Customer service and customer support teams are making the shift to focus on customer success or even customer experience. Instead of simply reacting to customer problems, customer success teams help identify and predict problems and offer potential solutions.
Plus, not all customers are the same value to your company, so not all should receive the same level of service or same spend on appeasements.
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, it’s important to 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 (FIFO) method to handle support tickets, consider rank ordering your tickets by CLV. This way, you’re providing speedy 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.
While the FIFO method may work effectively for your company while it’s small, it’s important to reassess your strategy as your company grows. Shifting to a CLV-based method will allow you to retain your best customers as your company grows.
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. This strategy empowers customer success representatives to make informed decisions in real time for each individual customer.
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 disproves the commonly used phrase in service: “The customer is always right.”
In fact, 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.
The customer success strategies outlined above rely on predictive customer lifetime value metrics. 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.