Customer Acquisition & Retention

You will notice that some of 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 made a significant outreach for new customers in January of this year - how many customers did you gain, and how did that compare with previous years? Once those customers started shopping, how did their habits differ from those who started the prior January? Did the new customer influx meet your goals? Gaining new customers is of course great, keeping them is even better.

Leaving the Initial Purchase Date filter turned off 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.

Also note that when the Initial Purchase Date filter is turned off, the "% of Customers Buying" is calculated on the basis of the entire time period.  For example, in the line graphic titled % of Customers Buying each Month, the trend shows per year. The % for January is calculated by dividing the number of new customers in January divided by the number of customers for the whole year. This is because all months are included as "initial months".  Conversely, if Initial Purchase Date is filtered to January of this year, the % of Customers Buying is on the basis of new customers in January of this year - calculated by dividing the number of customers each month who initially purchased in January by the number of new customers in January - this method shows you how many of those customers have been retained each month.

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 "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 heading of the dashboard, click on Dashboard Help to access the document.

On the right side of the dashboard you will use the filter to select the initial purchase date(s) or choose to include all of them in your analysis. In order to understand overall customer behavior, which is the goal of this dashboard, filtering to specific locations is not made available here. Keep in mind that the # of Days between purchases and the calculation of # of months since the initial purchase are made on the basis of all customer activity, without regard to in-store vs. web sales.

KPIs

At the top of the dashboard are KPIs (Key Performance Indicators) that show the # of days between purchases, the monthly purchase frequency, the monthly customer sales $ 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.

Use the KPIs to get an overall idea of the metrics, use the filters to drill into the areas you specifically want to analyze, and then follow to the remainder of the dashboard for trends and insights that you can compare.  Here you might want to look at your entire customer base (without selecting an Initial Purchase Date) to see the average # of days between purchases. Maybe you want to compare that with all customers who started shopping with you this year - has the span between purchase dates increased or decreased?  Are your customers shopping less frequently yet spending more each month? Maybe they are starting to drift away, increasing the number of days between purchases and also spending less - how can you entice them to shop more often?

You might have done a marketing campaign to entice more customers to shop with you - again you can look at the overall metrics, this time maybe monthly sales $. You can then hone in on the customers who joined after the marketing campaign - how does the monthly sales $ from that group compare with the overall amount?  Is it trending in the right direction? Maybe you want to send a targeted survey to that group and offer a discount to survey participants. You'll see more in the data tables later in this document, where you can see where trends started shifting.

Keep in mind that the top line KPIs are a high level average and as you filter to specific groups of customers (sometimes called cohorts) you will likely find the metrics to be more useful.

Graphics

Below the KPIs are graphics that start by showing you how many customers have joined you on a monthly basis, for the past few years. You can see at a glance how the trends have moved throughout each year, and as they compare with the other years. Do you see peaks or valleys at the same time each year? You might be a strong holiday retailer - is that reflected with new customers joining more heavily during the holiday season? Maybe you have not made any special customer drives, yet the trends are similar from year to year - how can you capitalize on months where customers tend to join in larger numbers organically? Maybe your business sees fewer new customers just as winter comes to an end, what efforts can you make to get them shopping just a little sooner?

The legend at the bottom of the graphic shows the years by color - you can turn specific years on & off by clicking on the legend. Turning off different years will often bring the others into clearer focus.

Next to the line graphic is a monthly calendar, you can hover in the date area & use the arrows to scroll through the month. The calendar shades each day based on the number of customers who started shopping on each date; the shading is applied on the basis of the past three years in the view. As you scroll through the months, you might notice that new customers tend to join in higher numbers around your businesses seasonality. Are there other similar trends, maybe weekends?  maybe the beginning or end of each month? You can hover over each date to see how many customers joined each day.

Note that these two graphics do not respond to the Initial Purchase Date filter.

The next set of graphics will show you four different metrics, which can give you great insights when you take the time to look at various customer start dates.  Do this by using the Initial Purchase Date filter, compare the results and understand the potential causes as well as opportunities to retain solid shopping habits.

In this example, you will see that for all customers who joined in the three year period (Initial Purchase Date filter off) the average # days between purchases trends are relatively the same for all three years in the first three months (0, 1 and 2). In the third month since the customer started shopping (3), the  years begin to diverge & the most recent two years have a higher number of days between purchases.  The most recent two years then seem to follow a similar profile.  You'll notice that in the second graphic, average sales, the most recent two years again follow a similar profile - also, on a year over year basis customers are spending less.

Look at the same two graphics for customers who first started shopping in October of each year.  The trends look quite different - the number of days between purchases is very consistent from year to year, and it increases as the customer relationship matures. In this example, the 2018 line stops at month 13 because that is the most recent month for customers who started in October 2018. On the sale graphic, the trends are reversed - you can see that monthly sales are higher for customers who started in October 2018 that they were for customers who started in October 2017.

Ask yourself questions about what was different in your operations. What makes your customers more (or less) loyal? What can you do to ask customers for their loyalty?  Here you can make efforts, try new marketing campaigns and then measure the results as new customers give you their business.

The next two graphics function similarly and  show you the frequency of purchases (how many each month) and the percentage of customers who continue to shop with you. Here let's focus on the second chart, to understand retention rates. This graphic is most useful when the Initial Purchase Date is set to a defined start date each year, or for one specific month / year. In this example you will see that customers who started shopping in January 2019 had a sharp drop in the month (1) following their initial buying month (0).  The trend continued to decline, representing the retention rate (hover at any point to see the percent) of customers who joined you in January.

By adding January 2018 to the filter, you can see that the retention rate for customers joining you in January is relatively consistent, with 2019 slightly lower after two months have passed. You will want to combine this information with what you know about your operation, how you advertised to customers, what incentives you gave them.  You'll also be able to test new methods for customer loyalty and measure them with these metrics.

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 6 months of shopping. You might see that for customer retention, more customers who joined a year ago have continued shopping vs. customers who joined two years ago.

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.