When customer lifetime value is a poor metric

In the book Drilling Down, Jim Novo makes a lot of great points about when customer lifetime value (CLTV or LTV) matters.

One of their main points is that the absolute value doesn't matter much at all. LTV should be used to select the campaigns that generate customers with the highest relative values. Campaigns with lower relative value should be ignored until the higher ones are exhausted.

This means you calculate LTV, but use it as an internal measurement to tell if specific campaigns performed well and as a way to measure prospective campaigns.

This is vastly different than how LTV is used in the popular articles where it's treated as a scoreboard (e.g. Store A has an LTV of $140 so is better than Store B who only has $120).

Say your average LTV is $100 across all customers.

Then you find retail customers have an average LTV of $120 and Shop app customers are $80. That tells you:

Add in costs and market research and it can even tell you which campaigns to run and which to retire. That's the forecasting side of the metric.

Say you want to expand to five new areas and have collected sample data from each to know how they should respond. Using the expected LTV for each area you can figure out which area to expand into first, second, and so on. You'd start with the highest LTV area first and moving onto the next ones once the first area is exhausted.

Rinse-and-repeat until the costs don't justify the investment.

This is a harder, more scientific, and methodical method of using lifetime value, but it simplifies a lot of decision-making and removes unproductive marketing waste. Who cares if you don't advertise on Facebook if the cost of acquiring customers from Facebook causes you to lose money.

For help calculating lifetime value for your Shopify store, including based on different acquisition channels and products, Repeat Customer Insights will automatically run many of the numbers for you.

Eric Davis

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Topics: Customer lifetime value

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