Putting customer lifetime value to work for your customers

Customers have choices. With choices comes power. And with power comes the ability to walk away from poor products and services. Although both products and services are core to a company's value proposition, it is the service that is often easier to improve. Especially in sectors where it is hard to differentiate by products, an exciting customer experience can leave a lasting impression in a customer's mind. That’s why banks, insurers, telco's and energy companies are constantly looking for new ways to boost their customer service and thus customer experience. How can Customer Lifetime Value (CLV) help?

The customer service model
Typically, a customer care department tries to strike a good balance between costs and customer satisfaction. Cost efficiency is steered through metrics such as call propensity (CP; average number of contacts per contract per year) and average handling time (AHT). Customer satisfaction metrics include the customer effort score (CES), the net promotor score (NPS), and the first time right (FTR) rate.

Because of this balancing act, people tend to think that the well-known business saying about marketing – "anyone can save 50% on marketing expenses, but no one knows which 50%" – also applies to customer service. CLV puts that metaphor to rest. By embedding CLV-based operational rules, you can allocate a large majority of your customer service expenses to the right customers.

Great customer service is not a one-size-fits-all proposition. As preeminent behavioral data expert Peter Fader has argued, it is all about customer centricity. Figure out who your most valuable customers are, and put them at the heart of your efforts.

Improved way of working via operational rules
A predictive CLV can be estimated for each and every customer. To make this information actionable, the CLV must be calculated quickly (in real time) and be accessible at various places in the customer lifecycle. When done properly, CLV enables entirely new opportunities for automated logic via operational rules and informed customer care outcomes. Examples of operational rules in customer service include:

• Value percentile displayed on screen to better inform care agents prior to customer contact, enabling them to make better decisions in turn, e.g. a 95th percentile customer expects an agent who goes the extra mile, whereas a 3rd percentile customer may be satisfied with basic care protocols
• Prioritizing callers with higher CLV
• Routing callers to the most experienced or best customer service agents based on CLV
• Pushing special offers before the call ends, based on CLV/segmentation; When you know exactly how much a customer is worth to your store it becomes a lot easier to justify (or not justify) spending money to keep them. This could be in the form of free products, coupons, or free returns. Perhaps even personalized.

But what if AI takes over customer service
According to BrandGarage, 87% of the retail marketers and executives polled believe that artificial intelligence (AI) will help their customer service efforts. With this in mind, it is no wonder that companies are questioning the benefits of CLV in customer service. Why diversify your customer service when a single, automated AI solution will serve your customers at zero marginal costs?

It is exactly because of AI that we believe CLV is so vital. For one, AI will use the same logic and operational rules that your physical agent would – and in a more rigid way. You still have to provide your most valuable customers the best service possible. Second, the customer lifecycle also includes non-standard interactions that do not fit nicely inside the rulebook. The operational rules are just a starting point for customer engagement. Using AI and CLV in sync, the customer experience can truly become an optimal source of value.