Using Lifetime Value (LTV) as your retention metric for your Shopify store

There are a lot of different metrics you can use to track customer retention and loyalty.

Lifetime Value (LTV) is a good one for many stores.

Since it measures how much a customer has spent already, it's clear and straight-forward to calculate. That's why it's used a lot with paid advertising.

LTV has some drawbacks for retention. Primarily, it treats rare high-spending customers the same as frequent low-spending customers. These two segments are very distinct and should be handled differently (e.g. like how my app puts them into the Whale/high-spender segments and loyal/VIP segments).

It also doesn't take into account time at all. Customers who only ordered far in the past will be less loyal than customers who ordered this year. Or customers who order every month are more loyal than customers who place a 12x-sized order every Black Friday.

To make it your main retention metric you'll want to make it easy to track. Shopify has some reporting on it per-customer but you might struggle to get a high-level overview like Average LTV. Extracting and summarizing your data every month in Excel or Sheets could help with that if you're willing to do some manual data management.

If you had to choose one retention metric to watch, LTV or Average LTV could serve you well. Repeat Purchase Rate or RFM are a bit better but more difficult to track without a strong analysis system.

With Repeat Customer Insights you can track your Average LTV every day, along with Repeat Purchase Rate and stronger models like RFM. The automatic data imports saves you from having to keep the data in sync.

Eric Davis

Segment your customers to find the diamonds in the rough

Not all customers are equal but it is difficult to dig through all of your data to find the best customers.
Repeat Customer Insights will automatically analyze your Shopify customers to find the best ones. With over 150 segments applied automatically, it gives your store the analytics power of the big stores but without requiring a data scientist on staff.

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Topics: Average ltv Customer lifetime value Customer retention

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