We Can't Acquire Customers Anymore

The analysis indicated problems prior to COVID:

• New + Reactivated Buyers in 2019 = 206,593.
• New + Reactivated Buyers in 2018 = 247,194.
• New + Reactivated Buyers in 2017 = 277,550.
• New + Reactivated Buyers in 2016 = 316,902.
• New + Reactivated Buyers in 2015 = 334,441.

COVID slowed the problem, allowing this brand to generate 201,006 new + reactivated buyers.

But since February 2021, quantities are once again in free fall.

When I shared the data with the Executive, the Executive said something interesting:

• "We just can't pay for new customers anymore. The math doesn't work. Paid Search, our primary source of new buyers, is imploding with fewer conversions and more cost. We honestly just don't know what to do anymore. It's exasperating."

COVID masked underlying business challenges. When you force people to sit at home, e-commerce businesses thrive.

The past fifteen years showed us what the future is going to look like.

• 2006 - 2011 = Catalogers lost significant sales volume to e-commerce brands.
• 2014 - 2019 = Retailers lost significant sales volume to Amazon / e-commerce brands.
• 2022+ = Stale e-commerce brands are about to lose sales volume to Amazon, to modern e-commerce brands that cater to younger customers, to Shopify-based brands, and to newer Retail brands moving into abandoned retail centers.

The first place you see negative trends happening is with new+reactivated buyers. Your most loyal customers hang in there with you ... but prospect audiences don't have a history with you ... they just don't care.

So please, monitor your numbers, and as we head into 2022 adjust for your COVID-bump and then analyze underlying trends. If you are struggling with new+reactivated buyers after accounting for the COVID-bump, you need to take action with your customer acquisition programs now in an effort to avoid what the Executive (above) is dealing with.

P.S.: For catalog brands, this means getting away from co-ops.

P.P.S.: For many of us, this is going to mean finding paths outside of Google.

Podcast

I recently appeared on the Ecommerce Influence Podcast ... click here to listen to the discussion!!!!

P.S.: This e-commerce story is reasonably compelling (click here).

Increase Unsubscribes

Marketers live in a constant state of fear. If customers/prospects unsubscribe from your 6x/week email blasts that always offer 40% off or 50% off or 60% off, somebody gets mad ... "WHAT DID YOU SCREW UP TO CAUSE THAT? FIX IT!"

A few weeks ago I wrote a post telling you that the Salad Days were over (click here). We're in a spot now where the COVID-bump is winding down for many(most) e-commerce brands. We aren't going back to normal, but we are moving on to something new.

The next morning I check my email and here's what I observe:

• UNSUBSCRIBE
• UNSUBSCRIBE
• UNSUBSCRIBE
• UNSUBSCRIBE

I had more unsubscribes from that post ... a post telling you what is coming and telling you to think mathematically about the challenge and what it means to your business ... than any other post this year. People were so offended they said "THAT'S IT, I'M OUTTA HERE!"

Good.

Good riddance.

Our community doesn't need people who are offended by the prospect of a sales decline caused by the resumption of civilization following a global pandemic.

And you don't need to worry if somebody doesn't want to receive your 6x/week missives about the promotions you are offering. Let them go. Let them buy from Macy's or any other brand that doesn't want to be profitable.

You don't want large quantities of subscribers. 

You want the right subscribers.

You already know this. Your Customer Development Executive constantly harps on what the ideal customer is, right?

So don't worry when some of your customers walk away. 

Worry when the ideal customer walks away.

Customer Development: Putting All Pricing Factors Together

We built three relationships:

If we multiply all three factors together, we get a picture of how much a customer is worth. If rebuy rates are lower and orders per year are lower and items per order are lower but the price of an item is higher, the customer can potentially be worth more.

Pretend that prices currently average $9.00 per item.

• Fitted Rebuy Rate is 20.4%.
• Fitted Orders per Year is 1.594.
• Fitted Items per Order is 5.315.
• Calculated AOV = $47.84.
• Next Year Sales = $9.00 * 0.204 * 1.594 * 5.315 = $9.73.

Pretend that you are forced to raise prices to an average of $10.00 per item.

• Fitted Rebuy Rate is 19.7%.
• Fitted Orders per Year is 1.576.
• Fitted Items per Order is 5.054.
• Calculated AOV = $50.54.
• Next Year Sales = $9.00 * 0.204 * 1.594 * 5.315 = $9.97.

In our example, net sales per twelve-month buyer are forecasted to increase (from $9.73 to $9.97). Net sales increase. Now, if cost of goods increased more than the $0.24 gain, then the net result is negative.

But that's how I put the pricing/forecasting work together for you in a Customer Development project.

Contact me now (kevinh@minethatdata) for your own personalized Customer Development project.

Customer Development: Price Increases and Items per Order

Recall the first two quantified relationships.

What does the relationship look like for Price vs. Items per Order.

As prices increase, customers purchase fewer items per order (#duh).

In this example (using actual data), we see three trends associated with increased prices.

1. Customers Are Less Likely To Repurchase Again.
2. Customers Purchase Fewer Times Per Year.
3. Customers Purchase Fewer Items Per Order.

Is this bad?

Not necessarily.

The key is to multiply the relationship out across metrics ... if the net result is an increase in gross margin dollars, the short-term is protected.

The long-term, however, is undoubtedly hurt. You'll have fewer customers long-term, and that means you'll most likely have a smaller business than you otherwise would have.

Customer Development: Price vs. Annual Orders per Buyer

Last time we talked, I shared with you a fitted relationship between price and rebuy rates:

I can generate a comparable relationship between price changes and annual orders per buyer. Do increased prices cause customers to purchase fewer times per year?

The answer is "yes".

For the brand we're studying, we have learned that higher prices lead to lower annual repurchase rates (inferring that you'd also see fewer new customers), and we have learned that higher prices lead to fewer orders per year.

This doesn't mean that higher prices are "bad". It means that higher prices changes how Customers Develop.

Next up? We'll look at items per order.

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.

P.S.: You are probably a pickleball maven, or you wouldn't be reading this, #amirite? If so, you'll want to watch this best-of-five slugfest from Atlanta ... what some are calling the greatest pickleball match of all time.

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.

P.S.: If you want to follow where a drop of rain ultimately ends up, click here. I'm fond of following anything in the Snake River.

They're Using a Fax Machine?

Not only are customers using a Fax Machine ... it's the BEST CUSTOMERS (in this case) who are using a fax machine!

The top row is really, really green ... 26% of weighted historical demand from the very best customers (in this case) comes from customers who fax orders in to this brand.

Yeah, a fax machine!

Turns out (in this case) that those are businesses that have recurring orders coming in via fax. And those, once again, are the best customers this brand has.

Kinda makes you think about what you could do to Develop those Customers, right?

P.S.: I mentioned you'd get non-traditional content during the summer. Click here for this singing bird ... and subscribe to get all of the bird's antics.

Share of Merchandise Sales by Customer Quality

Here is a variant of the Time Lapse Analysis we talked about a few weeks ago. In this situation, I measured the percentage of sales generated by a Merchandise Category based on weighted historical spend (recent sales weighted 100%, sales three years ago weighted 20%).

That's a gorgeous image!

The best customers (top rows of the image) buy from this Merchandise Category at lower rates (about 2.7%) than lower-quality / less recent customers (about 6.0%).

When focusing on Customer Development, you focus on giving customers what they want. Best customers (in this case) are about half as likely to spend money on this Merchandise Category than are customers who are of low quality (about 30-36 months of recency in this case).

P.S.:  If you like maps, you'll like these maps!! And after clicking on the link, you'll be exposed to weeks of ads courtesy of the unsophisticated retargeting industry.

No Paper To Mail Catalogs? You Need QuickScores

Yeah, here's a shameless promotion. If you think you're entitled to free content, skip the rest of this post and click on this article here (read the last two paragraphs, both are relevant for opposite reasons).

Catalogers are being told there's not enough paper available to ... wait for it ... wait for it ... mail catalogs!

If you are in a situation where you are worried there won't be enough paper, you'll need what I call "QuickScores" to filter out the online buyers who don't need catalogs anymore.

I've been using this methodology for twenty years ... it's a comparable methodology to one my team used at Nordstrom to determine that we didn't even need to mail catalogs anymore (saving $36,000,000 in ad cost per year).

For just $11,000, you get the following:

• A Catalog QuickScore that tells you who is purely responsive to catalog mailings.
• A Brand QuickScore that tells you who is responsive to your brand.
• An Email QuickScore that tells you who is responsive to email marketing ... STRONG HINT - THIS BECOMES CRITICALLY IMPORTANT IF THERE ISN'T ENOUGH PAPER AVAILABLE OUT THERE.
• Merchandise QuickScores - you learn the Primary Merchandise Category the customer loves and the Secondary Merchandise Category the customer likes.

Click here for more details.

If your vendor isn't already producing Catalog, Brand, Email, and Merchandise QuickScores for you, it's time to find a new vendor. Or, if you love your vendor, I'll quote you a fee to teach your vendor how to execute the catalog portion of the QuickScore. Yes, I'll teach my competitors how to do what I do ... and they'll make a killing off of the logic. Either you or your vendor (or both) will pay the training fee. Tell me how many partners you have who are willing to do that?

Contact me now (kevinh@minethatdata.com) ... you need to get these scores in your database by the end of July if you want to impact Q4.

Incrementality of the New Store

Often the online brand opens a store and the store actually works.

Here's an example where the new store cannibalizes online sales and still works.

When the store opened, here were sales within the zip code where the store opened (rolling twelve month basis).

• Direct = $159,706.
• Retail = $4,864.
• Total = $164,570.

Today (about seventeen months later), here's what we see (pre-COVID example):

• Direct = $135,477.
• Retail = $87,877.
• Total = $223,354.

By subtracting the change in sales over time, we can identify the incremental impact of the new store.

• Direct = ($24,230).
• Retail = $83,013.
• Total = $58,783.

On paper, it looks like the new store generates $83,013 in sales. But the Direct (online + call center) Channel generates $24,230 LESS in annual sales. This means the new store is truly generating $58,783.

How much of the sales of the new store are incremental?

• 58,783 / 83,013 = 71%.

In other words, whatever you "think" is actually happening at the new store is wrong. Instead, 71% of the sales at the new store are incremental, while 29% of the sales are cannibalized from the online channel (in this example).

Is the new store performing at an acceptable level?

Maybe.

Work with your CFO and run a zip-code level p&l. If (in this example) the new store generates 71% incremental sales and 29% sales cannibalized from the online channel AND the resulting sales ($58,783) are profitable, by all means open more stores.

But if discounting sales by 29% results in an unprofitable outcome, then realize that your VC-inspired, pundit-endorsed strategy isn't working. That's how you want to think about this issue.

It's common for about 30% of the apparent sales of the new store to be cannibalized from your online channel. If you discount the sales you forecast for the new store by 30% and the result is still profitable, then by all means, open the store and consider more stores.

Does that make sense?

P.S.: I said there will be non-traditional content this summer. If you like hummus, you'll like this recipe (click here).