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TPM INSIGHTS
Why Trade-Promotion Transformations Fail Before They Start
It's rarely the technology or the strategy. It's the data foundation underneath and the order you build in. by Peak & Peak TPM Experts, 2026
TPM INSIGHTS
It's rarely the technology or the strategy. It's the data foundation underneath and the order you build in. by Peak & Peak TPM Experts, 2026
Ask a Consumer Goods commercial leader where their trade-promotion programme could improve, and you'll hear a familiar wishlist: better analytics, smarter optimization, maybe an AI agent recommending the next-best promotion. Ask why the last transformation didn't deliver, and the answers get vague. The tools were fine. The strategy made sense. And yet the needle barely moved.
That gap keeps recurring across the industry and the reason isn't the one most roadmaps assume.
Trade promotion is the second-largest cost line on a typical Consumer Goods P&L behind only cost of goods, at roughly a quarter of gross sales. It's also the only major line a manufacturer fully controls. COGS moves with commodities. Trade spend moves with decisions made inside your commercial team.
That's what makes the waste so frustrating. Industry research puts trade spend at 20–30% of gross sales, and the consensus is that 20–30% of it earns no positive return. For a mid-size manufacturer, that's tens of millions of euros leaving the business every year with no reliable ROI and recovering even a few points lands straight on EBIT.
So companies invest to fix it. And often, the investment underdelivers.
When trade-promotion transformations stall, it's usually one of three things and notice that none of them is the software:
Scope collapse: Eighteen-month mega-projects that miss go-live, or land so late the business has already moved on. Around a third of CG companies still plan in spreadsheets with no predictive models often after a programme meant to fix exactly that.
The data-foundation gap: Only about 30% of CG companies have cleansed, harmonised data. Deploy tools on the other 70%, and the insight is unreliable from day one.
Adoption breakdown: Key Account Managers revert to Excel when the system wasn't built around their workflow. Nearly half still recycle last year's promotional plan.
Look closely and these aren't three problems. They're three symptoms of one: building in the wrong order. Scope collapses when too much is attempted at once. The data gap opens when capability is layered onto a foundation that was never laid. Adoption breaks when the system reaches users before it fits them.
Here's where it gets sharper. When trade ROI is poor, the instinct is to reach for the most advanced capability on the shelf. Today that means AI agents that flag the promotion about to lose money, or chase down the deductions quietly eroding your return.
Those agents are real, and they're powerful. But an agent is a confident reader of whatever data it's handed. On harmonised, well-modelled trade data, it's a force multiplier. On fragmented spreadsheets, it's a fast route to confident, wrong answers.
That's the uncomfortable truth behind a lot of stalled "AI-first" ambitions: AI doesn't rescue a transformation built in the wrong order. It inherits the problems and amplifies them. The data foundation isn't the boring prerequisite you get past on the way to the interesting work. It is the work that makes the interesting work safe.
The industry has a useful mirror here. The Promotion Optimization Institute frames RGM maturity as a progression: from cleansed, harmonised data, through predictive and diagnostic analytics, to prescriptive, anticipatory AI. It's worth holding yourself against it honestly, because most organisations rate themselves a rung or two too high. A company running predictive promo models on data that isn't harmonised isn't "analytical." It's foundational, with analytics bolted on, and the cracks show up later as forecasts no one trusts.
The maturity model tells you where you are. It doesn't tell you how to climb. For that you need a delivery sequence, not a diagnostic, and the two should never be confused.
The companies that pull ahead in trade promotion don't start with an eighteen-month programme. They start with two modest, decisive moves: an honest read of where they sit on the maturity curve, and a fixed-scope first phase that earns trust in four to six months getting master data, promotion planning, real-time P&L and approval workflows in place, so no spend leaves the building without a forecast, an owner and an approval path.
It sounds less ambitious than "AI-powered RGM optimisation." It's also the thing that makes AI-powered RGM optimisation possible later on a foundation that's already paying for itself.
Trade promotion is the most controllable line on your P&L. The reason it so rarely gets controlled isn't a missing tool or a missing strategy. It's the order of operations. Get that right, and the rest compounds.
Trade promotion is ~20–30% of gross sales and the only major P&L line you fully control, yet 20–30% of that spend earns no positive return.
Transformations fail from sequence, not software: scope collapse, an unaddressed data gap, and poor adoption all trace back to building in the wrong order.
Only ~30% of CG companies run on cleansed, harmonised data, AI deployed on the other 70% amplifies errors instead of fixing them.
Win by starting small: an honest maturity read, then a fixed-scope first phase (4–6 months) covering master data, planning, real-time P&L and approvals.
Peak & Peak is a boutique Salesforce consultancy dedicated entirely to Consumer Goods and Trade Promotion Management. Our whitepaper Peak Pathway TPM sets out the full sequenced path (Foundation, Scale, Optimize) on Salesforce Consumer Goods Cloud. Get the Free Whitepaper here
Why do most trade-promotion transformations fail? Not because of the technology or the strategy, but because of the order of operations. When the data foundation isn't laid first, scope collapses, insights become unreliable, and users abandon the system.
How much of trade spend is wasted? Trade spend typically runs 20–30% of gross sales in Consumer Goods, and industry consensus holds that 20–30% of it delivers no positive return.
Does AI fix a struggling trade-promotion programme? No. AI agents amplify whatever data they're given. On harmonised data they multiply value; on fragmented spreadsheets they produce fast, confident, wrong answers. The data foundation has to come first.
Where should a Consumer Goods company start? With an honest read of RGM maturity, followed by a fixed-scope first phase (four to six months) that puts master data, promotion planning, real-time P&L and approval workflows in place — before scaling toward optimization and AI.
Bain & company; McKinsey & Company, BCG, POI Industry Report and industry reports (2023–2025) on trade promotion performance, digital maturity, and FMCG retail execution trends.
Reinventing Commercial Excellence: What Every CG Leader Should Know Webinar by Peak & Peak.
PEAK TPM PATHWAY Whitepaper by Peak & Peak
