Launching Next Week

Next week, we'll talk more about my latest booklet ... "Hillstrom's Pricing".

Until then, you can order the print version ($7.99) or the Kindle version ($2.99) right now (click here). Why not buy it, read it, and be ready to think about how the concepts impact your business?

Pier 1

2016:  Pier 1 completes omnichannel transformation (click here).

2019:  Pier 1 to close at least 70 stores as loss doubles (click here).

Here's what is coming in retail.

• 50% to 75% of the store base ... the stores that are boring ... are going away.
• Those stores will be replaced (already are being replaced) by boring, commodity-based e-commerce.
• The stores that remain become much more "Sports Arena" like ... providing great entertainment or outstanding knowledge/service.

We saw this transformation play out a decade ago in catalog marketing ... remember when 40% of all circulated catalog pages "simply disappeared" and never returned (by 2011)?? The pages were boring, they went away, and that was that.

The same dynamic is happening in retail.

Best to adjust now ... better late than never, right?

P.S.:  Next week I'll formally announce my new booklet and new product called "Hillstrom's Pricing" ... if you are a Kindle fan, you can get the Kindle version right now (click here). The print version should be available in a day or two.

It's Gonna Get More Expensive!

Remember, each run of the MineThatData Elite Program is virtually free.

• $1,800 for first-time participants.
• $1,000 each run thereafter (3 runs per year, February, June, October).
• You'll send me 5 years of purchase data (I'll forward you the file layouts and invoice when you say yes).
• Data sent to me by October 15.
• Payment sent to me by October 15.
• Analysis completed by October 31.

In this run, you'll get the typical rolling twelve month analysis, the comp segment analysis, and the rebuy table that is included in every run. 

In this run you will also get an assortment of pricing analytics, helping you see if you potentially have a price point problem that is hurting your business. This style of analysis will become a new product offering in the next few weeks, paired with a new booklet on pricing analysis. Needless to say, this analysis will get more expensive than the $1,800 you'll spend here at the "experimental phase".

So sign up now ... contact me (kevinh@minethatdata.com / 206-853-8278) and let's get busy, ok??!!

It's Time!! The MineThatData Elite Program!

Every four months MineThatData Elite Program members get to learn how their business is performing, and they get to participate in some of the experimental work I'm doing. This run will be no different!

I've been talking a lot about pricing in the past three weeks, and for good reason. This is the hot topic in most of my projects in 2019, and the fact that it has been the most popular topic in 2019 results in a forthcoming booklet called "Hillstrom's Pricing". You'll learn more about the booklet in the upcoming week ... and there will be a full product offering to match up with the booklet.

Regardless, in this run of the MineThatData Elite Program I plan on giving program members bonus analytics included in the pricing booklet (in addition to the normal tables I run for your business). You'll learn if your pricing strategies are driving away customers or not in this analysis. I'll toss in other pricing analytics as well.

How much does this cost?

• $1,800 for first-time members.
• $1,000 for loyal customers.

Data needs to be sent to me no later than October 15.

Payment needs to be sent to me no later than October 15.

Your analysis will be completed no later than October 31.

If you want to hop on right now at the ridiculously low price of $1,800, contact me right away (kevinh@minethatdata.com / 206-853-8278) and I'll get you invoiced and ready to go.

$1,800 is practically free, folks.

Maybe The Email Marketers Weren't So Bad After All

Yesterday I criticized email marketers for purposely skewing their assortment to items priced lower than average.

Here's the same company ... looking at share of email marketing dollars by price band. Green represents higher-than-average figures.

The data shows that the email marketer has spent two years gradually featuring more expensive items in email campaigns. Email marketing might still skew to lower price points, but the multi-year trend is increasingly positive.

When you review whether your business has pricing issues, please review all of your marketing channels. It's common to see oddities (for example - catalogs that have higher-than-average price points due to items being fossilized on paper forever) ... and those oddities manifest themselves via strange downstream customer behavior.

Oh Come On Email Marketers!!!

It's price point band by high-level channel. And once again, there's a consistent theme that runs through my projects.

Look at share of annual volume by price point band for email marketing. Tell me what you see?? Green numbers are "above average" ... red numbers are "below average".

Email transactions skew toward items priced between $0.01 and $29.99 ... at the far low end of the spectrum.

Do you realize how often this happens? It happens in at least 75% of my projects. It's out of control! For the past twenty years, we optimized email metrics by subtly shifting the cheapest items into the campaigns. Notice I didn't say "optimize the business" ... nope ... we just optimized our email marketing metrics.

Email metrics (in this context) should be a straight average of the whole business ... we should reflect everything in the assortment over time ... not just the cheapest stuff so that the email marketing team gets credit for having good metrics.

If You Like Stories About How Brands And Vendors Purposely Deceive Customers, Read This

Here you go, click here.

How Does A Shifting Customer File Impact The Future?

For the company we're studying, here we see what customers spent, on average, by price band over the past five years.

Notice that customers are leaking out of lower price point bands as the company expands volume among items >= $20.

On average, customers are spending at least $3 less this year than last year, and nearly $6 less than four years prior.

Does this mix impact future value? We can take our equations from yesterday and find out.

Using the 2019 equation, we score 2019 customers and 2018 customers to measure the impact of file mix ... take the 2019 column in the first table and multiply it by the 2019 column in the second table, and add the constant.

• 2019 Customers are worth $50.02 in 2020 based on 2019 file mix.
• The file mix for 2018 customers yielded $52.28 in 2020 value.

In other words, the current file mix delivers 4% less value next year. This brand shifted customers out of lower price point bands, and the activity is causing the file to be 4% weaker than it would have otherwise been.

You'd probably want to know this if you were planning what 2020 will look like, right?

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.

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.

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?

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.

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.

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.

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.

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.

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.

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?

Absolutely!

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.

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.

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.