Promo Lift Decomposition: Where a Promotional Bump Actually Comes From

Two published studies decompose the same promotional bump and come back with very different answers. One says about three quarters of it comes out of rival brands. The other says about a third. Both are right, and knowing why is what lets you hold your ground when somebody quotes the other number at you.

The first counts the elasticity. Working from household panel data covering 173 brands across 13 categories, it splits the promotional price response into the three decisions a shopper actually makes: which brand to pick, whether to buy the category at all on that trip, and how much to carry home. On average 75% of the response sits in the brand choice, 11% in the decision to buy at all, and 14% in the quantity [Bell and colleagues]. Read as units, your promoted brand gains 100 and rival brands lose about 75 of them.

The second counts the units rivals actually lose. Working from store scanner data, it nets off something the first calculation leaves standing. Your promotion also pulls extra shoppers into the category that week, and a share of those shoppers buy a rival, so part of the 75 comes straight back. The net loss to rivals lands nearer 33 units for every 100 you gain [van Heerde and colleagues], with the remainder splitting at roughly a third each between volume borrowed from your own future weeks and demand that is genuinely new to the category.

Which number you reach for depends on who is in the room. Your share story runs on the gross figure, because that is what moved on the shelf. The retailer's category story runs on the net figure, because the category kept most of what you took. A buyer working from one paper and a brand manager working from the other can spend a whole meeting disagreeing about numbers they both have right.

The four buckets below take the net view, because that is the one a promotion has to pay itself back out of. They also split the borrowed third into its two sources, which is an illustrative calibration rather than a published set of numbers.

The four buckets, plain English

Every promotional event hides four very different streams of units inside the same headline lift number:

• Cross‑brand switching (~33%): shoppers who bought your brand instead of a competitor. Real volume, the manufacturer wins, the retailer's category is flat.
• Acceleration (~33%): shoppers who would have bought the brand anyway, but bought it during the promo window instead of next week. The dip after the event is where this volume came from.
• Own‑brand cannibalization (~8%): shoppers who switched from one of your other SKUs to the promoted SKU. Same brand, same shelf, thinner margin.
• Category expansion (~26%): genuinely new occasions or new shoppers who entered the category because of the promo. The only stream that grows the pie for everyone.

Add up the within‑brand pieces (acceleration plus cannibalization) and you get the 41% that is timing‑shift and own‑portfolio reshuffle. Subtract that from 100 and you are left with the 33% switching plus the 26% expansion that together represent real, additional volume.

Whether the pack can be stored decides where the rest of the bump goes

The share that comes out of rivals barely moves between categories. What changes completely is the quarter that does not, and one property of your product decides it. Where a shopper can store the pack, the elasticity splits 75 / 3 / 22: almost nobody new comes into the category, and the extra volume is existing buyers filling a cupboard. Where they cannot store it, the split is 75 / 17 / 8: the promotion brings in people who were not going to buy at all, and they buy a normal amount.

That one property changes what your promotion is even for. On a storable pack you are buying pantry space, which is worth something against a competitor and nothing as new demand. On a pack nobody can hoard you are buying an extra shopper, and that is the only stream that grows the category for you and the retailer at the same time.

Where promotions genuinely raise consumption, and where they only pull the same volume forward

The same work compared how much shoppers bought on deal against how long they then waited before buying again, which separates two outcomes that look identical on a lift report.

• They consumed more: bacon, crisps, soft drinks and yogurt. Volume rose and the gap to the next purchase held steady, so the extra units were eaten and drunk rather than stored.
• They only bought earlier and bigger: bathroom tissue, coffee, laundry detergent and kitchen towel. Volume rose and the wait to the next purchase stretched to match it.

Detergent is the cleanest illustration. On promotion, buyers took about 60% more of it, and then waited about 60% longer before buying again. The cupboard absorbed the entire difference.

⛔ One caution before you use that list as a verdict on your own category. The test only looks at people who bought, so it cannot see a promotion that brings a brand new household into the category. Ice cream shows no consumption effect on this measure while category penetration rises about 2% when it is promoted, which is real growth the test is blind to.

The spread is far wider than any single number admits

Across those 13 categories the switching share runs from 49% in butter to 94% in margarine. Two products a shopper will happily swap for each other, and their promotions do close to opposite things. Every benchmark on this page is the middle of a very wide distribution, and the only number that decides anything is the one from your own category.

Applying the split is easy. The discipline is in candidly setting the four shares for your category and event, rather than defaulting to the ~33% / ~33% / ~8% / ~26% averages.

The decomposition equation

Apparent Lift = Brand Switching + Acceleration + Cannibalization + Category Expansion

The share you actually get to bank as new volume is:

Net Incremental = Brand Switching + Category Expansion

Or equivalently:

Net Incremental = Apparent Lift x (1 minus Within-Brand Share)

Where Within‑Brand Share is acceleration plus own‑brand cannibalization (about 41% on the benchmark averages).

Worked numbers at the standard split

Take an event that shows a 2.0x lift on a baseline of 1,000 units per week, sustained over a 2‑week window. Apparent uplift is 2,000 units. Run the benchmark four‑way split (an illustrative calibration around the verified one‑third switching core, not a published set of numbers):

• Brand switching: 2,000 x 33% = 660 units (real, manufacturer wins)
• Acceleration: 2,000 x 33% = 660 units (timing‑shifted, dips next month)
• Cannibalization: 2,000 x 8% = 160 units (own‑brand swap, no net manufacturer gain)
• Category expansion: 2,000 x 26% = 520 units (real, retailer category grows)

Net incremental = 660 + 520 = 1,180 units (59% of apparent lift)

A 2.0x event report becomes a much smaller 1.59x event after credible decomposition. ROI math built on the 2,000‑unit lift is overstated by roughly 70% relative to the math built on the 1,180‑unit net.

Why the split is rarely the textbook split

Categories with long shelf life (pasta, detergent, multipack soft drinks) run higher acceleration shares because shoppers can stockpile. Categories with short shelf life (bakery, dairy, fresh produce) run lower acceleration but higher category expansion (the promo created an extra purchase occasion that could not be deferred). Heavy TPR runs higher within‑brand than equivalent display events because TPR mostly attracts existing buyers. New product launches run higher category expansion in the early weeks while distribution is still building.

The ROI consequence

Plug the decomposed lift into the standard ROI formula:

Promo ROI = (Net Incremental Volume x GP per unit minus Promo Cost) / Promo Cost

A 2.0x apparent lift on a 35% gross margin SKU at a 20% TPR depth that looks ROI‑positive on the apparent number frequently lands at negative ROI once the within‑brand 41% is stripped out. That swing is exactly why so many FMCG brands run promotional calendars that look healthy on event reports and look broken on the annual P&L.

An illustrative scenario in carbonated soft drinks. A premium brand with a 6‑can multipack at $5.99 shelf price runs two events at the same retailer in the same quarter. Same brand, same SKU, same retailer, same baseline. Both events show an identical 2.4x apparent lift on the post‑event report. The CMO is happy with both. The decomposition tells a different story.

Event A: 25% off TPR on the brand's hero SKU, 2 weeks, no display

Apparent lift: +1,400 units over the window. Apply a category‑calibrated decomposition for shelf‑stable mainstream FMCG (a touch heavier on within‑brand than the textbook split because multipack soft drinks stockpile easily in a pantry or garage):

• Brand switching: 1,400 x 28% = 392 units
• Acceleration: 1,400 x 38% = 532 units
• Cannibalization: 1,400 x 12% = 168 units (the brand has 4 other soft‑drink SKUs at the same retailer)
• Category expansion: 1,400 x 22% = 308 units

Net incremental = 392 + 308 = 700 units (50% of apparent lift)

Within‑brand share: 50%. The TPR is mostly subsidising loyalists who would have bought next week, plus stealing volume from the brand's value‑tier multipack on the same shelf.

Event B: 10% off plus gondola end display, 1.5 weeks

Apparent lift: also +1,400 units over the window. But the mechanic is very different: a shallower depth, with display support that intercepts shoppers who were not planning to walk the soft‑drinks aisle at all.

• Brand switching: 1,400 x 36% = 504 units (display puts the brand in front of competitor‑brand shoppers)
• Acceleration: 1,400 x 22% = 308 units (smaller, because the depth is too shallow to motivate stockpiling)
• Cannibalization: 1,400 x 8% = 112 units
• Category expansion: 1,400 x 34% = 476 units (display drove genuinely new occasions)

Net incremental = 504 + 476 = 980 units (70% of apparent lift)

Within‑brand share: 30%. The shallow‑plus‑display event delivered 40% more genuinely incremental volume than the deep TPR, despite the identical apparent lift.

Reading the comparison

The post‑event report would tell the CMO that both events were "+140% lift" wins. The source‑of‑volume decomposition tells the trade‑spend committee that one event delivered 700 real units and the other delivered 980. At the brand's $2.20 gross profit per unit, that 280‑unit gap is $616 of incremental gross profit per store, multiplied across 200 matched stores = $123,200 of profit difference between two events that scanned identically on the lift report.

The published split works as a calibration discipline for your in‑house decomposition, not a formula you apply to three decimal places. Treat it as the gravity check on whatever model your TPO team is running.

Three places the rule pays off

First, pre‑approval challenge. When a planner submits a projected ROI based on a 2.5x lift forecast, ask: "What is your assumed within‑brand share, and why is it different from the roughly 41% benchmark?" If they cannot answer, the projection is sitting on a number they have not interrogated. The split turns a vague forecast into a defensible one.

Second, post‑event reality check. The post‑event report shows a 2.2x lift. Apply a within‑brand haircut of roughly 41% as a sanity floor. Does the resulting net‑incremental number still look good? If the post‑event story falls apart at that haircut, the original story was probably overstated.

Third, category‑mix prioritization. Categories where the within‑brand share runs reliably above the 41% benchmark (heavy stockpiling, low brand‑switching propensity, narrow assortment) are categories where promotional ROI is reliably lower. That benchmark lets you flag those categories early in the annual planning cycle and steer trade dollars toward categories with more switching room.

Two questions that tell you which case you are in

Neither needs a new model, and most teams have never been asked either one.

Does our category store? If the pack sits happily in a cupboard for a month, expect your promotions to buy pantry space rather than new shoppers, and take category growth out of the business case unless you can show it. If the pack has to be used within the week, the reverse holds, and your display money is working harder than your depth money.

When we promote, do buyers wait longer before they come back? Pull the average gap between purchases for promoted weeks and for normal weeks. If the gap stretches by about as much as the volume rose, the promotion moved sales in time and created none. If volume rose and the gap held steady, people genuinely consumed more, and that is the event worth funding again.

Three places the rule misleads if used naively

First, new product introductions. Early in a launch, switching and category expansion both run far above the 33% norms because the brand has no existing buyers to cannibalize from. Applying these benchmarks to an NPD event will make the ROI look worse than it actually is.

Second, competitive defence events. When you are running a counter‑promo specifically because a competitor moved first, the brand‑switching share can spike to 60% or more (you are pulling shoppers back). The 33% switching number understates the real incremental lift in defensive moments.

Third, bonus‑pack and pack‑size mechanics. The split was estimated on price‑promotion data. Bonus packs and pack‑size moves change the consumption rate, not just the purchase decision, and have a different decomposition profile. Use the benchmark as a directional gut‑check on those mechanics, not as the actual decomposition.

How the rule travels with you

When you move from one company to another, or one category to another, the actual decomposition shares will differ. The discipline of making the share explicit is what travels. Senior RGM leaders who have internalised this split never ask "what was the lift?" without immediately asking "and what share of it was within‑brand?". That second question is the one that separates trade‑spend stewards from trade‑spend spenders.