The art & science of forecasting
How I achieved nearly 100% portfolio accuracy across dozens of openings
The tl;dr
Forecasting new store sales can be really difficult
Many retailers’ forecasting is over-reliant on gut, irrelevant experiences at prior brands, and/or minimal data
No single forecasting methodology will work every time
If you’re a DTC or omnichannel brand, you can tap into the power of your data to develop multiple methodologies
Leverage numerous methodologies to look at an opportunity from multiple angles
Approach your store performance results as a portfolio to avoid over-reacting to individual wins or losses
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Forecasting a new store’s sales is f***ing hard
When you’re building a business case for a new store, the Year 1 Sales forecast is arguably the most difficult line item to pin down. It’s also one of the most influential variables in the model that can make or break the business case. Most of the expenses shouldn’t vary from your business case because they are, in theory, almost all controllable, and the uncontrollables typically aren’t so large it swings the business case one way or the other.
A lot of forecasting for new store “pro formas” (another word for business case) is shockingly done with little science, and more often based on gut or irrelevant past experiences.
In many organizations, there’s a retail leader who says “we did really well at [my last irrelevant/incomparable brand], so we should do well.” The “finger in the air” method almost always leads to overly optimistic/pessimistic bets based on irrelevant, or a lack of, information. They key is to find the benchmark data points that are relevant, and build a data set to extrapolate trends from.
As you forecast, you need to establish your goal: is it to get every single store right, or get the portfolio right (the aggregate)?
It really depends on where you are in your journey. If you’re just beginning to test into your first few, then each forecast needs to be met/exceeded. If you’re in scale mode, then the portfolio approach lends you a little more flexibility and is the more important output as you answer to your investors (“is this working?”).
The portfolio approach
Opening stores is not too unlike being an investor: you raise funds, invest in risky assets (stores/stocks/companies), and you optimize for the portfolio ROI, not necessarily for every individual investment. Put another way, the successes or failures of individual bets isn’t as important (but they’re still important!) as long as the total portfolio achieves the target ROI.

This method is great to evaluate your overall ability to (a) forecast and (b) prove out the channel, but can also lead to misguided conclusions. See the below example:
This is less likely, but can be possible. The important thing is to evaluate your store pro formas both individually and as a whole. The opposite can also happen and is arguably more common: a few big, terrible bets’ losses threw off the total portfolio:
This example tends to be the common outcome of investing in flagships, or super expensive real estate that doesn’t actually produce sales (“high street” retail).
For what it’s worth, and just to beat a dead horse, the portfolio approach is also how a lot of venture capital investors approach their investments: they just need a single 100x investment to make up for all the duds, so that their portfolio returns 10x overall to the fund owners.
I describe all of that because it’s impossible to get every individual bet right, especially if you’re in scale mode. The most important thing to keep in mind is to avoid extrapolating from individual results and instead focus on analyzing where the beat or miss came from. The beauty of the methodologies outlined below is that they also enable you to break down where your pro forma went wrong, because they all require some type of math to calculate their outputs.
There are a LOT of ways to forecast sales, and they’re all equally informative
The below is an in-depth list of the four methods I’ve found most useful, and how to interpret them in a framework Wall Street calls “the football field.” This is the collection of methodologies that I used to achieve nearly 100% portfolio accuracy over dozens of openings, and also refute others’ qualitative forecasts with my quantitative ones:
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