Imagine a Customer Lifetime Value (CLV) calculation that can keep up with a rapidly changing economy. Individuals are adjusting their purchase habits: shifting from in-store to online; restaurant tabs to grocery receipts; and re-booking trips for the fall.
We need a way to measure the value of a customer that considers nuances around purchasing behavior while also considering industry or global purchasing trends. We need to be able to identify if the change in purchasing habits has increased sales, decreased sales, or just changed the purchase window with delayed consumption or bulk purchases. We need a calculation that goes beyond retention scores and average order value and incorporates the ‘frequency‘ and ‘recency‘ of purchase behaviors to determine if a customer is going to come back or if they’re gone for good.
Customer Lifetime Value Approach
Customer Lifetime Value is a metric to measure the health of your customer and product or service base. More importantly, CLV should be driving daily decisions about your investments – both time and money! CLV is defined as the predicted value of revenue tied to the future relationship with a customer.
An agile way to calculate the lifetime value of a customer is by using probabilistic models to assess (1) the expected number of future transactions the customer will make and (2) the probability they are retained.
We look at historical sales or transaction data – specifically the tenure of the customer, how frequently they’ve purchased, and how recently they’ve purchased – to fit two probability distribution curves per customer. We can calculate:
The expected number of transactions the customer will make in future period of length T which follows a Poisson distribution
The probability they are still retained in the given period which follows a shifted Geometric distribution
These curves are then combined and used to predict lifetime value:
CLV = Expected number of transactions * probability they are still retained * average transaction value
Using the example graph below – we see that both the recency of a purchase and the frequency of purchases increase the probability of a customer being retained in the following period.
New customer habits and behaviors are forming – forcing organizations to respond. This agile approach to CLV only requires three data fields per customer and has proven success in determining if a customer is going to come back or they’re gone for good. Customers are innovating the way they purchase; so let’s meet them where they are and innovate the way we measure, predict, and respond to their behavior.
Meghan Villard is a manager with RevGen Partners. She is passionate about empowering clients to make data-driven decisions that deliver value to their business.
Diving headfirst into AI without a strategy? That's like setting sail without a compass. A clear AI plan can turn tech buzz into tangible business wins and ensure your business is aligned for growth.
In an increasingly connected world, we need to engage with our customers and create a digital customer experience that feels genuine and is tailored to their journey.
This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Strictly Necessary Cookies
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
3rd Party Cookies
This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.
Keeping this cookie enabled helps us to improve our website.
Please enable Strictly Necessary Cookies first so that we can save your preferences!