Published by Rishabh Kishore on May 21, 2020

#3: Double Your Profits with The Magic Metric: Customer Lifetime Value

The key metric in determining your company’s Best Customer group is Customer Lifetime Value or the Magic Metric. Our data analytics team has, inspired by Peter Fader’s CLV model, created an accurate formula to calculate the Customer Lifetime Value of every customer in your customer base. The customers that fall in the top 20% percentile in CLV, form the Best Customer group. Customer Lifetime Value is the sum total of the predicted revenue that an individual customer will bring to the business. It is a forward-looking, predictive measurement calculated by modeling and projecting how long the customer relationship lasted or will last, number of transactions, and value of the transactions.

How is CLV useful to your brand’s success?

Tells you what exactly each individual customer is worth. Is the customer worth retaining? Is a customer with a similar profile worth acquiring? By knowing the precise value of each customer, the brand can decide whether it is costlier to retain the customer and engage with them or leave them as is. Essentially, the brand will realize that the only group worth retaining and engaging (especially to boost profits) is the Best Customer group.

Segmentation: Perhaps the most useful function of CLV is that it helps in dividing your customers into real, quantifiable, and tangible segments. It helps in distinguishing between the most-valuable customers and the ones that do not meet the threshold.

Customer Equity: CLV (and consequently Best Customer) gives us a new measure of a firm’s value. The sum of all CLVs of present customers determines the Customer Equity in the brand. A term first exclusive to the marketing world, customer equity has emerged as an important determinant in the firm’s total equity because customers are seemingly sticking around longer than they rationally should. (Learn more about Customer Equity and CLV from Peter Fader here.

Why is our CLV Model  Superior?

Several CLV models already exist but ours avoids simple mistakes that those models tend to make.
Status of the Customer: The easiest database is to leverage the data of the ex-customer to build models and predict future customers but just as important is to quantify the uncertainty of the still-active customer; a factor our model takes into account.
Type of Customer: It is quite easy to determine the CLV of a contractual customer relationship but non-contractual businesses are quite complex. Our model differentiates between both types of businesses and focuses on non-contractual, continuous businesses.
Customers are not Normal: Customers do not have a normal distribution with relation to their CLV. As emphasized earlier, the 80:20 Pareto principle applies strongly to our CLV model. The model stresses on the fact that 80% of the revenue is brought in by 20% of the customer base (aka the Best Customers).

Our data analytics team has developed this Magic Metric just for companies like yours to identify your customer base and optimize your marketing or advertising efforts.
In the next blog, we will talk about how you can create a growth model with VRM. Stay tuned!

 

Rishabh Kishore

Rishabh Kishore works with Rajesh's team, and is helping in developing the VRM model. He did Economics and Legal Studies at the University of Wisconsin. When not working, he likes to play frisbee

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