Customers are all the same, right? Can’t we just pull
metrics from our customer analytics and build strategies using averages? Perhaps.
But what happens when you start digging deeper into the averages? Customers know
they aren’t average and they don’t expect to be treated as such.
Let’s look at how we can unpack metrics based on average
order value (AOV). Before we get into a statistics course, I just want to point
out the difference of average (mean) versus median. A median can eliminate
outliers on the high or low end of your data set since it’s simply the middle
value. However, if you have a shifted bell curve, you can end up with a median
above or below the average. In a perfect world they would be the same number.
Here’s an interesting look at how the AOV compares to the
median order value for a retailer’s customer base. Notice how different the AOV
is from the median in upper revenue deciles. This indicates we have outliers
pushing the average way up, while the values converge to approximately the same
number around deciles 4 to 9.

Now back to customer AOV… Your customers are different,
but they can be grouped by several different similarities. Simply by looking at
AOV by customer decile, you can start to identify shoppers with the highest
likelihood to become your next best customers before they make their second
purchase. In the chart below, you can see how the top three (3) deciles produce
an AOV between \$105-164 which could be used to put an automated campaign in
place to nurture first-time buyers to a second purchase, bolstering your best
customers segment.

Another way to look at AOV would be to track it over time.
By plotting AOV by week, you can spot trends and then break down buyers from
that period to identify trends in products or categories. In the example below,
we can see that the AOV for the year is \$115 but it varies from \$82 to \$149
throughout the year. That’s an 82% jump and when you look at the people who
made their first purchase during that AOV peak; the customer’s average lifetime
value (LTV) based on a first purchase in March/April jumps from \$163 to \$296, another
82% increase. Coincidence? Not really, once you do the math.

Hopefully this gives you a perspective on AOV that you never
considered before. We need to stop thinking about our customers as one big mob
of people. By using a customer analytics platform like Listrak CRM, we can help
show you the secrets that lie within your customer data.

Mike Hartman
Senior Director of Product Strategy, Strategic Planning
Listrak

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