The Lift Assumption Hiding Inside Every Promotional Plan
Every promotional plan has a number buried inside it that nobody talks about: the lift assumption.
It's the percentage demand increase your team expects when a promotion runs. It lives in your causal adjustments. It shapes your open-to-buy. It feeds your inventory receipts. And in most planning organizations, it's a guess. And not a reckless one.
Planners work hard to make it reasonable. They pull last year's event data, find something comparable, and apply a lift that feels defensible in a planning meeting. But defensible and accurate aren't the same thing. Most planners know that. They use the number anyway because there's nothing better within reach.
That gap, between how confident a promotional plan looks on paper and how uncertain the assumptions underneath it actually are, is where margin gets lost.
The Margin Cost of a Miscalibrated Lift Assumption
Miscalibrated lift assumptions compound in both directions. Over-estimate demand lift and you've over-planned receipts against a promotion that won't deliver. You end up with inventory you're now marking down further to clear. Under-estimate and you've constrained receipts unnecessarily, leaving revenue on the table when the promotion actually performs.
For a brand running six to ten promotional events a season across categories and channels, the cumulative margin exposure adds up. What makes it hard to address is that the error rarely shows up cleanly. There's no post-mortem that ties a specific lift assumption to a specific markdown outcome. The problem stays invisible until the end-of-season inventory position makes it very visible.
The other cost is time. Building a promotional causal means triangulating across last year's event files, category-level averages, and informal conversations with merchants, then landing on a number you're still not fully confident in. That process can take hours. And it still doesn't answer the most basic question: if I discount this product 20% in this region during this window, what demand should I actually expect?
How to Build a Promotional Plan You Can Actually Trust
The fix isn't eliminating judgment. It's giving planners a foundation that's better than memory and gut feel.
Ground Lift Estimates in Price Elasticity, Not Category Averages
That starts with capturing promotional performance at the right level of granularity. Category-level lift averages hide the variance that actually matters. A 25% off event on a core fleece jacket performs differently in a flagship urban store than it does in a suburban outlet. If your lift data is averaged across those contexts, your assumptions will be structurally wrong for both.
Lift estimates need to reflect price elasticity: how a specific product in a specific market actually responds to discounting. That relationship isn't constant across products or locations, and treating it as if it is will keep your promotional assumptions soft. A blended average across your portfolio tells you very little about how any individual item will behave when it goes on promotion.
Make Scenario Testing Part of the Workflow, Not an Afterthought
The second piece is scenario testing, and it has to happen before the plan is locked. If running a what-if requires a data analyst or a half day of spreadsheet work, it won't happen in practice. Planners will default to the number they have. The scenario tool needs to be embedded directly in the planning workflow, fast enough to test three discount levels in the time it currently takes to test one.
Separate the Recommendation From the Commitment
Planners should be able to see the predicted demand impact of a promotion before they create it, not set it and then wonder whether the lift assumption holds. The ability to simulate gives the plan an honest foundation. It also makes the planning conversation with merchants and leadership more grounded. You're not defending a judgment call. You're presenting a modeled outcome.
From Institutional Memory to a Repeatable Process
This is the process change that actually moves promotional planning from institutional memory to something repeatable, defensible, and less dependent on whoever happened to run the last comparable event. It doesn't remove the planner from the equation. It gives them better information to work with.
This is the direction modern merchandise planning platforms are heading: giving planners a modeled read on demand before a promotion goes live, not just a number to defend after the fact. Toolio builds toward that same goal, grounding the lift assumption in your historical promotional data and price elasticity instead of memory.
