Customer Brand Loyalty

For example, if your customers tend to try a few different brands when they first start shopping with you - at what point do they settle on a specific one?  Are there similar brands competing for the same dollars?  Do your customers try more expensive brands and stick with them?  Do they purchase more often from one brand to another?  The graphics are set up to look at a single brand or split between several that you might want to compare.

You will notice that the graphics begin with month "0".  This represents the month when the initial purchase was made, followed with the months numbered since that initial purchase. Using the Initial Purchase Date filter, you are able to focus the dashboard on specific time frames - for example, you might have started selling a new brand and want to see how your customers react to the new brand and also compare it with other similar brands.  By setting the initial Purchase Date filter to the date when you started selling the new brand, you can follow customer behavior from their first purchase forward - comparing all customer journeys no matter the dates on the calendar when they shopped.

Using a specific Initial Purchase Date (for example, January of this year) allows you to follow a defined group of customers, because Purchase "0" represents the customers making their initial purchase in January of this year - and the months following will represent that same group of customers because you have isolated them with this filter.

Leaving the Initial Purchase Date filter turned off (or set to "Include All") will cause the dashboard to count each customer at month "0" for the month when they started shopping. For example, a customer who starts shopping in January and a customer who starts shopping in April will both be counted at month "0" and their habits in subsequent months will follow along. It is important to understand this in contrast to more traditional calendar thinking.

It is also 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 "initial purchase" 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.

You will see a link to this document in the top of the dashboard, under the heading Dashboard Help.

On the right side of the dashboard you will use the filters to select the brands that you want to study, a specific product type if you want to focus narrowly, initial purchase date(s) if desired and choose whether the sales are web based, store based or both.  In order to understand overall customer behavior, which is the goal of this dashboard, filtering to specific locations is not made available here.

KPIs

At the top of the dashboard are KPIs (Key Performance Indicators) that show the monthly customer sales $, average number of items purchased,  the monthly purchase frequency, and the percent of customers buying each month. Remember that each of these is affected by the dashboard filters - if for example you choose to filter on Initial Purchase Date of January of this year, the KPIs will reflect the activity of customers who began shopping with you in January of this year.  Likewise, as you filter to specific brands and product types, the KPIs will reflect that.

Use the KPIs to understand overall the volume and shopping behavior of the brands and product types that you've selected. From there you can roll through the dashboard to understand how those behaviors compare among different brands and product types.

You might want to look at your entire customer base (without selecting an Initial Purchase Date) or a specific group of customers who started purchasing when you offered a special incentive. By toggling this filter for all customer vs. those starting to shop on a specific date, the KPIs can help you understand which of the metrics deserve a closer  look. You can do this by choosing a specific date range on the Initial Purchase Date filter, then use the on / off toggle to quickly switch the view between all customers and those who joined you during a specific time period.

Suppose you are measuring how well an introductory offer in October performed as compared with how your customers typically shop. You can set the Initial Purchase Date filter to October and see the KPIs for the group of customers who first purchased in that month.  Then you can toggle off the Initial Purchase Date filter to include all customer, regardless of when they started shopping with you, and compare the KPIs of that group. Naturally you would want to understand the goals you set when you made the introductory offer in the first place - and you can start to measure the results here.

Graphics

The set of graphics will show you four different metrics, where you can compare them among certain brands and product types.  For example, you may have a couple of top competing brands that you want to analyze. Do your customers have similar buying habits for each? Do they tend to purchase one brand more frequently than the other? What about sales dollars - there could be different price points for sure, look at the patterns through the customer journey though - are they similar or does one brand tend to drop off after a number of months has passed? Look closely at the percentage of customers buying each month - first at the initial purchase month then the following months - at what point do the customer purchases level, at what point do they tend to rise or fall?

Customers may try a brand when they first start shopping with you, and stick with it - or they may try one & decide it is not the best product for them - you'll be able to see that in the graphics. Remember that you are looking at your customer base, as you have filtered it here. After they start shopping with you, customers may shift their habits for a number of reasons - if you see this in the graphics, look to the corresponding sales dollars - are they increasing, decreasing or staying level? Look to the trend of items purchased - is there a rise, suggesting satisfaction with the brand and maybe some new items added to the cart, or a decline, suggesting a shift away from certain items? Are you able to discover a corresponding shift with a competing brand? You will want to understand all aspects of the behavior before drawing conclusions.

In this example, you will see two different brands of the same product type compared - the filters were used to select the specific brands and product type. If you hover over the points in the graphics, you will see a tool tip that displays the value of each point. 

The Blue brand shows significantly more in sales dollars (top left graphic) with a healthy lift in the first few purchase months; this suggests that after trying the product in the first purchase, customers were satisfied and as a result increased their spending noticeably over the next few months. You'll notice lower dollar values, with a similar trend line for Green brand; this suggests that Green brand might have a lower price point overall, yet still a loyal following of customers. Blue brand also shows a higher volume of items purchased each month (top right graphic), and both brands show a nice lift after the first initial month suggesting initial satisfaction sufficient to add an item or two in the following month.

The average purchase frequency for Blue brand (bottom left graphic) is higher and remarkably similar in trend to Green brand; this suggests that the product is a consumable (hint: it is, in this case) and as expected it also translates to higher or lower sales dollars (top right graphic) in the months where the purchase frequency was moving up & down. The percentage of customers buying each month (bottom right graphic) for Blue brand does have a slight decline in the trend, yet when compared with Green brand it reflects a customer following that is relatively steady.  In this bottom right graphic, where you notice a sudden or steady decline, you'll want to dig deeper to understand the reasons fewer customers are purchasing this particular brand / product type - remember that the trend is based on the months of each customer's shopping journey rather than a calendar based trend.

There are of course many factors that affect customer buying, and no single set of metrics can tell the entire story. You are able to draw conclusions, or assumptions looking at this dashboard - and then combine it with the insights from other dashboard metrics. In this example, you might look to other sales based reporting for specific product types & items if you need to take a deeper dive.

Look at the same graphics for customers who first started shopping about six months ago (by setting the Initial Purchase Date to a specific month).  Using this filter narrows your focus to a specific set of customers, the ones who started shopping in the month you filtered. The trends look quite different - this is how you might analyze the effectiveness of an introductory offer (for example, an offer for new customers only), or the introduction of a new brand into your inventory. In this case, you see data that is a shorter time span than the example above, and you can see the initial impact of a recent initiative. In this set of graphics you'll notice that Blue brand and Green brand have a very different sales dollar trend as compared, than in the first example where we looked at all customers; this is something you would want to track over time, and understand what in your operation might be influencing the behavior to be different from what you saw in the first example.

The final section of the dashboard allows you to see data tables of these metrics in a cross sectional format. See each different set of data by clicking on the heading at the top:

Each data table lists the Month of Initial Purchase on the left side and the months since that date in the columns.  Here you can see which direction your metrics are moving both as customers join you and as their shopping history matures. You might for example, see that in the first month (0), the average # of days between purchases has decreased for more recent customer acquisitions - or that this metric stabilizes after a number months of shopping. You might see that for some brands or product types, customers who joined a year ago have very different habits than those who joined six months ago.

Use the filters on the dashboard to narrow the focus of these data tables. You are able to export the data to CSV or Excel to compare several brands against each other, for example - or you can simply toggle the filters to view the data on screen.

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.