Customer Development and Inflation

In a recent conversation, the Executive said that a key item that normally sells for $9 will have to sell for $10 this fall, because the cost to get the item here has gone up. The Executive wanted to know what impact "inflation" will have on this item.

While inflation doesn't appear to be an across-the-board issue, it certainly impacts some of us in certain instances.

We have the data to understand what impact inflation "could" have. Each of us have numerous Merchandise Categories. The items in each Merchandise Category tend to be offered at different price points, meaning that the average price point within a Merchandise Category is different than in another Merchandise Category.

Here's what I do in my Customer Development projects. For each Merchandise Category, I measure annual customer repurchase rates, orders per buyer, items per order, and price per item purchased.

Below I illustrate two examples from actual customer behavior within two Merchandise Categories.

Merchandise Category #1

• Rebuy Rate = 18.6%
• Orders per Buyer = 1.548.
• Items per Order = 4.905.
• Price per Item Purchased = $10.13.

Merchandise Category #2

• Rebuy Rate = 13.1%.
• Orders per Buyer = 1.376.
• Items per Order = 4.219.
• Price per Item Purchased = $12.94.

As the price increases from $10.13 to $12.94, rebuy rates decrease from 18.6% to 13.1%.

As the price increases from $10.13 to $12.94, orders per buyer decrease from 1.548 to 1.376.

As the price increases from $10.13 to $12.94, items per order decrease from 4.905 to 4.219.

There are two important concepts to think about here.

1. Does the higher price point result in more overall sales?
2. Does the higher price point result in fewer future customers?

In this case, there is essentially no impact on future sales.

• Category 1 = 0.186 * 1.548 * 4.905 * 10.13 = $76.97.
• Category 2 = 0.131 * 1.376 * 4.219 * 12.94 = $75.12.

Based on the error rate of the methodology, there isn't really a difference in sales. The higher price offsets lower metrics, yielding comparable spend.

However ... however ... rebuy rates are lower (18.6% vs. 13.1%), meaning that there will be fewer active customers next year in category two than in category one.

If ... and this is an "if" ... if this methodology can be applied to theoretical price increases within a category (our example is across categories), we can get a good idea whether we should eat increases in cost of goods sold or whether we should pass the costs on to the customer.

Sound interesting?

Good! We'll discuss this in more detail in the next few weeks.