. You can then focus on these areas for proactive customer service.
For example, if customers that purchase a certain product are high-value, reps can use this product as an upsell opportunity for low- to medium-value customers.
Customers can be wrong
Ultimately, this new way to think about customer service and customer success throws shade at the commonly used phrase in service: “The customer is always right.”
We’re arguing, in fact, that the customer can oftentimes be wrong. If you’re providing support to a low-CLV customer that only makes purchases with steep discounts, it doesn’t make economic sense to offer an expensive appeasement. You shouldn’t waste limited resources on a customer that you know won’t spend enough money with your brand in the future.
Seeing the future
The customer success strategies outlined above rely on predictive customer lifetime value metrics. Of course, we aren’t fortune tellers. That’s why it’s important to implement a customer lifetime value model that’s accurate at the customer level. Some popular models can be very accurate at the cohort level, but have performed poorly at the customer-level and require months of customer history to make predictions.
When making the shift from customer support to customer success, make sure the future view of your customers is clear.
At Retina, our early CLV model is the most accurate on the market today at the individual level. We can predict how valuable your new customers will be at the point of first transaction. Contact us to learn more and discuss how to use CLV to optimize your customer success strategies. For more information, read our whitepaper on how to think about customer sales concentration.