Exploring the Relationship Between Lifetime Value and the Customer Journey

Is it possible to have bad customers? Kinda, maybe, maybe not. Is it possible to have good customers? Well, of course.

A good customer can be many things. In one case, it can be a customer with a consistent annual spend, it could be a customer who while not attributable to a large revenue gain may advocate for your product, service or company, thus pulling in additional revenue indirectly. Lastly, a good customer might be one who spends more each year and whose profit margin remains consistent.

One way to quantifiably tell good from eh, not as good customers is through a metric known as Lifetime Value (commonly referred to as LTV). KissMetrics provides a wonderful breakdown into how to calculate LTV.

In short, lifetime value calculates the projected revenue a customer is going to generate throughout their lifetime doing business with your company. In the KissMetrics example of calculating LTV for a consumer goods company like Starbucks, there are certain variables necessary to build out the model. Specifically, customer spend per visit, customer visits per week, the average customer lifespan, customer retention rate, profit margin per customer and the discount rate are all critical components of the equation.


So how can the journey map assist in calculating the LTV? (Warning: this is about to get a little math-y, a little geeky.)

Customer visits per week is an easily calculated figure if using journey map automation. Aggregating across events come out of the box when mapping customer journeys using hard data generated through order systems or product metadata.

Average customer lifespan is the duration of the journey from the point of purchase to the point of cancellation. Again, this can be easily grokked as long as your journey map isnt just an illustration but backed by true metrics.

Customer retention rate is comparing sets of journeys. Take the number of journeys that involved a purchase and dont involve a cancellation and divide by the number of journeys that involved a purchase.

So, journey mapping can assist in calculating LTV. And now we know our LTV. Now what? Well, breaking out LTV across customer segments can actually be used to optimize the customer journey! (If youre anything like me, youre marveling at the interrelatedness of these two concepts.)

Segmentation becomes a key step in this exercise. Which customer segment has the highest LTV? Now, how can the journey map be optimized to include more of these customers? Lets pretend our business is in auto repair. Well, the lets pretend that the highest LTV from our customers are those that allow us to service their automobiles consistently. Why? Because the consistent flow of revenue (albeit smaller than revenue incurred from major repairs) lasts longer as customers build trust in the service to avoid the higher costs of major repair.

For the segment of customers that have a lower LTV, how can the journey map be reconfigured to change the path of that customer? Take those customers who have come by the garage once or twice in emergency repair scenarios. Can we market our services to sell a different product and increase the longterm relationship with the customer? Trust (and LTV) are maximized in this approach.

Whether it be an auto-repair shop or a coffee shop (that undoubtedly keep our human engines running!), the symbiotic relationship between lifetime valueand the customer journey is critical to marketers. Optimizing one will undoubtedly help you optimize the other.

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