In the book “Customer Centricity”: Focus on the Right Customers for Strategic Advantage, Professor Peter Fader of Wharton Business School describes the customer-centric strategy as follows: «identify the most valuable customers, extract the most from them, and find other clients just like them».
Without a doubt, it is a unique way of defining the concept of customer focus, emphasizing the importance of the company’s long-term profitability. Fader’s strategy encourages a shift from providing good service to all customers to providing excellent service to exceptional customers.
But, who are exceptional customers? Those who are the most valuable to the company in the long run, i.e. those with the highest Customer Lifetime Value.
Customer Lifetime Value is defined as the value that the company will obtain from each of the customers throughout their relationship with the brand.
How to take CLV to its maximum potential?
Customer Lifetime Value can be broken down into three basic factors: The average ticket or average purchase, the number of purchases in a period of time and the length of time in the relationship with the customer.
What makes these factors so effective is that each one can act as a standalone lever to increase a client’s CLV, and when combined, they create a multiplier effect.
The customer is omnichannel by definition; he uses the available channels depending on his circumstances and needs. The CLV measures the client’s current global value to the organization and how much we can expect it to contribute in the future.
How to use CLV to target high-potential customers and find them among our audience.
This vision of the client’s future is one of the keys that Fader shows us in his book, where he talks about «satisfying the current and future needs of the most valuable clients».
Thanks to the use of advanced analytics we can better analyze, anticipate, and forecast customer behavior in order to prescribe services and goods that meet their future needs.
Machine learning, artificial intelligence, and advanced analytics techniques learn from our clients’ data to create seemingly magical algorithms. In reality, it is nothing more than the rules of behavior learned from data.
The CLV is a reflection of the evolution of customer habits, which is why it is critical to detect changes in their behavior and launch actions such as upgrading, upselling, and retention.
We can, for example, more accurately measure the ROI of our acquisition campaigns with a long-term benefit thanks to CLV.
If we profile our audiences as a true reflection of our clients with the highest potential value, we will obtain clients that are more in tune with our offer, more loyal, and ultimately more beneficial, leading us to a kind of virtuous circle in which we increasingly manage to make current clients more profitable while attracting new profitable clients.