What-if analysis for retail: a practical guide

What-if analysis sounds more technical than it is. It is the simple practice of asking: if I do this, what happens to my numbers? And then working out the answer before you commit, not after.

For retail, where most major decisions are reversible but expensive, what-if analysis done well is one of the highest-return habits an operator can build.

The decisions where it matters most

Not every retail decision benefits from a formal what-if. Deciding where to put a display stand does not need a model. Deciding whether to run a 20% off promotion on your top-selling category does.

The decisions where what-if analysis returns the most value share three characteristics. They are consequential, affecting revenue or margin materially. They involve uncertainty, you do not know exactly how the market will respond. And they are reversible, meaning you can course-correct, but only after you have paid the cost of getting it wrong.

In retail, those decisions are: promotional discounts and markdown depth, reorder quantities and buying decisions, new product ranging, price changes, and new location decisions.

How to build a simple what-if model

For a promotional decision, a basic what-if has four inputs. Current price, proposed price, current volume, and your estimate of price elasticity.

Price elasticity in retail ranges widely by category. Essential grocery items are relatively inelastic: a 10% price cut might drive 5 to 10% volume increase. Fashion items are more elastic: a 20% markdown might clear stock twice as fast. The elasticity estimate does not have to be precise. It has to be better than no estimate at all.

From those four inputs, you can calculate the revenue and margin impact at three scenarios: optimistic, base, and pessimistic. The spread between the three scenarios tells you how sensitive the outcome is to your assumptions, which tells you how much uncertainty you are carrying into the decision.

The markdown decision worked through

A specific example. A retailer has a line of winter jackets, 80 units, currently priced at £89. The season is ending. The options: hold price, markdown to £69 (22% reduction), or markdown to £59 (34% reduction).

At full price, in 4 remaining weeks of season, they expect to sell 20 units: £1,780 revenue, £1,040 margin at 42% margin. At £69, they estimate 35 units: £2,415 revenue, £910 margin at 37.7% margin. At £59, they estimate 55 units: £3,245 revenue, £975 margin at 30%.

On revenue, the deeper markdown wins. On margin, the options converge. On stock clearance, the deeper markdown is materially better, with 25 units remaining at £69 versus 60 remaining at full price.

The right answer depends on what you value most. Cash now, margin protection, or stock clearance before you pay for storage or write-off. The what-if makes that trade-off explicit before you commit, not after.

The tool question

You can do this in a spreadsheet. The limitation is that spreadsheets model what you ask them to model, without challenging the assumptions. The elasticity estimate you put in is the one that comes out.

A proper what-if tool challenges the assumption. It runs the model at multiple elasticity scenarios, flags where your assumption is optimistic relative to comparable decisions, and argues the downside as well as the upside.

For retailers making more than a handful of these decisions per month, the compounding value of a tool that challenges the assumption, rather than just calculating it, is significant. The decisions where you are systematically overoptimistic are the ones that cost you most.

Building the habit

The goal of what-if analysis is not to produce a perfect model. It is to produce a better-informed decision than you would have made without it. Even a rough three-scenario model on a markdown decision, built in ten minutes, is materially better than committing to the price change because it feels right.

Start with your most consequential recurring decision. For most retailers, that is the markdown decision at season end. Build a simple what-if template. Use it. Improve it after you see the result. After six months of this, your markdown decisions will be materially better, not because you have become a better modeller, but because you have stopped making the same optimistic mistake twice.