FMCG
Marketing & Promotions
FMCG marketing teams fund a long tail of activations — display contests, retailer incentives, digital bursts, and national TV — but the feedback loop to incremental off-take is often slow and sliced by channel. Finance sees scheme liability; sales sees primary billing; brand sees reach. Few stacks answer, in one place, whether last month’s modern-trade promo actually moved secondary cases in the treated stores.
FireAI unifies scheme masters, DMS secondary feeds, SFA visit data, and optional media or agency exports into a single analytics layer. You ask in plain language — for example which SKUs gained numeric distribution in East but lost velocity — and get answers with charts tied to your own hierarchies (zone, territory, channel, outlet segment).
The outcome is not another marketing dashboard: it is decision-ready visibility on which promos paid back, where distribution quality is slipping before share does, and how calendar clashes between trade and consumer campaigns show up in off-take. See it with your data — book a demo.
Trade Promotion ROI & Incremental Lift
A volume spike during a scheme is meaningless if it is mostly forward buying or if control outlets would have grown anyway. Real promotion analytics needs treated vs control comparison, a clean washout window after the promo ends, and SKU-level granularity so you can see cannibalization inside your portfolio.
FireAI joins scheme definitions (eligibility, depth, duration, channel) to outlet-level secondary and, where available, SFA proof-of-execution. For each promo it surfaces baseline velocity, in-promo lift, post-promo washout, and incremental volume after statistical matching to similar non-treated outlets. You see which territories executed late, which batches of outlets never received the display condition, and where lift came from premium pack trade-up vs base pack only.
What you can ask FireAI:
- "What was incremental secondary cases for the GT 8% extra-margin promo vs matched control outlets in West?"
- "Show washout by week for the March modern-trade multi-buy — did velocity stay above baseline after week +4?"
- "Which ASMs have the largest gap between scheme-eligible outlets and outlets with confirmed display compliance?"
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Activation & Media Effectiveness vs Off-take
Digital and offline activations are often judged on impressions or spends; commercial success requires linking those inputs to secondary movement in the geographies and channels you actually funded. Without that bridge, you cannot defend budget reallocations between regions or between brand vs trade.
FireAI lets you ingest weekly or monthly spend / GRP / reach proxies (CSV, agency reports, or in-house sheets) and align them to your territory and SKU secondary series. You can compare pre/during/post windows, control for seasonality using prior-year or sibling-SKU baselines, and split results by channel (GT, MT, chemist, eB2B). Correlation is not causation — but FireAI surfaces where lift timing matches activation timing and where spend burned with flat secondary so you can investigate distribution or competitive interference.
What you can ask FireAI:
- "Did secondary in Tamil Nadu beverages accelerate in the two weeks after the regional digital burst vs Kerala (no burst)?"
- "Show correlation score between monthly media spend and secondary growth for hair care in MT"
- "Which territories had high activation cost but negative velocity change vs last quarter?"
Numeric Distribution, Weighted Distribution & Shelf Quality
Share battles are won in distribution quality — not just outlet counts. Numeric distribution (ND), weighted distribution (WD), and category-weighted measures tell you whether you are in the right stores with the right shelf muscle. Static monthly PDFs from research agencies rarely sync with your weekly sales review rhythm.
FireAI computes ND/WD from actual outlet lists and secondary in your DMS (and can blend in audit or SFA facings where you capture them). You can trend by SKU, pack size, and channel; slice by ASM; and flag outlets that are “present” but declining in velocity — an early signal before they drop off entirely. This connects directly to which promos or replenishment fixes will move the needle.
What you can ask FireAI:
- "Where did WD for 200ml SKU drop more than 3pp vs last quarter while ND was flat?"
- "List territories with the largest gap between ND and category-weighted distribution for snacks"
- "Show outlets that moved from ‘active’ to ‘no purchase in 45 days’ in Pune MT"
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Promo Calendar, Spend Pacing & Cross-Team Alignment
Overlapping schemes — consumer promo plus trade discount plus modern-trade national agreement — can cannibalize margin or confuse the field. Operations and sales often execute a different calendar than what marketing assumed in the annual plan.
FireAI can ingest your promo calendar (Excel or planning tool export) and reconcile it to live scheme parameters in DMS and billing. You see pacing of estimated promo liability vs budget, upcoming clashes by region, and historical overlap windows where incremental lift was weak. That turns the calendar from a slide deck into an operational control tower finance, sales, and marketing can share.
What you can ask FireAI:
- "Which weeks in Q2 had overlapping trade schemes on the same SKU in North?"
- "How does projected scheme liability for June compare to budget by category?"
- "Show regions where consumer promo start dates slipped vs planned SFA launch"