Customer Development: Price vs. Rebuy Rate

As prices increase, annual repurchase rates decrease. For the Merchandise Categories at an actual brand, here's what the relationship looked like.

At a price point of $9.00 a unit, rebuy rates averaged around 20.7%.

At a price point of $10.00 a unit, rebuy rates averaged around19.5%.

The red line represents the fitted relationship (power function for those of you who are interested).

As prices increase, repurchase/rebuy rates decrease.

This is going to be an issue should inflation begin to creep into our business models.

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.

Who Embraces Discounted Merchandise?

Many customers embrace discounted merchandise, but the preference is not universal or uniform.

In this Time Lapse Analysis, green represents segments of customers at points of time spending above-average amounts of money on discounted merchandise. Red represents a skew toward full-priced merchandise.

About twenty months ago the "middle" of the file was thriving because of discounts/promotions.

In the last three months the very best corner of the customer file (and the very bottom of the customer file) are generally buying because of full-priced merchandise.

If you have to liquidate merchandise, make sure you know "who" is most likely to buy the stuff. Develop the Customers who specifically want to be developed via liquidations/discounted-merchandise, ok?

P.S.:  If you see birds flying overhead, you might think it's a random occurrence. Maybe. It could also be migratory behavior. Click here to see is you are under the flight path of a major migratory event.

The End of the Salad Days

An awful lot of e-commerce brands are about to experience a trend not experienced since before COVID started.

It's the end of the Salad Days.

Let's walk through a very simple example. Pretend that your brand had a 30% rebuy rate, had 100 customers to start 2019 and acquired 70 new/reactivated buyers during 2019. Pretend each existing buyer spent $150 and each new/reactivated buyer spent $100, annually.

Here's what the old business looked like:

• Existing Buyers = 100 * 0.30 Rebuy Rate = 30 existing buyers @ $150 each.
• New/Reactivated Buyers = 70 @ $100 each.
• Total Net Sales = 30*150 + 70*100 = $11,500.
• Total Year-End Buyers = 30+70 = 100.

During COVID, let's assume you had a 35% rebuy rate and let's assume that instead of acquiring 70 new/reactivated buyers you acquire 125 new/reactivated buyers.

Here's what your COVID business looks like:

• Existing Buyers = 100 * 0.35 Rebuy Rate = 35 existing buyers @ $150 each.
• New/Reactivated Buyers = 125 @ $100 each.
• Total Net Sales = 35*150 + 125*100 = $17,750.
• Total Year-End Buyers = 35+125 = 155.

Your business is more than 50% larger during the COVID era.

Those days are coming to an end, as you already know from your in-house reporting. So let's pretend that we go back to pre-COVID metrics. Does this mean that sales go back to $11,500 as per our example above?

• Existing Buyers = 155 * 0.30 Rebuy Rate = 47 existing buyers @ $150 each.
• New/Reactivated Buyers = 70 @ $100 each.
• Total Net Sales = 47*150 + 70*100 = $14,050.
• Total Year-End Buyers = 47+70 = 117.

Sales don't go back to $11,500, but they don't stay at $17,750. Sales trend "somewhere in-between" at $14,050.

Let's move ahead another year.

• Existing Buyers = 117 * 0.30 Rebuy Rate = 35 existing buyers @ $150 each.
• New/Reactivated Buyers = 70 @ $100 each.
• Total Net Sales = 35*150 + 70*100 = $12,250.
• Total Year-End Buyers = 35+70 = 105.

After two years the bump is virtually gone.

Let's move ahead to a third year.

• Existing Buyers = 105 * 0.30 Rebuy Rate = 32 existing buyers @ $150 each.
• New/Reactivated Buyers = 70 @ $100 each.
• Total Net Sales = 32*150 + 70*100 = $11,800.
• Total Year-End Buyers = 32+70 = 102.

I'll recap annual net sales in our example:

• 1-2 Years Ago = $11,500.
• COVID Year = $17,750.
• Next Year = $14,050.
• 2 Years Ahead = $12,250.
• 3 Years Ahead = $11,800.

The COVID era ... the Salad Days, they're going to end.

However, even with comparable customer productivity, you'll get a sales bump because of all of those customers you acquired during COVID. Those customers will pay you compound interest for a few years.

But ... but ... eventually your business goes right back to where it was. With the same customer dynamics as before COVID the trend is inevitable.

So this leads me to a handful of questions.

1. Have you measured this dynamic? If not, email me (kevinh@minethatdata.com) and I'll get it measured for you.
2. Have you planned your inventory metrics around this concept? If not, now is a good time to adjust your plans.
3. Unless you fixed core issues with your business, your business is going to slowly lope back to where it was during Calendar 2019. The trend is unavoidable.

Questions?

Customer Development Project Example

Many of you have emailed me wondering "What does a Customer Development project writeup look like?"

Well, today is your lucky day!

Click here for details. On the landing page you will find a link to a .pdf file that contains a writeup.

After you read the writeup, contact me (kevinh@minethatdata.com / 206-853-8278) for your own Customer Development project. You are going to need to know what the future holds for your brand now that you have all of these customers acquired during the COVID-era.

Changes in New Merchandise Adoption Across the 24-Month File

In this Time Lapse Analysis, we look at the percentage of weighted historical spend in New Merchandise. We have twenty-four month of weighted customer history, and we look back across the past twenty-four months. It's "go time".

Yeah, I circled two areas for your amusement.

18-24 months ago new merchandise was a smaller fraction of weighted customer spend. Interestingly, the best customers (the top rows in the Time Lapse Analysis) were least likely to buy new merchandise. The best customers (remember how those customers faxed orders in and bought via the phone) like what they like, and they don't like being swayed by merchants offering tempting new items.

Now look at the circled area on the right side of the analysis. The middle of the twenty-four month customer file was most likely to embrace new merchandise when the assortment shifted. Yes, all customers shifted - but those in the middle of the file were willing to take a chance on new merchandise.

So often we see Executive Teams who bring in a new merchandising team. These poor souls are tasked with "fixing the assortment". Oh boy. The minute they try to fix things, the most loyal customers rebel ... they reject ... they refuse what is being offered.

As a Customer Development expert, be sure to know "who" is most likely to buy the new stuff your merchandising team is pushing on customers. Target those customers first - then take risks among customers less likely to embrace the assortment.

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They're Phoning It In!

Here's an example of a Time Lapse Analysis of share of orders via the phone. Tell me what you observe:

Phone orders are a phenomenon among catalog brands. Customers thumb through the catalog and then call the contact center, where somebody guides the customer through the ordering process. Yeah, I know, you're busy taking 85% of your orders via a mobile device ... but in the catalog world there are brands (like this one) where 26% of orders from BEST CUSTOMERS come in via the call center (while 3% of orders from marginal customers come in via the call center).

You read content from pundits and you just shake your head ... the pundits are suggesting that Mabel from Rutland should abandon using her land line and just place the order on her mobile device. Alright.

The trap of the previously successful brand is that the BEST CUSTOMERS are the ones that hold the brand back. They don't want to change. They won't change. Their legacy behaviors and legacy merchandise preferences cause your entire brand to become stuck.

From a Customer Development standpoint, you need to employ two different strategies.

1. One strategy for the old-school customer base who won't change.
2. One strategy for the future of your brand.

Most of your legacy best customers are unlikely to embrace the future of your brand. It's up to you to thread the needle on this topic.

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