When you can find when a customer will make another purchase in your ecommerce store, you can use that knowledge to increase your store's revenue and decrease it's inventory costs.
Individual customer behavior is impossible to predict but when you're looking at your customer base as a whole, you can draw some conclusions and make some fairly accurate predictions about their behavior.
One powerful prediction is around repeat purchase behavior. If you know when a customer will be making another purchase you can:
- adjust inventory so what they want is in-stock
- send them targeted marketing messages when they are getting ready to buy
- have accurate forecasts of your orders and revenue without hoping and praying
In order for this it happen though, you need to calculate how often your customers are repurchasing. This can be done by using a formula called the Customer Purchase Latency.
Customer Purchase Latency
Customer Purchase Latency simply tells you the delay between a customer's purchases. If Sandy buys from your store every Monday then her Customer Purchase Latency is 7 days.
Regular customer behavior isn't that consistent though. Sandy might buy today, then 13 days from now, and then 38 days from now. Her overall latency is 19 days but that's not really that useful.
What's better is to lay out her entire purchase history based on her order number:
- first order latency: 0 (always 0)
- second order latency: 13 days (13 days from now)
- third order latency: 25 days (38 days from now - the second order's 13 days from now)
Now we can see a little bit of her behavior, but let's get away from looking at a single customer and instead look at a group of customers.
(If you want to learn how to calculate customer purchase latency yourself, read this article here)
Looking at the latency storewide
Lets say you took the time to calculate the Customer Purchase Latency for your entire store and you come up with these values.
| Order number | Latency |
|---|---|
| 1st | |
| 2nd | 19 days |
| 3rd | 32 days |
Now we can start to predict the behavior of an average customer.
New customer journey example
We'll call Sally a new customer who just ordered from our store today. If she's an average customer we can predict that she'll place her second order in 19 days. Roughly speaking, that's a little over a week after we fulfilled and shipped her first order. I good way to make sure she comes back is to send a post-delivery email, maybe with a special offer for her second purchase.
Then about a month later she'll be coming back to buy for her 3rd order. We'll want to stay in touch throughout that time, maybe with a customer email campaign or something similar. We'd want to put more buy messages in the emails as she approaches the one month mark.
Existing customer journey example
John is an existing customer who placed his first order two weeks ago. Assuming he's an average customer, he'll probably be starting to think about placing another order in the next week or so. Now would be a good time to send him a personalized marketing message to see if we can jump to the top of his mind and secure his reorder.
Store impact of Customer Purchase Latency
Knowing your store's Customer Purchase Latency will help you predict the behavior of your new and repeat customers. It's not 100% certain but you can use the knowledge of upcoming orders to better forecast your store's revenue and inventory needs.
Optimize your promotion timing to save money and attention
Repeat Customer Insights will analyze a ton of customer behavior data for you, including their buying cycles.
If you knew exactly when the majority of your customers were ready to buy again, you can increase your orders and profit just by tweaking your message timing.