An order placed by a customer is a like a little section of their retail DNA, a small piece of the overall picture of their relationship with your brand. If you can synthesise this data with other details, such as demographics and engagement behaviour, you can begin to create a nucleus of customer information that can be used across all of your targeted campaigns to encourage that next purchase.
A client recently asked me how they should analyse their customer orders and use that information effectively, so I put together a step-by-step guide. Follow these three easy steps to make your data work for you.
Step 1: Determine how many orders your customers place.
It’s important to get an idea of how many times your customers typically buy from you. Do you sell a product that users buy frequently, or do you tend to see longer periods between purchases? Create segments (order pots) based on the number of orders placed by each customer, and calculate your average. For most businesses we work with, the average order placed by a customer in a 12-month period is between 1 and 2. How many customers have placed no orders in the past year, and how many have placed more than your average?
Step 2: Establish key metrics for each group.
By analysing the behaviour of each segment, you can create a detailed profile of your customers and serve personalised content based on your data.
For those subscribers who haven’t yet made their first purchase, you might observe the following metrics:
- Registration date: What is your average purchase cycle? If a subscriber is approaching that date and still hasn’t purchased, develop a campaign to entice that first purchase.
- Number of opens: If the subscriber hasn’t opened several emails, look closely at their preferences and send more relevant content. On the other hand, if they always open your messages, reward them with an incentive for their consistent engagement. For example, give subscribers who’ve opened 10 of your emails a 10% off coupon.
- Last click date: Are subscribers only clicking on certain emails? Change up the content for non-clickers, and expand the content for those who click on particular things.
For subscribers who have placed one or two orders, you might take a look at these metrics:
- Average order value: AOV is a powerful insight as it gives you the entry price point for the majority of your customers.
- Product category: What product categories are most associated with orders? Analyse the products you sell, and promote the ones in those categories that align with your AOV to encourage a first or second purchase.
Step 3: Use this information across different campaigns.
Once you’ve created your segments and established the most pertinent information about their shopping behaviour, identify ways to incorporate this valuable data into your regular campaigns.
There are many different ways to set up your welcome series. If you’re going to include an incentive, set the the value of the discount amount so it meets your AOV. The customer feels happy as they’re getting an introductory offer, while you are able to protect your profit margins. For example, if your AOV after 1 order is £40, create an incentive to give 20% off when you spend £50.
As with the welcome series, use your AOV for two orders to incentivise that second purchase in your post-purchase messaging. You can even take it one step further and break it down based on AOV per product category.
I know I’m more likely to click on an email or take advantage of an incentive if it’s right in line with my typical preferences and regular shopping habits. And with the vast amount of data we’re now able to collect from consumers, it only makes sense to use it. Use it wisely to give shoppers what they want and keep them coming back again and again.