Customer Purchases Overview

You will see the average purchase life of your customers - in other words, the time span between their first and most recent purchases - in some instances, the most recent purchase will be one of many more to come, and in some it will have been the last purchase they will make with you. As somewhat of a balance between retaining and losing customers, you can understand on average how many days have passed since the last purchase.

Use this dashboard in conjunction with the other Customer Purchases dashboards - Customer Product Type Purchases and Customer Brand Purchases are available. Once you have a picture of how often your customers shop with you, and for how long they are likely to keep shopping, you can study your various product lines and brands to determine incentives to improve your bottom line. For example, if your average customer makes 10 purchases, what are they purchasing in the purchases leading up to number 10 - do they need an incentive to continue purchasing a product type?  Look to your top sellers, what happens after that 10th purchase?  Do you see increases or decreases in the metrics?  For your customers who stay longer than the average, which way do the metrics run?  How can you entice the "average" customers to stay around longer?

You already know that customers with accounts are the ones you can reach most effectively - perhaps you can encourage more of your guest shoppers to open an account if say, after 10 purchases you will offer them a special discount or offering.

It is important to note that the data considered begins with the date on the heading panel of the dashboard, under Earliest Transaction Date.  This means that a customer's "purchase #1" is the first purchase on or after that date.  Also be aware that guest purchases are not reflected here, without a customer id purchases cannot be numbered. Customer purchase numbers are calculated on the basis of all customer activity, without regard to in-store vs. web sales; for example a customer who makes their first purchase in a store and their second purchase online will be reflected exactly in those terms regardless of any filtering on store 

You will see a link to this document in the heading of the dashboard, click on Dashboard Help to access the document.

On the right side of the dashboard you will use the filters to select the purchase numbers that you want to review.  You are able to turn this filter off to include all purchases, though you would be including all of the outliers in that scenario.  You may want to take the average purchase frequency number and narrow the purchase filter on that basis - perhaps looking at the first 50 or 100 purchases. You are able to select specific stores and also choose whether the sales are web based, store based or both.

KPIs

At the top of the dashboard are KPIs (Key Performance Indicators) that show some "all purchases" metrics, meaning that they are calculated without regard to the "Purchase #'s Filter".  These will give you a basis of comparison as you study the graphics on the balance of the dashboard. In the first row of KPIs you will see the average total purchase (again, remember the earliest transaction date in the heading panel), average purchase frequency, median customer purchase #, average purchase life and average days since the last purchase.

The average purchase frequency is determined by taking all of the purchases divided by all of the customers - this is important because it represents your entire customer base and tells you that on average this is how many times your customers have shopped with you - customers who purchase only once, and those who purchase in really high numbers affect this average.  The median customer purchase number in simple terms is "the item in the middle".  This means that if you were to line up every single customer purchase number (all the 1', all the 2's, etc.), the one in the middle of that very long list is the median. As an example, consider that every customer will have a "1", many of those same customers will have a "2" and so on... until your customer with the most number of purchases has the only "99999" in the list. Understanding the median value (the middle item in this list) will help you understand how purchase numbers cluster for your customer population.  Here's a simple example: the median of this group of numbers 1,2,3,4,100 is 3 because it is the middle item.  This is different from the average of those numbers, which is 22.  The median value of 3 is much closer to what most of the numbers represent (where they cluster), than say the value of 22 which was affected by the single "100".

Average purchase life will help you understand how long an average shopper has been actively purchasing from you - this of course is affected by how many times in that period they make purchase and what they buy.  Another metric that you will want to watch is how many days have passed since the last purchase - this is an average, so naturally it is affected by customers who may have moved away from you.  How can you entice them to make another purchase - maybe a "we miss you" incentive? This could also be an opportunity to reach out for some customer feedback.

Just below the first set of KPIs is a "box and whisker" graphic. This groups your customer purchase numbers into quadrants, punctuated by the median value that was described earlier.  The "box" shows the clusters of the middle 50% of purchases (in two sections), including a line in the middle of the sections where the median is. The "whiskers" show you the other 50% of the purchases on either side, and will contain any outliers in the numbers.  This graphic will respond to the "Purchase #'s filter" to the right of the dashboard.  You will see in the box sections where the purchase numbers are most clustered, this is a good representation of your group of customers.

Next you will see two more panels of KPIs, the first panel aggregates the range of purchases that you selected in the filter. The second panel shows metrics for individual purchases, and can be scrolled with the arrows to the left & right of the panel. Each of these is of course based on the Earliest Transaction Date in the heading panel & each contemplates all purchases as filtered.

Included in each panel:

Average Total Purchase - on average how much customers are spending on purchases

Average # Types Purchased - on average how many different product types are included

Average # Brands Purchased - on average how many different brands or manufacturers are included

Average Items per Purchase - on average how many different items are included

Average Item Value - on average how much is spent per item

As you compare the different purchases, and range of purchases look for obvious shifts as well as how the metrics compare with your assumptions. Also pay attention to the metrics as the average and median numbers of purchases are reached - how do they differ before and after? Is there a point where your customers seem to settle into a routine? In the second panel, you will see mini line graphics that respond to the "Purchase #'s Filter" and show you each metric's trends over that span.  You will want to look further wherever you see peaks and valleys in these trends to understand causes and how you can affect them.

Graphics

Below the KPIs are graphics that illustrate how your customers are shaping their purchases, both in amounts spent and the frequency, In a scatter chart, these are displayed in high level product groupings to give you a high level view of the "aisles" that most capture your customer dollars. You will see any outliers, perhaps your best selling products really outshine all others - and also where the lower dollar, and lower frequency purchases are happening. Is your product diversity sufficient? Do you have opportunities to rotate some products to the impulse buy areas? How often are your customers purchasing each product grouping? 

In the next graphic customer purchase trends are displayed, and reflect in the column chart a running total by purchase number. You will see a black line that represents the average total purchase (from the KPIs in the earlier metrics). Compare the average total purchase with the running totals as purchases continue. Since you already know from the KPIs the average number of purchases your customer make, you can see here how the missed opportunities compound over time. Using the example of an average customer making 10 purchases, look here to see the missed sales from purchase 11 and more.  How can you convince your customers to make that next purchase? Look to the sloping line that represents the percentage of customers as it compares with the first purchase - can you use this to make goals for your stores? For example, if you see here that 65% of your customers make a second purchase, can you set a goal of 67% and challenge your store teams to really engage with first time customers, perhaps asking when they will see them next? It could seem that a 2% increase is not significant - when you do the math though, and understand the number of customers in that 2% at the average transaction value, those numbers can add up quickly. 

The last set of graphics are best viewed together and will show you your top selling product types and brands as they compare from the first purchase to the group of purchases you select in the filter.  You might want to compare the first purchase with the next nine for example, in keeping with the example of average purchases of 10. You might want to look at a longer stretch to see which product types and brands fall into and out of favor with your customers over time. Each of these graphics show the contribution to sales for your top sellers and the make-up of product types or brands as they reach 100% of your sales for the Top 25. Do your customers buy a larger variety earlier in their experience with you, or do they diversify their carts later?  Do certain product types or brands initially contribute a larger portion and fall out of favor? Which product types or brands perform better as customers get familiar with you?  These changes can be caused by a number of variables including price, merchandising and availability - which do you control?

Any of the data tables can be exported to work with the data outside the dashboard. Do this by clicking on the 3 dots at the top right corner of the widget, choose Download and export an image or file.  The recommended file type for a spreadsheet is CSV.