Before the analysis, the evidence
Every conversation about a promotion starts with a question that sounds simple: what did the shopper actually see? Before an analyst can say whether a promotion worked, or an account team can argue for running it again, someone has to establish what was on offer. That means the advertised price, the mechanic, whether a temporary price reduction, a multi-buy or a coupon on top, and exactly which items were included.
Get any of that wrong and everything built on it inherits the error. Lift is read against the wrong price, trade spend is judged against the wrong offer, and the recommendation that follows rests on both.
At a large consumer goods manufacturer, the revenue growth management (RGM) work depended on this evidence. Producing it consumed substantial resources, and all of that effort came before any analysis could begin.
Assembled by hand
The evidence was put together manually. Analysts gathered the feature ads to see what each retailer had advertised, and pulled syndicated average prices for the promoted period. They then matched the promoted items in the ads to the items in the sales data, and assembled the result into a record of how each promotion had run in market.
Each step is reasonable on its own. Together they make a workflow that grows with every retailer, every brand and every week, and that starts again each time a promotion ends. The sources don’t line up neatly, either. An ad describes an offer in the retailer’s own words; a syndicated average describes a price across a period and a set of stores. Matching one to the other takes judgement as well as time.
The cost showed up in two places: a data management tool to hold and organize the data, and the validation analysts who checked it. Between them, the workload ran to more than $1M a year. That money bought a record of what had happened. It didn’t yet buy any analysis of it.
The coupon gap
Even at that cost, the record wasn’t complete. Coupons were a gap in its coverage.
A coupon is part of the offer. It can change what the shopper pays and whether they buy, yet it may not appear in an ad’s headline price, and an average price for the week can’t show it as an offer of its own. With that gap in the evidence, teams had an incomplete view of the offers available to shoppers. When a coupon is missing from the record, whatever it contributed is credited to the rest of the offer, and an analysis can only be as complete as the record underneath it.
What Alakai does instead
Alakai replaces the manual assembly with collection. Every week it collects promotion evidence from retailer sites and standardizes it, so the same kind of offer is described the same way whichever retailer ran it. The evidence covers the manufacturer’s own brands and its competitors, and it includes coupons. The advertised mechanics sit alongside syndicated sales context, so the team can see what was on offer next to how it sold.
Standardized evidence is ready one week after the event runs, at less than one-tenth the cost of the manual workflow for comparable scope.
Alakai’s retail pricing and promotional data describes each offer at the UPC level. It records the full price cascade, from the shelf price through every available deal and coupon stack, along with the tactic type and the overlay structure: a BOGO, a 2-for-$X, a temporary price reduction, a digital coupon or a loyalty requirement. Competitors’ promotions at the same retailer, in the same window, are in the same data set on the same timeline. The question of what else was competing for the shopper that week is answered from the same record as the question of what the manufacturer ran.
Because the evidence arrives structured, it can go wherever a team already works, from a spreadsheet to a data lake. It is also where Alakai’s own workflows begin. Promotion validation is pre-filled with it, so an account team confirms what ran rather than rebuilding it, and post-event insights use it to show how each offer was structured to the shopper.
Before and after
| Measure | Before | With Alakai |
|---|---|---|
| Cost of assembling promotion evidence | More than $1M a year, for the data management tool and validation analysts | Less than one-tenth, for comparable scope |
| How the evidence is gathered | By hand: feature ads, syndicated average prices, item matching | Collected weekly from retailer sites, then standardized |
| When the evidence is ready | — | One week after the event runs |
| Coupons | A gap | Included |
| Competitors’ offers | — | Included |
A dash means the historical baseline gives no figure.
What it means
Less than one-tenth the cost, for comparable scope, means more than 90% lower. That is the headline, but the money is only part of the change.
With Alakai, a team spends less effort assembling promotion evidence, and it has a fuller record of the offers that were in market: its own and its competitors’, with the coupons this team’s record was missing. Time that went into gathering ads and matching items can go into the question the evidence was always meant to answer: did the promotion work, and what should happen next? When that answer comes, it rests on the offers shoppers actually saw.
How to read these numbers
The $1M+ annual baseline covers the data management tool plus the validation analysts. Alakai delivers standardized promotion evidence, coupons and competitive offers included, within one week of the event at less than one-tenth the cost of the manual workflow for comparable scope (more than 90% lower). "With Alakai" figures describe what Alakai delivers today.