tl;dr
This report shows you how long it takes for your customer to place their next order.
The reports increases in granularity - at first we differentiate between the second purchase and all other repeat purchases. We then break up to time-lag into different groups and lastly break-up all repeat orders. This gives you full view of the time-lag for different levels of customer engagement.
Understanding this helps you to improve the Customer Lifetime Value by for example incentivizing inactive customers at a point in time that minimizes lost margin.
⚡ Since this is a retention report, the Date Range you define in this report select customers that were acquired in that period, but all activities until today are used to calculate the values. More Information here - Date Range Definition
What can I analyse:
The report is split up into three different sections with a increasing level of granularity thereby allowing you to get an overview first and then dive deeper into the details.
Filters:
On top of the report, there is the option to filter the customers used. Available filters include:
Product Title
Variant Title
Product Type
Customer First Order Tag
Customer Tag
Newsletter Subscription Status
The Product Title, Variant Title, Product Type and Customer First Order Tag only refer to the first order of a customer and filter the customers based on that.
Overview
The Overview shows you the core summarized time-lag metrics.
Average Order Value - while this is not a time-lag metric, it provides some context of the order that was placed prior to the time lag and therefore can be useful.
Time-lag - the number of days it took on average for a customer to place his next order. Only customers that actually placed an order are considered when calculating the average.
Standard Deviation of Time-Lag - the standard deviation show you the variability among the time-lag.
The time lag metrics are broken up into Second Order and Repeat Reorder as getting a customer back for the first time represents a different challenge than getting him back for a third and fourth.
Time-Lag Graph
The following graph breaks down the time-lag into different intervals and groups customers into them. On top of the graph, there are two controls.
Type - here you can select if you want data to be displayed only for the time-lag to the Second Order or for all Repeat Orders.
Interval - here you can select the time interval you want to group your customers into to.
The bars in the chart will show you the percentage of repurchases that are taking place within each interval whereas the line chart shows you the cumulative value of all repurchases up until that point in time,
Usually, this number is decreasing over time. Meaning that customer, if they place another order, are more likely to do so shortly after their previous order. This may not be the case for products with a high basket size and/or some seasonality factors build into them.
Time-Lag Detail:
This table breaks down this into even more detail by breaking up your customers based on the number of orders they have already placed.
The column headers indicate order count of the following metrics - e.g. "1-2 Order" shows metrics from the first to the second order and "6-7 Order" shows metrics from the sixth to the seventh order.
The following metrics are shown for each group of customers:
# Customers - How many customers are in this group. So for the "6-7 Order" group it shows you the number of customers that have placed at least 6 orders.
Conversion Likelihood - the percentage of customers that have placed an the following order. So in the "6-7 Order" group the percentage that placed the 7th order.
Avg. Time-lag - the time in days it took on average for the customer to place that next order.
Standard Dev. Time-Lag - the standard deviation of the time-lag.
Share of orders after x days - this takes the customers that placed the next order and checks what percentage of them did so within a certain time interval (intervals are taken from the Interval control in the Time-Lag Graph).
What can I use this for?
The time-lag report is an integral part of understanding the retention of your customers as the quicker your customers back to higher their Customer Lifetime Value will be.
When is the right time to incentivize customers to return?
To win-back inactive customers, many online stores use email automations containing discount codes to incentivize customer to buy again. But when is the right time to send this email? You want to maximize the number of customers buying again but at the same time minimize the margin you sacrifice by giving a discount to people that would have bought anyway. The time-lag report can answer that question.
Simply look at your time-lag intervals. Once you see that the incremental repurchases start flattening out, you likely maximize your outcome by sending out an email automation with an incentive.
💡 What does flattening out mean exactly? Though to say, without seeing the overall trajectory, but likely in the 2-5% range, depending on how aggressive you want to be, with ideally 75% of repeat orders already placed.
Measure success of onboarding emails.
Similarly you can also measure the early impact a new onboarding email has on your customers' repeat behavior (or many other UX improvements). The email automations are designed to introduce a customer to your brand and start the cross-selling process. When done well, these automations/initiatives not only result in more customers coming back but also customers coming back quicker.
As the Time-Lag report's Date Range is following our retention report logic, you can simply filter your customer by when these changes were introduced and clearly measure their impact.
Do you have a strong core of your customers that use your product habitually?
Ideally you should develop a strong core of customers that use your product frequently. This can easily be measured by the Time-Lag Detail report. If the Conversion Likelihood keeps increasing while the Time-Lag and the standard deviation keeps going done.
💡 Benchmarks to look at are Conversion Likelihood from second to third order of more than 50% and continually increasing to >80% of that.
Are customers getting fatigued with your product?
The same KPIs can also be used to understand if your customers are growing fatigued of your product at some point. This could be observed through the decreasing conversion likelihood as well as as increasing time-lag and standard deviation.
This could especially be the case for CPG products which people first might start to use regularly but after some time get bored and switch to something else.