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.
Use cohorts to find out who the best customers are in your Shopify store
Repeat Customer Insights will automatically group your customers into cohorts based on when they first purchased. This will let you see how the date customers bought would impact their behavior.