Learnings

When you start a business, the easiest way to price your product is to use a strategy called cost-plus pricing. You take your cost of goods sold (COGS) and you mark them up to your desired gross margin—this thing cost me $8 to make, I want 20% profit, so I’ll charge $10. It’s simple, fast, and creates a profit target that’s forecastable and measurable. As demand increases, raw profits increase.

But there is a tradeoff.

While that profit target may feel “good enough,” you don’t know whether “good enough” is actually the best you could do. Perhaps customers would be willing to pay more, or perhaps you could massively expand your market by charging less. 

This topic (or set of topics, really) was the focus of a Twitter comment the other week that caused a stir:

I got involved for a bit, but never had a chance to reply as deeply as I wanted. So, I’m doing that now!

DTC Ecommerce is an industry that has historically adopted cost-plus pricing. And in a cost-plus pricing world, a change in costs requires a change in price to maintain a profit target. Tariffs— which increase costs—MUST increase prices.

This alone explains why the tweet triggered a bunch of responses from people on DTC Twitter. In a cost-plus model, the assertion of the tweet is clearly wrong. 

Upon pressing, John clarified his point as brands are already “sell[ing] for the best possible margin which means they’re selling for the price that gives them the best possible gross profit per visitor regardless of tariffs.” 

I’d challenge two different assumptions here:

1. This assumes that brands are not doing cost-plus pricing, but instead doing profit-maximizing pricing.¹ I don’t think most brands approach it this way (yet—at Intelligems we’re trying to change this)

2. Even if you assume a profit-maximizing price approach, in almost any normal case you’re going to end up with higher costs meaning a higher profit maximizing price

Let’s walk through how “profit-maximizing pricing” actually works. 

To start, we need to recognize that the demand for a product changes depending on the price point. Cheaper price = more customers and visa versa. It’s rarely a linear pattern, and the sensitivity here is also known as “price elasticity”

Generally speaking, this price<>demand relationship could look something like this: 

With that elasticity data, you can multiply quantity * price to get to revenue. Chart that, and you can find the price that leads to the most possible revenue. Now you can also take into account COGS data and use that to find your profit-maximizing price and your revenue-maximizing price. They’re very likely to be different if you have any real COGS.² You can run experiments to measure your elasticity, and use that data to find these maximizing prices. 

With the above demand slope and a COGS of $40, the price that leads to the most revenue would be $45 (lots of people would buy here, but it’s not very profitable); but the price that leads to the most profit would be $80 (almost half as many people are willing to buy here, but it’s far more profitable):

But if you change the COGS (say a 30% increase to COGS, so the cost of goods is now $52) the curve looks like this.

The revenue maximizing price stays the same since costs don’t affect it, but the higher cost resulted in a higher profit-maximizing price. It’s now $95. In a situation where you have a normal demand curve (goes down with higher prices), and that is held steady, a higher cost is mathematically going to result in pushing out the profit maximizing price.

That’s what I think the original tweet gets wrong. 

There is another dynamic going on which is that I think tariffs were the breaking point for many brands that forced them to go evlaute this data and seek a profit maximizing price after running cost-plus in the past. This was the proverbial straw on the camel’s back. Brands are being forced to learn these slopes and build out these curves - and that’s often leading to higher prices! 

None of this is lying. It’s learning.

Intelligems was quite literally founded to deliver these learnings to brands, and it’s a set of learnings that, as you dive in, end up getting fairly complex. There are all sorts of ways these charts simplify the real world. Your product mix might end up causing cannibalization during a price test (people “trade” into the product being tested and out of a more expensive product in the catalog); different products may end up with different demand curves; and different customer segments might have different price sensitivities; inflation and consumer confidence can impact the demand curves The list can go on—and that’s just within your business and your brand. 

What happens when every brand’s costs go up and, therefore, every brand’s prices go up? The demand curve is going to change without you changing anything.

Can you change it back? Does that require a price increase? A price decrease? How does that change your profit maximizing price? 

There are a lot of questions to ask right now, and just as many answers to learn. Tariffs just forced us to get there.

1  I actually like that this is the assumption/perception, because it means we at Intelligems are doing our jobs—this is a clearly superior way to price, and we’ve been helping brands do it for a few years via price testing.

2  For software or other things with marginal costs close to 0, the revenue and profit maximization price will be basically the same.