Crazy Math Outcome That Actually Helps Not-So-Good Marketers

I was taught this concept in my Demography classes in college. Increasingly, the concept appears in ecommerce.

In a recent video conference, the Executive said "we have a long repurchase cycle". The Executive wasn't wrong, of course. However, the Executive didn't understand one of the reasons why the repurchase cycle was long ... virtually no Marketers understand this concept because nobody bothered to teach Marketers the concept.

We'll start with a simple example. Let's pretend you have 1,000 new customers. Pretend in the first year that 30% repurchase, with 10% of those who remain repurchasing in the second year.

• 1st Year Repurchasers:  1,000*0.30 = 300.
• 2nd Year Repurchasers:  (1,000-300)*0.10 = 70.
• Total Repurchasers = 300+70 = 370.

Now, let's pretend this brand has average marketers who do not encourage first-time buyers to repurchase quickly. As a result, only 20% of first-year buyers repurchase, with 10% of those who are left repurchasing in the second year.

• 1st Year Repurchasers:  1,000*0.20 = 200.
• 2nd Year Repurchasers:  (1,000-200)*0.10 = 80.
• Total Repurchasers = 200+80 = 280.

Did you see what happened in Year Two?

• More lapsed buyers purchased in year two for the average marketer.

The average marketer doesn't meet the standard in year one, so there are more lapsed buyers available in year two. Simply doing an average job results in 80 reactivated buyers instead of 70 for the brand that does a good job. The average marketer could do a bad job (reactivating 9% of those buyers instead of 10%) and still end up with more reactivated buyers in year two.

The average marketer will come to a different conclusion as a result of average marketing.

• Smart Marketer converts 300 buyers in year one, 70 buyers in year two, sees the importance of quickly converting customers.
• Average Marketer converts 200 buyers in year one, 80 buyers in year two, sees that the numbers aren't that different, then concludes that because the numbers aren't different there must be a longer repurchase cycle than average, becoming even more complacent.

The math eventually works out well for the Smart Marketer. The Smart Marketer moves 100 additional buyers close to loyal status, where the customer pays the brand back a fortune in profit ... profit the Average Marketer fails to generate.

This "Crazy Math Outcome" is happening all over the place post-COVID. It's happening because Average Marketers are not quickly converting first-time buyers to a second purchase.

Coaching Opportunity

If you're looking for marketing/analytics guidance, I have an opening in my calendar.

Clients who hire me for coaching opportunities ask me to analyze their customer transactions, then participate in weekly video conferences. The topics vary by client, frequently including the following issues:

• Marketing Investment and Subsequent Growth / Profitability.
• Marketing Channel Strengths.
• Future Customer Value by Marketing Channel and Merchandise Category.
• Email Marketing Targeting Tactics.
• Merchandise Productivity / Effectiveness.
• New / Existing Merchandise Evolution and Off-Price Effectiveness.
• Fusion of Targeting and Merchandise.
• Staff Mentoring.

If this is something of interest to you beginning in January, let me know (kevinh@minethatdata.com) - I can structure something that meets your specific needs.

The Missing Ecommerce Middle Class, Part 2

This is how the composition of a business changed over eight years. Across the top we have purchase frequency, the rows represent recency.

The 0-12 month file is smaller (numbers represent change vs. eight years ago), but the 0-12 month file with one (1) historical purchase is larger.

The 13-24 month file grew a tiny amount, mostly from 1x buyers who lapsed (did not become 2x buyers after purchasing for the first time).

The 25-36 month file grew, mostly from 1x buyers lapsing for 2+ years plus 2x and 3x-5x buyers not repurchasing thereby lapsing.

Notice how middle-class buyers shrunk, but in this case loyal buyers (6x+) shrunk even more. This happens when the middle class shrinks, resulting in fewer customers migrating from the middle class to loyal status.

You've heard me repeatedly ask you to work hard to convert first-time buyers to a second purchase (quickly). This is why. Our businesses are shifting, away from loyal buyers (in spite of every possible loyalty confection known to mankind applied to best customers over the past decade). Our businesses are shifting to prospects who have a need at a point in time ... we meet that need, then the customer moves on. Quick ... you recently purchased athletic wear on Amazon, what was the name of the brand you bought, and why didn't you go directly to that brand to buy from them?

I wouldn't have shared this finding with you (twice) this week unless it is something I repeatedly run across, something that I repeatedly observe causing harm. Do not wait until the customer is lost (beyond 13-15 months of recency) to do something, ok? 

A Dumb Mistake

Back in the day, I performed some "fancy math", demonstrating that poor business performance was correlated with the S&P 500. I was so happy!

I shared the results of my brilliant work with my boss ... who was not happy!

• "I'm not paying you to correlate bad business with an economic downturn, I'm paying you to fix bad business performance."

That's a fair statement.

We need to fix things.

"Ecommerce in a box" ... the Shopify / GA4 / ESP / Search / Social / Marketplaces environment gives the illusion of not having to fix things. A co-worker in 1992 once said "Read a step, do a step, get a banana". He was referring to what would become "Ecommerce in a box". We execute, we get a paycheck.

I once had a communication with a professional who noticed that there weren't enough new items ... he said "what do I do?" When I responded "you either figure out how to introduce more new items and/or you learn how to feature the new items you have so that customers buy them at higher rates" the person just went silent.

Here's the thing ... within the "Ecommerce in a box" environment, business can be fixed. Your email service provider would love to implemented targeted streams to customers pre-disposed to buy new merchandise from specific categories. Your search agency would love to implement ideas they've seen work with your competitors, they want to help you. 

So don't make the dumb mistake I made ... don't quantify the problem ... solve the problem!

The Ecommerce Middle Class

Not "middle class" in terms of income. "Middle Class" in terms of value to a client of mine.

If we go back twenty years, your customer file was comprised of a lot of "middle class" customers. These were customers who generally purchased two times. This was a good thing for ecommerce brands, because these customers had a fighting chance of becoming loyal.

Whether it is Amazon taking all the middle class customers away or the Shopify empire encouraging purchases from a million smaller brands, the middle class has generally disappeared in ecommerce.

Twenty years ago, a first-time buyer had a 35% +/- chance of purchasing again in the next year. This accelerated the rate of customers moving to a second purchase, where the customer had a 45% chance of placing a third order. There were many 2x/3x buyers as a consequence, customers in the middle of the customer value bell curve.

When first-time buyers have a <= 25% chance of buying again in the next year, the middle class gets hollowed out. Not nearly enough customers develop to 2x/3x status, which means eventually not enough customers become loyal.

You've heard me talk frequently about "Welcome Programs". These are separate email marketing streams that target customers who just purchased for the first time. The goal of the programs are simple:

• If the customer needs to return something, make it easy for the customer to return something - and then find something else for the customer to purchase.
• Teach the customer about who you are. Get the customer hooked up with Instagram or any of the social media platforms. 
• Based on the merchandise purchased in order #1, cross-shop the customer into the next most likely category that the customer wants to purchase from.
• If you have a loyalty program, get the customer enrolled in the program. 
• Rapidly convert the customer to a second order - get this customer into your "middle class".

How many businesses do an excellent job at quickly converting a first-time buyer to a second order?

• Very few.

I'm convinced few are good at this because few possess the creativity to be good at this. It's one thing to create a beautiful image that you put up on Instagram. It's quite another thing to put together a dozen different triggered email programs based on "what" a customer purchased in a first order. It's not easy for a campaign-centric creative professional to adapt to the reality presented to us in late 2026.

New Customers: Does Quality Matter?

Maybe your data looks like this:

• All Other New Customers:  $20.00 profit in the next year.
• Google Shopping New Customer:  $12 profit in the next year.
• Paid Social (Facebook):  $10 profit in the next year.

When you email me your questions, you'll ask if you should avoid the Google Shopping / Paid Social new customer?

What you should avoid is wasting a lot of money marketing to those new customers. If you can earn a profit acquiring those customers, market to them in the "welcome window" ... the first 90 days after acquiring the customer, then be gentile with your marketing dollars. Don't waste them chasing a customer that is not interested.

I know, this is the point where you suggest it isn't worth acquiring a customer that doesn't want to have a relationship with you. Almost no customers want to have a relationship with any of us. The customer has a need at a point in time. The customer wants that need met.

The quality of a new customer matters in relation to how you manage the downstream relationship. If you can earn $25 profit acquiring the customer and then earn another $10 in the future (whereas a different new customer pays you back $25 / $20), just back off your downstream marketing efforts to that customer.

The average ecommerce brand doesn't have this problem, FYI ... they don't have outbound marketing programs that cost $15/year. This is a decidedly old-school problem.

Old School Geography

I think this goes back to 1991 or 1992. I was asked to explain what was happening to swimwear sales at Lands' End. I mapped sales by state by month.

• In January/February, sales boomed in the Midwest and Northeast.
• In March, sales thrived in the Southeast United States.
• In April, the eastern seaboard performed well.
• By May/June, sales moved back to the Midwest and Northeast.

In other words, sales followed the weather. Vacationers bought merch in the heart of winter. From there, the weather improved most in the south, then southeast, then eastern seaboard, then in the midwest/northeast.

The relationship was consistent. In fact, when I left for Eddie Bauer, the relationship at Eddie Bauer was nearly the same.

But when swimwear business was not great, it was largely a weather issue.

Back in 2005-2006 when we killed off the catalog business model at Nordstrom, it was my job to field angry calls from customers. For about four months, angry 45-75 year old women in Minot and Rapid City and Cheyenne and Bozeman let me hear their frustrations with no longer receiving a catalog. I recall telling my boss where these customers lived and she said "those aren't our core retail customers". The stirrup pants customer in Billings was not the core retail customer at The Galleria outside of Dallas. In fact, when we coded zip codes, those who bought from the catalog skewed disproportionately rural. One of my more successful consulting projects was from 2008 ... "Zip Code Forensics", where I created an algorithm to code zip codes, then showed how certain zip codes aligned with catalog marketing. That project lingered for eight years until ecommerce fully consumed the catalog business model.

A while back a CEO talked about the core customer being "native" to New England. I didn't think that was possible until I analyzed where his customers lived ... sure enough, they were skewed to zip codes starting with a 0 or 1.

There are things to be learned by analyzing business performance by geography.