September 18, 2019

Price Impact Over Time

So this is kind of fun!

If you can run a regression equation for one year, why not run one for each of the past four years? This allows us to compare how customer spend in one price band is impacted over time.

In this comparison, I evaluate a customer spending $200 last year on items only in the $0.01 to $9.99 price band. Over time, customers are becoming less valuable:
  • 2016 Customers = $113.83 next year.
  • 2017 Customers = $106.99 next year.
  • 2018 Customers = $109.50 next year.
  • 2019 Customers = $103.86 next year.
You're seeing the impact of merchandise productivity ... and it's not pretty, is it?

We can run a comparable table for other price points ... how about $50.00 to $74.99?

The relationship is different, isn't it? There are productivity gains in each of the past two years at this price point.

Granted, the customer is less valuable. But the customer is becoming more valuable.

So the key, I suppose, is this:
  • Is the company shifting toward higher price points over time?
  • If so, is the mix away from lower price point items into higher price point items good for customer loyalty?
We'll answer that question tomorrow.

September 17, 2019

Not All Dollars Are Created Equally

It keeps coming up in project work, so something is going on.

It's common to see a merchandising team that is purposely trying to increase prices. When the merchandising team adds new items to the assortment, those items are added at price points that are a bit higher than the items that are being replaced.

Is that a good thing?

From a gross margin standpoint, it can be a good thing.

From a customer standpoint?

In the table above we have a regression equation. I'm measuring how much a customer will spend next year, based on how much a customer spend last year in various price point categories.
  • p010 = Items $0.01 to $9.99.
  • p020 = Items $10.00 to $19.99.
  • etc
The coefficients (under the column labeled "B") represent how much a customer will spend next year (in total) based on how much was spent last year within that price point band.

So when we see a value of 0.537 next to p010, we know that next year the customer will spend $53.70 for every $100 spent last year on items $0.01 to $9.99.

When we see a value of 0.273 next to p150, we know that next year the customer will spend $27.30 for every $100 spent last year on items $100 - $149.

Where are the larger values?
  • Larger values are associated with lower price points.
For the brand studied here (actual data), customers buying from lower price point items (all things being equal) become more loyal than customers buying from higher price point items.

Now, there's a caveat here. Let's look at three different customers.
  • Customer #1 Spent $100 last year on items priced $10.00 - $19.99.
  • Customer #2 Spent $150 last year on items priced $10.00 - $19.99.
  • Customer #3 Spent $150 last year on items priced $50.00 - $74.99.
Using the equation above (including the constant term), we obtain next year's customer value:

  • Customer #1 = $29.36.
  • Customer #2 = $59.91.
  • Customer #3 = $44.61.
Do you see the caveat?
  • If customers are more loyal because they buy from lower price point bands, and you cause customers to spend less in the lower price point bands, future value will be less.
In other words, it's good to get a customer to buy 5 $20 items instead of 1 $100 item. It's not good to shift the customer from 1 $100 item to 3 $20 items.

Not all dollars are created equal. You need balance in the merchandise assortment, skewing toward what the customer wants, offering breadth of assortment to capture gross margin dollars as well. But clearly, you need to skew the assortment toward what the customer is pre-disposed to purchase.

September 16, 2019

Who Is Most Valuable?

Remember our three tables ... for low price point band customers ...

... for average price point band customers ...

... and high price point band customers ...

Let's look at next year's total expectation by customer type.
  • Last Year's Low Price Point Band Customers = $52.01 next year.
  • Last Year's Average Price Point Band Customers = $45.71 next year.
  • Last Year's High Price Point Band Customers = $33.91 next year.
Oh oh.

Remember, this company was consciously moving the merchandise assortment toward high price points. Unfortunately, the customer who buys from the high price point bands spends less in the future.

These are analytical tools that you use before your merchandising team wants to make changes, or before your CEO wants to make changes. You get an idea if the strategy will work before you ever implement the strategy. That's the power of this stuff, right?

September 15, 2019

The High Price Point Customer

For the company we're evaluating, high price point band customers tend to spend the majority of future dollars in high price point bands. Look at the results of our equation.

  • $4.45 next year in low price point bands.
  • $7.24 in average price point bands.
  • $22.23 in high price point bands.
It's a good thing that the high price point band customer stays there ... this protects the integrity of items at those prices. It prevents you from having to discount those items, because you have a loyal customer base willing to buy those items.

Tomorrow, we'll explore who has the most value ... low price point band customers, average price point band customers, or high price point band customers.

September 12, 2019

Will The Average Customer Move Up Or Down??

Here's a customer who buys only from the average price point band ... now look at next year's spend levels.

Expect the customer to spend $12.55 in the low band.

Expect the customer to spend $22.14 in the average band.

Expect the customer to spend $11.03 in the high band.

The average band customer will move up or down ... and that's a good thing, because this customer can be pushed toward more expensive items.

Up next - we review customers who buy from high price point bands.

September 11, 2019

Customers Like The Price Bands They Shop In

Here's actual data ... customer spend is broken down into low / average / high price bands. Then I regress next year's spend in each price band based on last year's spend levels.

Here's what the outcome looks like for customers who spent $100 exclusively in the low price point band last year.
  • Will spend $29.15 next year in low price point bands.
  • Will spend $14.34 next year in average price point bands.
  • Will spend $8.53 next year in high price point bands.
Had the three predictions been nearly "equal" this brand could have had confidence in increasing prices.

But that's not the case ... customers will spend more next year in lower price point bands than in the other bands summed together.

Tomorrow we'll look at the outcome for $100 spend in the average price point band.

Run these analytics for your business, ok? You need to teach your co-workers how customers behave.

September 10, 2019

Sales Are In Decline

Sometimes you have to take a step away from micromanaging conversion rates, don't ya think??

Recall our table ... where we analyze annual sales by low price points, average price points, and high price points?

Well, this company has a problem. Sales are in decline, and have been for each of the past four years.

Did sales decline among high price point items? No! Sales actually increased.

Did sales decline among average price point items? Yes.

Did sales decline among low price point items? Yes, and they decreased fastest in this category.

Let's compare 2019 to 2016:
  • Low price point items = $11 million decrease.
  • Average price point items = $7 million decrease.
  • High price point items = $1.5 million increase.
When there is a marketing problem, you see decreases across the board.

When there is a merchandising problem, you see differences across price point bands ... like we see in the table above.

Clearly, this company is de-emphasizing low price point items and is promoting high price point items. The result? A dying brand.

Keep your analytics simple. You can't see the issue above when trying to micro-manage conversion rates or analyzing e-mail open / click-through rates. And yet, the issue above is what is driving this company into the ground.

September 09, 2019

Simplified Pricing Brands

Take demand from the past year. After weighting by demand, create a frequency distribution of 1/3rd / 1/3rd / 1/3rd price points.

Then create a variable in your database ... low = bottom third of price points, average = middle third of price points, high = high third of price points.

Finally, sum annual demand for the past five years.

Look at the table here ... tell me what you observe. We'll discuss the table tomorrow.

September 08, 2019

Training The Customer To Spend Less

In retail, my client base spent 20 years telling the customer to never visit a store. And guess what? Customers listened!! Malls have been emptied, stores have been closed, and the experts are left staring at the rubble wondering why the failed omnichannel thesis didn't grow sales?!

The same thing happens when you teach a customer to pay less for an item than the item used to sell for. Be it discounts/promotions or actual price cuts, it turns out that customers notice when you do this stuff, and they don't forget ... they like paying less.

Of course, if the customer pays less, you make less, so that's not a good equation if your job is to increase profitability, right?

For every item in your assortment, I calculate whether the customer paid an average or above-average price for the specific item or a below-average price for the item.

Then, I create four variables.
  • September 9, 2017 - September 8, 2018 total demand spent on items at or above the average item selling price.
  • September 9, 2017 - September 8, 2018 total demand spent on items below the average item selling price.
  • September 9, 2018 - September 8, 2019 total demand spent on items at or above the average item selling price.
  • September 9, 2018 - September 8, 2019 total demand spent on items below the average item selling price.
With the four variables, I run two regression models.
  • Next Year's Above-Average Volume based on Last Year's Above Average and Last Year's Below Average spend.
  • Next Year's Below-Average Volume based on Last Year's Above Average and Last Year's Below Average spend.
The two regression equations allow us to determine how much a customer will spend next year based on actions of the past year. Fair enough? Let's look at the results. Here is the model for next year's items sold below their average historical selling price.

For every $100 spent on items below average last year, the customer can be expected to spend $26.10 on items below average next year.

For every $100 spent on items above average last year, the customer can be expected to spend $13.40 on items below average next year.

So clearly, there is some affinity for customers who bought below-average priced items last year to continue to do it again next year.

The big question is this ... what will customers who spent money on below-average items prices last year do when presented with above-average item prices next year? Here's the equation (yes, this is actual customer data being presented to you).

For every $100 spent on items below average last year, the customer can be expected to spend $24.10 on items above average next year.

For every $100 spent on items above average last year, the customer can be expected to spend $31.40 on items above average next year.

So this is where things get interesting.

Let's pretend that a customer spent $100 on above-average priced items last year. The image below depicts what we can expect from this customer in the next year:

We expect the customer to spend $12.18 on below-average-priced items and $29.88 on above-average-priced items, for a total of $42.06.

Say instead you offered the customer 20% off ... and let's assume that the customer spent MORE because of the generous discount, spending $100 regardless. What do the equations tell us about this customer?

The customer will actually spend more ... $47.46 instead of $42.06. But look at the reduction in spend on above-average-priced items ... we go from $29.88 down to $22.58 ... a 24% reduction.

Let's pretend that gross margins are 50% for above-average-priced items and 40% for below-average-priced items. Last year's above-average-priced customer would generate $19.81 while last year's below-average-priced customer would generate $21.24. You'd actually enjoy an increase in gross margin dollars. How about that??

But what if you offered 40% off ... so that gross margins are 50% for above-average-priced items and are just 20% for below-average-priced items? Now you are comparing $19.81 to $16.26 ... you've harmed the business.

The key, then, is to find the right balance ... let the equations above drive what the optimal level of discounting and price adjustments should be.

Make sense?

P.S.:  These are the type of posts that garner criticism from the stat/math/data-science folks. They'll criticize that the methodology is too simple, or doesn't take into account the "right" factors. Well, the ones criticizing are right. But how am I supposed to teach the concepts from a machine learning algorithm factoring in 29 different variables? Here, it's easy to see the outcome, right? Sometimes you go simple so that you can teach the concepts. I hope that's an acceptable outcome. If not, ask your favorite vendor to do the work for you the way you want it done, and pay them. You're getting this information for free.

September 04, 2019

Remember Our Definition?

If an item were available for two months and was sold for $100 in the first month and $50 in the second month, then the month where the item was sold for $100 represents an "Above Average Price" and the month where the item was sold for $50 represents a "Below Average Price".

Here we see a business that is healthy. Items above their average are selling better and better as time progresses. Items below their average selling price are generating less volume.

Does this matter?


You can train customers to pay more, and you can train customers to pay less. Patience with great merchandise coupled with appropriate pricing discipline results in trained customers.

Next, I'll show you what happens when you train customers to buy items that are selling below their average selling price.

September 03, 2019

A Healthy Business

The healthiest businesses are able to charge more for items ... when you see Macy's constantly selling at 40% off, you might be tempted to say "that's their business model".  That's not their business model. That's the outcome of failures in the past. If business is good, you maximize gross margin dollars.

I once worked with an Inventory Director who constantly wanted to mark down merchandise, in an effort to get rid of merchandise. Her goals and objectives were tied to getting rid of the crap. She didn't care if she harmed customers as long as she got rid of the junk.

We can use our Above/Below framework to determine how healthy a business is. Remember, the healthiest businesses are able to extract the most gross margin dollars possible. Tomorrow we'll discuss the business featured in the graph above.

September 02, 2019

Item Price

You have an item. You offer the item at $100. There are several possible outcomes for the trajectory of the item.

  • The item sells at full price, no promotions.
  • Promotions (30% off, 40% off) impact the real price the customer pays for the item.
  • The item doesn't sell well, and is systemically marked down until it is part of your clearance assortment, selling for $40.
Now, there's lots of ways to analyze this item, so I'm not telling you what the "right" strategy is.

But at least consider this. Let's say that the item sold for the following amounts, after factoring in promotions.
  • January = $100.
  • February = $100.
  • March = $70 (30% off promo).
  • April = $80 (20% off promo).
  • May = $60 (40% off promo).
  • June = $50 (clearance).
  • July = $40 (clearance).
For the sake of simplicity, let's assume that the average selling price of this item is an average of each month ... (100 + 100 + 70 + 80 + 60 + 50 + 40)/7 = $71.43.

Now let's look at our monthly distribution.
  • January = Above Average.
  • February = Above Average.
  • March = Below Average.
  • April = Above Average.
  • May = Below Average.
  • June = Below Average.
  • July = Below Average.
If you create a new variable in your database called Above_Below ... where for each customer and each item you designate if the item purchased was sold at Above Average or Below Average the historical item price average ... well, then you've got something interesting!!!

More on the topic tomorrow.

August 28, 2019

If you want to read something truly interesting to send you into the long Labor Day Weekend, read this (click here). But start with this (click here) for context. And then if you want to go deeper into the rabbit hole, try this.

That is all. Thank you.

We dig into pricing data starting next week, so get ready!!

August 25, 2019

Looks Like Merchandise Category 07 Is A Problem

In a lot of my projects in 2019 there is a "lurking merchandise problem". In other words, it's a problem that isn't easy to identify on the surface. 

But a simple modeling technique identifies problems.

Take all customers who purchased 13-24 months ago, and for that year sum up their annual spend in the 13-24 month timeframe by merchandise category.

Then create a dependent variable for total company spend in the past 0-12 months.

Regress 13-24 month variables against 0-12 month spend (after cleaning up outliers, of course).

Merchandise categories with smaller-than-average coefficents are categories that dissuade customers to repurchase.

Merchandise categories with larger-than-average coefficents are categories that encourage customers to repurchase.

This is an old technique ... we ran this stuff at Lands' End in the early 1990s (yes, I said 1990s ... early 1990s) and learned that Home purchases dissuaded subsequent orders (because the customer needed Home items less often than the customer needed a mock turtleneck).

It's common to find a merchandising team that expands into a category that dissuades future customer loyalty. And your merchandising team probably doesn't have an analyst running regressions against the customer file, so how could they possibly know that their decision was a bad one?

If you don't have the resources to do important work like this, give me a holler ( and I'll do it for you.

August 21, 2019


Yeah, that's a lousy picture. Too bad.

Today is essay day. If you don't want to read something long, stop here.

I spend a lot of time on Twitter criticizing retail ... not actual retail brands necessarily, but the "thought leadership" regarding retail.

Were you in retail in the late 1990s? I was. Business wasn't great as we approached the year 2000. And once we entered a recession, the online world crumbled. Remember the online world pre-2000? It was the wild west.

When the online world crumbled, Retail Executives had an opportunity ... they would "reign-in" those crazy online wombats who were doing whatever the heck they wanted to do. I was in the meeting, I heard every possible flavor of this sentiment.

At Nordstrom, my team measured the impact of email marketing. In the first half-decade of the 2000s we'd send multiple messages to customers, personalized by customer merchandise preference. I'm stunned how few people do this today. It's free money.

Anyway, we'd always get a lot of feedback (even though we weren't responsible for the creative or the messaging).
  • Your email campaigns are too online-centric.
My team would test store-based messaging. We learned the following:
  • An online-centric message caused customers to spend a lot online, and a small amount in-store.
  • A store-centric message caused customers to spend some online, some in store, but overall, the customer spent significantly less in total than if the customer received the online-centric message.
We had the data. What were we supposed to do? We had to be data-driven, right?

So our messaging evolved to the point where it was mostly online-centric. That style of messaging performed best.

Fast forward to 2019, and the messaging is still digital-centric. Go visit Mailcharts and look for yourself. Nordstrom had a recent campaign that encouraged the customer to buy online and pickup in a store or to shop online right now. Those are the two choices offered. Never mind that 75% of their sales likely happen in a store, the messaging tells the customer to only visit a store to pick up an online order or to sit at home and click.

A typical retail brand sends 150-350 email campaigns per year ... and the majority of those messages are digitally-focused. For twenty years, we've trained customers to ignore stores. And guess what? Customers listened to us!!

In the 2005-2010 timeframe retail purchased began to be cannibalized by a fully functional online channel. You have to remember that most retail brands didn't have a viable catalog channel, so they had to build e-commerce from scratch. Once retail brand e-commerce performed at a credible level, customers began to shift, aided by 150-350 email campaigns per year demanding that the customer shift behavior.

You probably run your own channel-based simulations, so you probably already know what I'm about to share with you. Here's how channel-based dynamics destroyed retail.


Ok, you have a customer who used to be a pure retail shopper, and that customer spent $300 a year. But you had to send the customer 150-350 digital messages per year demanding that the customer shop online instead. And your messaging worked! The customer finally shifted. Now instead of spending $300 a year, the customer spends $330 a year (not the 8x amount of $2,400 a year that vendors, trade journalists, and research brands told us the customer would spend ... their lies amplified the problem, but that's a topic for another day), spending $250 in a store and $80 online.

Did you get what just happened there? The customer went from spending $300 in a store to spending $250 in a store.

Now, this drop in spend is barely noticeable when e-commerce is 4% of annual sales.

But this drop in spend becomes noticeable in two key ways as e-commerce approaches 10% of annual sales.

First, you get the 16% hit on sales across 10% of your customer base, meaning that your comp store sales drop by 1.6%. Strong hint ... Wall St. hates that.

But second, and far more important, is the fact that most retailers participate in a "shared traffic model". In other words, when Macy's brings a thousand customers into a mall, Ann Taylor benefits because 125 of those customers visit Ann Taylor as well. But when Macy's tells the customer to not visit a Macy's store, the Macy's customer listens ... instead of 1,000 customers entering Macy's maybe just 800 visit Macy's, which means that 100 instead of 125 Macy's customers visit Ann Taylor ... which means that Ann Taylor experiences a sales decline because Macy's executes digital messaging.

Now imagine what happens when every retail brand in a mall executes digital messaging, causing every brand to get less volume in a store, causing less traffic across the board???

Everybody loses. Everybody applying a "shared traffic" model gets less traffic, causing everybody to generate less in-store sales volume.

Do you think anybody decides that this trend could be reversed by simply having a great store experience coupled with a ton of customer re-education? Nah. Not really. Instead, retailers employ the poison pill ... they offer deep discounts to buy in a store. 40% off. 50% off. All this does is cause a quarter or more of the store portfolio to become less profitable, to the point where the CFO gets interested and starts "asking questions" ... questions like "why do we keep an unprofitable store open when we could just take in all the orders online and close the store???"

That, of course, is a lousy question to ask people who don't have a simulation environment that allows the CFO to get an accurate answer to her question. Instead, stores just close.

What happens when a store closes?

Remember that $330 that the customer spends, with $250 in a store and $80 online? Well, 70% of the retail spend disappears. $75 remains ... and if there are stores nearby those stores capture the majority of the volume ... otherwise the online channel "might" capture the sales. Let's assume the online channel captures all $75. Here's the story.
  • Store-Only used to cause $300 spend.
  • Omnichannel model caused $330 of annual spend.
  • Online-Only now causes customer to spend $75 + $80 = $155 of annual spend.
The customer instantly becomes less loyal. This doesn't become apparent until a year or two later when the customer exhibits reduced loyalty and the financials don't work and the CFO starts digging into the problem again, asking uncomfortable questions like "why isn't the customer buying online and what do we do to increase sales once again??" By that time, it's too late, the damage has been done ... digital strategy took a customer who used to spend $300 in a store and turned the customer into one who spends $155 online.

If you don't believe this is the case, go ask anybody working for a retail brand who operates a simulation environment what happens ... I'll sit here and wait, ok?



Welcome back!

This is what we've done to retail. We did it. All of us.

The critic in the audience will tell me that my logic fails to resonate because Walmart and Target and Best Buy are doing ok. The critic fails to remember that those brands do not operate in a "shared traffic environment" like mall-based retailers operate in. It's the "shared traffic" component of retail that amplifies the problem. When Ann Taylor tells the customer to shop online the customer doesn't go to the mall and consequently doesn't visit J. Jill, thereby hurting J. Jill.

Out on Twitter all sorts of opinion-based gurus, thought leaders, vendors, research brands, and trade journalists have ideas for fixing retail. The latest piece of nonsense coming from this audience is a term called "phygital". Google the term. Have a bag ready next to you to vomit in should you become nauseated by the thought of merging digital and online experiences to generate a seamless retail panacea. "Phygital" is the rebranding of the term "Omnichannel", which was the rebranding of the term "Multichannel" which, well, you get the picture. It's a failed thesis, and it's a thesis that makes vendors & pundits & thought leaders & research brands & trade journalists a lot of cheddar.

If you adhere to a "phygital" thesis, you might believe that marrying digital and in-store experiences is good for the customer. You might be right ... you might achieve the $300 to $330 a year gain I mentioned earlier. However, you haven't solve the "shared traffic" problem, have you?

That's the problem that has to be solved.

Shared traffic.

We need ALL retail brands to de-emphasize the digital side of the business and instead prioritize getting the customer to get in a car and drive 12 miles to a store.

I know, that's practically impossible, isn't it?

The job of a retail marketer becomes not fundamentally different than the job of the Athletic Director at the University of Illinois. Imagine trying to get people to attend an Illinois football game when the team (merchandise) isn't very good and the game is available on television for free (just like your merchandise is available online)???

This is what the future of retail looks like ... we have to figure out how to get people out of their homes, into a vehicle or into mass transportation, and then have to provide a compelling in-store experience ... or we end up like a college football stadium that is half-empty with a losing team getting pummeled 42-7.

"Phygital" doesn't accomplish this, does it?

The first retail marketer who figures out how to redirect traffic from the online channel to stores is going to become famous.

Questions? I'm at

August 19, 2019

Pay 'em!!!

Two weeks ago I shared this image, created via one of my "bifurcation" projects.

The image was part of a series outlining how old-school catalog brands possess a customer base that is splitting into two pieces ... traditional catalogs shoppers who can actually support "more" mailings (yes, more) ... and everybody else who should receive considerably fewer mailings.

There are two comments I receive when I share this feedback. The two comments sometimes happen at the same time.
  1. I don't want to mail more catalogs.
  2. I don't want to mail fewer catalogs.
I realize it's not fun to have an outsider tell you how you could make more profit. You are pummeled with outsiders telling you what to do, and frequently, the outsiders are wrong.

I also realize that way, waaaaaaaaay too many of you do not get rewarded when you make your company more profit. You work for a $50,000,000 brand that makes $2.5 million profit per year and you listen to me and you increase profitability by a half-million dollars and you don't get anything for it ... nothing ... no bonus, you get your typical cost-of-living increase ... and you watch as the digital folks climb the corporate ladder. I get it, that's not fun, and there's no financial incentive to do the right thing.

When I work on these projects with the Private Equity folks, there's a lot of financial incentive to do the right thing.

When I work on these projects with the CEO or CFO, there's a lot of financial incentive to do the right thing.

But for so many of you, there isn't a financial incentive to do the right thing. And that's sad.

Maybe the CEOs/CFOs who are reading this could change that????

August 18, 2019

Wanna See What Video Looks Like?

Courtesy of Fast Company, we learn about Trader Joe's YouTube Channel (click here).

This isn't difficult to do. It requires a modicum of creativity ... and because we've ROI'd creativity out of our businesses in a lust for digital efficiency, you might think this is hard to do.

It's not.

Go do something.

August 12, 2019

Stitch Fix Just Tells You What They Do

This is going to scoot way above the preferred mathematical levels of most readers, and that's fine. The reason for sharing this is that folks wonder why their $40,000,000 business isn't growing while Stitch Fix went from zero to a billion in sales in no time at all ... now granted, Stitch Fix needs humans with taste and they need cute merch ... but they also leverage math. And they tell you what they're doing ... almost daring you to copy them.

It's all there for you (or for you to get Cohere One or Belardi/Wong to do for you) ... just copy them ... you keep asking me for best practices, well, go get your favorite catalog agency to copy this for you. Just ask them.

Here's the link (click here).

August 11, 2019

Bifurcation and Shopify all in One Article

I talk frequently about catalog bifurcation ... and many readers leverage the Shopify platform. Well, you can read about the topic of bifurcation and you can read about Shopify (and Walmart and Amazon) in this article (click here).

August 07, 2019

Everybody Else

Look at everybody else ... the bottom half of the file ... there's barely any catalog profit available mailing them.

That's the future ... it's what e-commerce brands who embrace catalogs learn quickly. It's the future for catalog brands as well. You're not going to mail the vast majority of your customer file much anymore.

You may not agree with what I'm saying. That's fine. Work with your favorite agency, analyze your best "catalog" customers, and tell me what you learn, ok?

August 05, 2019

I Know, You Don't Believe Me

In this example, the best customers do the following:

(a) They spend $360 a year BECAUSE of catalog marketing (and another $200 organically without catalogs, but that's a story for another day).

(b) They receive 22.1 catalogs per year.

(c) With a profit factor of 45% and $0.70 ad cost per catalog, the customer generates $145.70 in annual profit.

A lot of you use a "rule of thumb". You add productivity at 50% to see if another mailing will work or not.

So in this case, let's do that. Except we'll add productivity at 33%.
  • $360 / 22.1 catalogs = $16.29 per catalog.
  • 33% productivity = $16.29 * 0.33 = $5.38.
  • $5.38 per book * 0.45 - $0.70 = $1.72 profit.
Let's try adding the contact at 15% productivity.

  • $360 / 22.1 catalogs = $16.29 per catalog.
  • 15% productivity = $16.29 * 0.15 = $2.44.
  • $2.44 per book * 0.45 - $0.70 = $0.40 profit.
Do you see where I'm coming from?

Run your customer data through a sim and see what the sim tells you.

You're getting this help for free ... go do something with it, ok?????

August 04, 2019


In the past three years, the simulations all show the same story ... the very best "catalog" customers are not being contacted enough.

Look at the data in this graph ... going from left to right we have increasing 12-month buyer customer quality (i.e. best customers are on the right).

The very best customers (big blue arrow) are worth an order of magnitude more than the next-best customer segment. They get just one more catalog, but are worth SIXTY (60) additional dollars of profit.

These customers should get more catalogs. Period.

Not just one or two ... quite possibly twenty (20) more catalogs per year.

It requires a complete re-thinking about how you construct a contact strategy.

It requires a complete re-thinking about how you score your customer base so that only the best "catalog" customers are treated this way.

It requires that you or your favorite agency (Cohere One, Belardi/Wong etc.) create a simulation environment so that you can plainly see how this is happening.

It requires your printer to help you develop 16-32 page dynamic catalog content (DCC) mailings with a personalized merchandise assortment.

It requires you to mail all of the remaining customers FEWER catalogs.

In other words, the process of Bifurcation is tearing the traditional 9-18 contacts per year strategy apart.
  • Old thinking = 9-18 contacts per year, with various segmentation strategies to "target" various customers.
  • Modern thinking = 30-45ish contacts per year for a small number of customers, 0-4 contacts for nearly everybody else.
I've got the data.

I've built the sims.

This is where things are headed.

I'm telling you this ... for free.

Go do something with the information, ok??

July 31, 2019

Making Small Page Counts Work

In the simulation from the start of the week, we noticed that more pages per contact was more profitable than fewer pages per contact.

This is where merchandise personalization comes into play.

When crafting a 32 page catalog, you dynamically insert merchandise that aligns with what the customer previously purchased. This causes the 32 page catalog to perform 20% to 30% better, and as a consequence it changes the math in the simulation. The math then favors small page counts. And you want small page counts, because you'll contact the customer more often. CONTACTS > PAGES.

This is why the future is all about dynamic content populating frequent/small contacts.

Your printer loves this.

Your favorite catalog agency loves this.

And you'll love this, because you're going to invent something new and interesting within the catalog industry.

I'm not aware of a lot of circumstances that benefit printers, agencies, and you.

Run your Optimal Mailing Strategy Simulation, see what it tells you, and then move in a new and interesting direction.

You can do this!!!!

July 29, 2019

What Does An OMSS Show For Great Customers With High Organic Percentages?

When a great customer has moved on from catalog marketing, the optimal contact strategy changes ... significantly!!

In this example, the brand is mailing a dozen 80 page catalogs to the great customer who is no longer a shopper due to catalog marketing.

And that, dear readers, is a mistake.

The brand should mail this customer six 32 page catalogs, or three 80 page catalogs (not 12), or two 160 page catalogs.

This is how the world has changed ... this is Bifurcation in action.
  • Great Catalog Customer = 1,800ish pages per year.
  • Great Brand Customer = 240ish pages per year.
  • Current Strategy = 960ish pages per year.
It's time to change strategy, isn't it?

It's time to start thinking about the future.

July 28, 2019

What Are The Simulations Showing?

This table illustrates the results of an Optimal Mailing Strategy Simulation (OMSS).

We enter customer metrics in the upper left portion of the simulation, and the spreadsheet calculates the optimal strategy at each page count.

The results are summarized in the upper right portion of the table. Go ahead, click on the table and take a peek, I'll wait for you, ok?

Welcome back!

This simulation demonstrates what happens when you figure out the optimal strategy for a customer that is responsive to catalogs (organic percentage = 20%). Look at the summary portion of the table. The customer could receive 40 contacts at 32 pages each, or the customer could receive 15 contacts at 160 pages each. Neither solution is being employed by this brand (12 contacts at 80 pages each). The best customer can be mailed MORE often. Yes, MORE often. Either more pages, or more contacts.

And in this case, more pages results in more profit because ad costs are steep at 32 pages and are efficient at higher page counts. Your mileage will vary ... CONSIDERABLY!!!!!

But the result of the simulation is MORE ... across the board.

This is the story that simulations repeatedly demonstrate across my project work.

Ask your favorite vendor to produce Optimal Mailing Strategy Simulations (OMSS) for you. They're smart people, and they can easily assemble the simulations at your request. Or ask me and I'll do it ... either way, you win, and if you ask your favorite vendor to do it they win. Or build it in-house ... challenge your analyst to do something fun!!!

Regardless, you need to produce Optimal Mailing Strategy Simulations (OMSS).

Price Impact Over Time

So this is kind of fun! If you can run a regression equation for one year, why not run one for each of the past four years? This allo...