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From the Little Stream Software blog

One store optimization pattern that works really well is to find and optimize your metric drop-offs.

First you need to pick a metric you're going to use for this. Most metrics can work as long as you can measure it for a subset of your customers or orders.

Let's pick Average Order Value. It's not my favorite metric for this analysis but it's one you're probably familiar with.

Then we need to pick how we're going to separate the analysis into a sequence. This second value is used to separate the entire data into smaller chunks. In data terms, this is the pivot point.

For AOV the good ones are by date (e.g. AOV by months after first order, AOV by year) or order number.

Let's pick order number, because this will be a more interesting analysis.

Now what you want to do is to calculate the AOV for each order number. In other words, what's the AOV for all customers' first orders? Second orders? Third, etc.

You'll end up with a list like this

  • Order number, AOV
  • 1st $104.15
  • 2nd $157.65
  • 3rd $144.44
  • 4th $146.27
  • 5th $131.16

Now that you have those metrics you can apply the optimization pattern.

First, you want to look at the sequence and identify places where it drops-off significantly. For AOV a drop-off would be a lower number, other metrics might be an increase (e.g. cart abandonment, bounce rate).

In this sample data, going from order #2 to #3 and from #4 to #5 had drop-offs.

With those identified, you'll want to brainstorm and implement tactics for each drop-off individually to improve them.

For #2 to #3, those are customers who have placed a second large order and then bought a third smaller order. I might try out a bonus gift offer for the third order if customers spend more than $160. Or time a special bundle to be emailed to them after their second order that costs $160.

#4 to #5 could be something similar or a completely different tactic. I'd wait to optimize it until you have the earlier one done, you might learn or be able to reuse the tactic.

Once the tactic is running, collect more data and watch how the numbers shift.

If they improve and the drop-off disappears (e.g. 3rd goes to $158+) then move on to the next drop-off.

If they didn't improve, try another tactic.

This pattern ends up cycle of: 1) identify a metric drop-off, 2) test out a fix, 3) repeat with the next drop-off.

You can use this with lots of different metrics and sequences. It all depends on what you're trying to optimize your store for and how your numbers crunch out.

Order sequencing is an under-utilized sequence. Lots of stores use cohorts which is a time-based sequence but order sequences can have a lot clearer behavior for customer loyalty.

Repeat Customer Insights includes both order sequencing and cohort analyses for Shopify stores. Each with multiple metrics you can pick from. It'll simplify the data-collection and measurement process.

It also highlights some of the drop-offs for you automatically which can make this whole pattern much easier to follow.

Eric Davis

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