idcom.ai

For Trade Marketing

Execution excellence, measured — not assumed.

Which promotions created value, and what to do about the ones that didn't.

A causal baseline that separates real lift from what would have sold anyway, mechanic rankings and best promo weeks, retail execution captured in the aisle, and an action list that ends in Stop, Increase, Decrease or Maintain — per customer.

What you'll surface

The questions trade marketing argues about.

None of them are settled by a post-promo recap deck. They are answered when the promo calendar, sales, cost and in-store execution data read from the same canonical model — so the lift you claim is the lift that survives the review.

  • How much of that promo was truly incremental — and how much would have sold anyway?
  • Which mechanics and weeks deliver the best return for each sub-channel?
  • Did the promotion create halo, or just cannibalize a sibling SKU?
  • Is the planogram actually being executed in store?
  • Which customers deserve the next investment?
  • For this account, do we stop, deepen, trim or hold?

What IDCOM.ai delivers

What IDCOM.ai delivers to trade marketing.

Six things the trade marketing seat needs from an intelligence operating system — each one running in production today, in the modules named below the card.

Promotion effectiveness

A causal baseline — TSB, Holt-Winters or Holt/SES chosen by how the series actually behaves — separates true incremental volume from the baseline that would have sold anyway, with halo and cannibalization attributed and a validation tab to check the work.

→ Promotions

Retail execution

Field surveys capture planogram compliance, pricing audits and competitor scans — geo-tagged, photo-backed and risk-scored — so execution is evidence rather than anecdote.

→ Surveys

Channel intelligence

Mechanic rankings, consumer mechanics, best promo weeks and a brand × sub-channel heatmap — so next year's calendar is built on what works, not on what was done last year.

→ Promotions

Distribution optimization

Numeric (ND%) and weighted (WD%) distribution per brand, coverage against last year's buying universe, and a gap table of customers missing your top brands — the white space, quantified.

→ Customers · Products

Customer prioritization

Non-promo vs promo KPIs per customer with one clear action tag — Stop, Increase, Decrease or Maintain — turning the whole analysis into a single next step per account the field team can act on.

→ Promotions

Promo ROI

ROI on a unified promo-investment denominator, elasticity gated by an R-squared floor so untrustworthy fits never reach the optimizer, and a recommendation for the shallowest profitable depth — never a reflexive deeper cut.

→ Promotions

Ready to see it on your data?

Request a demo and we'll walk you through the modules with sample data shaped like yours.

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