When you start to build a loyalty program, you have a ton of decisions to make. One is if you want to offer different levels of rewards and if so, what should those levels be.
You can make a guess and use something like 10 orders but that's probably going to be too low or too high for your store.
Before you decide, it's best to look at the data.
Say you know your customers place 8 orders on average, either from the analysis in Repeat Customer Insights or by crunching all of that data yourself.
Setting a loyalty level at 10 orders would keep it out of reach for most customers. Your frequent orderers will hit it and some customers might stretch by ordering a bit more frequently.
Setting it at 5 orders though will make sure most customers would reach that level.
At 5, since most customers will reach it they might order sooner. If you're at 4 orders, you're likely to order early to get the loyalty reward.
5 is also achievable for low orderers. If someone is at 3 orders, they might be incentivized to order two more times. If those orders are a positive experience for them, the reordering behavior might stick. Then they end up reordering more frequently.
Much will depend on your reward. A costly reward at 5 orders would be a bigger decrease in your profit than at 10 orders so it depends on how much margin you have on the other products. Basically, can your margin makeup for the reward's cost.
Lots to think about right? Having the data reframes a random guess into a business strategy decision. And a decision that you can monitor and change if it's not working out.
(This doesn't even consider the option of having multiple levels: one below the average to speed up orders and ones above to get customers to stretch for them).
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.