Introduction
You can find the Purchase Patterns Dashboard here.
This dashboard provides insight into customer purchasing behaviour. It is an essential tool for understanding how your customers engage with your store over time, starting from their very first purchase through to their subsequent repeat purchases.
It can be used to craft effective retention strategies, increase average order value (AOV), and maximize customer lifetime value (LTV).
Quickstart Guide (or just watch this video!)
The dashboard consists of a table with 5 columns, representing your customers' 1st, 2nd, 3rd, 4th and 5th orders. The columns are made up of several individual cards, with each card representing either a product, a category, or a brand depending on your setup (read more about dashboard setup in the below Customizing the Dashboard section).
The first column shows what products are popular with new customers. The second column shows what's popular with customers returning for a second time, the 3rd column represents customers returning for the third time etc.
Click on any product to find out more about these customers, including what they bought at the same time, and what they went on to purchase later.
Customizing the Dashboard
The core dashboard functions are highlighted in the below screenshot, and described in more detail underneath.
(Click on the above image to enlarge it)
Date Filter: This filters the dashboard to customers acquired within the selected date range. For example, if January 2024 is selected, the dashboard displays the overall purchase patterns of customers who placed their first order in January 2024. Remember that their subsequent orders could have been placed at any time since then. In other words, the date filter selects a group of customers based on their acquisition date, and then looks at everything they've purchased since then, regardless of when they purchased it. For this reason, if you pick a recent date range, the 2nd-5th order columns may be blank, as customers acquired more recently have had less time to place more orders with you. These additional columns are more likely to populate when you select a date range further in the past.
Additional Filters: Click on + Add Dimension Filter to filter the entire dashboard to a specific Product SKU, Name, Category, or Brand.
Saving a View: Click on Current View: Unsaved to open the view saving menu. Read more about saving views here.
Granularity Dropdown: This dropdown controls what each card represents. It defaults to Product Name, which displays the individual products that are popular with your customers at each stage of their lifecycle. It can be changed to Product Category or Product Brand, to show which categories or brands are popular with your customers instead. The option that works best for you will depend on how many products are in your catalogue, how many different categories you sell in, and whether you sell own-branded or third-party goods.
Sort Metric Dropdown: This determines what order the cards in each column appear in. You can choose to sort the cards by Unique Customers, Gross Revenue, or Order Contribution.
Number of Products Setting: This setting has two parts: the Top/Bottom dropdown, and the number textbox. These settings determine the direction of the sorting (i.e. highest to lowest or lowest to highest), and how many cards are shown in each column. For example, setting the Sort Metric to Unique Customers and the Number of Products to Top 5 will show the 5 most popular products in each column. If Number of Products was set to Bottom 5 instead, we'd see the 5 least popular products in each column.
Customize Button: This button allows you to choose which metrics appear on each card, and what order they appear in.
Reading the Dashboard
Below is a more in-depth breakdown of how to read and interpret the information displayed on the dashboard. Let's assume the dropdowns and settings have been configured like this:
Date Range: This Year (YTD)
Filters: None
Granularity: Product
Sort Metric: Unique Customers
Number of Products: Top 10
(Click on the above image to enlarge it)
1st Order Column
This tells us which 10 products acquired the most new customers this year. In our example the Rope and Ball Toy was the most popular, followed by the Tiger, and then the Elephant.
The 1st Order column will always display (100% of Unique Customers) at the top, as every customer has to place at least one order to become a customer.
The percentages that appear on each product describe the % of new customers who bought that product. e.g. in the below we see that 62.5% of new customers bought the Rope and Ball Toy, 20.0% of new customers bought the Tiger Toy, and 8.3% of new customers bought the Elephant Toy. This gives insight into which products are the best at acquiring new customers.
Bear in mind that the 62.5%, 20.0%, and 8.3% might overlap with each other. For example, a customer who purchased both the Rope and Ball Toy and the Tiger Toy will be contributing to both percentages.
We can examine this overlap by clicking on one of the products.
Clicking on a product within the Purchase Patterns Dashboard filters the group of customers to those who purchased that product at that point in time.
For example, by clicking on the Rope and Ball Toy, the numbers on every other card change, as now we're not looking at every customer acquired this year, but at every customer acquired this year who bought the Rope and Ball Toy in their first order.
Now we can see that of the 1.34K people who bought the Rope and Ball Toy, 52 (or 3.9%) of them bought the Tiger in the same order. Similarly, 26 (or 1.9%) of them bought the Elephant in the same order.
In this way, the Purchase Patterns dashboard is similar to a Basket Analysis, as we gain insight into what items are purchased together within the same order. These insights can be used to inform which products to recommend as cart additions, or which products could work well together within a bundle.
You'll notice the 2nd, 3rd, 4th, and 5th columns update too when we click on a product in the 1st column. These columns also get filtered to the customers who've bought our chosen product in their 1st order, so we gain insight into what these customers went on to purchase in their subsequent orders.
In our example we see that 2.7% of the people who bought the Rope and Ball Toy in their first order went on to buy it again in their 2nd order, while 0.3% of them bought the Giraffe in their second order. Again, these groups of customers might overlap as due to customers possibly buying more than one different product within their second order.
We can check out the overlap by clicking on the Rope and Ball Toy in the second column:
Now we have a view into what these customers 1st Order baskets looked like, and what their second order baskets look like.
Note that now the 3rd Order column is filtered even further: now we're only looking at the customers who bought the Rope and Ball in their first order and in their second order. We can see that 3 of these customers came back to purchase for a third time, and they all bought the Rope and Ball Toy.
Why this Matters
The Purchase Patterns Dashboard is a powerful tool for CRM teams, enabling them to:
Identify products with the highest likelihood of retaining customers.
Target specific groups of customers with tailored retention strategies based on their purchasing behaviour.
Proactively recommend products proven to drive repeat purchases.
Additional Use Cases
Marketing Optimization
Recognize products that drive first-time purchases or foster loyalty.
Enable targeted promotions and campaigns for customer acquisition and retention.
Merchandising Optimization
Identify products frequently purchased together to inform product recommendations and bundles.
Suggest complementary items to increase cart value and improve the shopping experience.
Sales Planning
Pinpoint top-performing products for prioritization in sales strategies or bundling opportunities.
Highlight underperforming products that may need marketing support or repositioning.
Measuring the Impact
The beauty of making data-driven decisions is that the impact of these decisions can be measured using data as well.
When it comes to making decisions based on the Purchase Patterns Dashboard, keep an eye on your overall AOV and LTV:
Average Order Value (AOV) can be maximized by identifying and suggesting products frequently bought together, either as suggested cart additions or as product bundles.
Customer Lifetime Value (LTV) can be enhanced by targeting customers with products they are likely to purchase as their second, third, fourth, or fifth orders.