Project Opportunity

I am in the process of finalizing a project package around discounts/promotions/pricing.

As is usually the case, I offer a significantly discounted project opportunity for loyal blog readers as I finalize the project code and test usability of the concept.

For you, that means I am offering two (2) readers the opportunity to purchase the discount/promo product (you've been reading about elements of what this project will become) for just $12,000 (it will be a $24,900 project when it is officially released).

Contact me immediately (kevinh@minethatdata.com), because both spots will be gone shortly.

Where Are The Discounts Being Applied?

Sometimes discounts/promotions are applied to items via liquidation efforts. This is one of those cases.

In this table, each row represents a group of customers. Best customers are at the top of the table (5% = best decile of customers, 15% = next best decile of customers). New/Reactivated buyers are at the bottom of the table. Meanwhile, item sales are rank-ordered across the top of the table. The best items are in the 5% column, the worst-selling items are in the 95% column.

Which cells (red) represent customers buying discounted products? The columns at the far right side of the table tell the story ... the worst-selling items are selling at/above their historical average price point about 70% to 75% of the time. The best-selling items sell at/above their historical average about 85% of the time.

So we have two dimensions at play ... marketers are offering discounts/promotions, no doubt about it. But at the same time, the merchants are liquidating lousy products. Both parties contribute to the challenge ... as is generally the case.

Optimal Price / Discounting Ratio

Ok, this isn't optimal because I'm not looking at gross margin dollars (that's reserved for clients), but you'll get the idea here about what I'm looking to accomplish.

I created regression models that predict the impact of prices on rebuy rates, and another model that predicts the impact of discounting on rebuy rates (again, gross margin work is done for clients).

The average price point for this category was $50.00, and the average percentage of items sold at/above the historical average price point was 75%. This yielded a 51.6% rebuy rate.

What happens if prices increase to $55.00 and discounting remains constant?  Rebuy rates decrease to 49.7%.

What happens if prices increase to $55.00 and discounting increases so that just 56.3% of the items are selling at/above their historical average price point?  Rebuy rates maintain at 51.5%.

This dynamic is coming to an omnichannel brand near you (your mileage will vary). Prices increased a year ago and continue to increase, customers balk at higher prices and purchase less often, leading "brands" to discount more to maintain response (which means that gross margin dollars decrease and the brand is less profitable).

It's hard to fight the customer. The customer is telling us how much s/he is willing to spend, and the customer does not care one bit, one bit, about how our cost-of-goods increased. Many of us are going to give up profitability, either by increasing prices / decreasing response, or by increasing prices / discounting (which decreases gross margin dollars).

Sometimes Discounting Leads To Positive Results

The secret to discounting is to generate more gross margin dollars and build a stronger customer file (for some, it is to liquidate merchandise, but that is a topic for another day). The goal should never be to steal market share, because honestly you'd have to be a ten billion dollar brand (or larger) for that to even make the slightest difference.

So, if you choose to sell a $50 item with a $20 cost of goods for $35 via your promotional/discounting program, you must sell twice as many units to equalize gross margin erosion.

I worked with a smart brand who knew what this relationship looked like, and they discounted only enough to make the math work (or, in this case, come very close to working). In their case, I created regression models that predicted how much customers would spend next year on items selling at/above their historical average price point, how much customers would spend next year on items selling below their historical average price point, and how much gross margin dollars customers would generate next year.

For customers who spent $100 on items at/above their historical average price point and spent $0 on items below their historical average price point ...

• $64.41 spent next year on items at/above the historical average price point.
• $8.44 spent next year on items selling below their historical average price point.
• $73.05 spent next year, total.
• $45.37 gross margin dollars generated next year.
• 62.1% future gross margin.

For customers who spent $0 on items at/above their historical average price point and spent $100 on items below their historical average price point (discount-centric customers):

• $56.51 spent next year on items at/above the historical average price point.
• $28.74 spent next year on items selling below their historical average price point.
• $85.25 spent next year, total.
• $44.27 gross margin dollars generated next year.
• 51.9% future gross margin.

This company comes really, really close to getting the math right. Full price customers generate $73.05 next year while discount-centric customers generate $85.25 next year. Discounting led to customers who spent 16.7% more in the next twelve months. However, full-price customers generate a 62% gross margin next year while discount-centric customers generate a 52% gross margin next year. Full-price customers generate one extra dollar of gross margin in the next year.

This is one way to make discounting work. As long as gross margin dollars are equal/greater and as long as the customer file is stronger, the math "can" work. I have a responsibility to share this fact with you.

But you have to do the math, perform the analysis, and understand the tradeoffs. At minimum you have to take the math down to future gross margin dollars. In a smarter world, you'd include pick/pack/ship information as well, and take into account any additional p&l metrics that matter.

By Year of Introduction

This example is from the pre-COVID era ... and the example didn't turn out the way I expected it to turn out.

When I looked at the percentage of sales at/above the historical average price point for an item by year of item introduction, I expected the brand to discount items more and more as the item aged. I didn't expect this:

Some of the trends make sense to me. Newly introduced items (look at 2016 / 2017 / 2018) are more likely to sell at full price than are older items. This happens all the time ... older items are discontinued and are consequently discounted.

2019 looks different - you can see that this brand made a solid effort of re-establishing price integrity after several years of discounting.

As many of you are experiencing in 2022 ... when prices go up (either due to the end of discounting or because of cost of goods sold increases), response goes down. That's how this stuff works. Eventually, you land on a local maxima where pricing is theoretically optimized ... until it isn't once again.

Pricing Evolution

This is from the pre-COVID era ... the before times ... I'm analyzing a brand that, over time, was becoming healthy. Look at the percentage of sales sold at/above historical average price points by year by pricing band (0 = lowest prices, 9 = highest prices).

When the company discounts, where does the company discount? With the lowest price points. The brand maintains price integrity on the most expensive items.

Compare 2019 to 2015. Tell me what you see?

Yeah, in 2015 this was a sick brand, selling half of merchandise at a discounted price. In 2019? Much healthier.

If you are a consultant like me, it's pretty easy to see when a brand is healthy or unhealthy, based on the prices that items are selling at over time. I realize that in 2022 many of you were forced to increase prices AND your business became unhealthy in the process ... that's a different issue than the one we're analyzing above.

Who Gets The Deals?

This company doesn't do a lot of discounting, but they marginally prefer to offer discounts to the very best customers or the very worst customers. This signature comes up often in my work.

Again, the differences aren't huge (77% of items sold at/above their historical average price point for best/worst customers, 81% for middling customers).

But there are instances where you'll see 65% sold at/above historical average price point for an item among average customers and 25% sold at/above historical average price point for best/worst customers. Those situations don't end well ... you train the customer to wait for deals, and the "average" customer simply never purchases. Seen it a lot. No bueno.