One team, thirty banners
At a large consumer goods manufacturer, a centralized revenue growth management (RGM) analytics team supported the company’s top 30 account banners. Its purpose was easy to state: take what had happened in market and turn it into insight an account team could use to change its commercial plan with a customer.
The insight was there to be found. The difficulty was getting it to the account team while it could still change something. Between the data becoming available and a plan changing stood a long run of manual preparation and handoffs, and each step waited for the one before it to finish.
Where the months went
The first stage was validation. Before any analysis could begin, the team checked the data for all 30 banners by hand and then loaded it into the company’s lakehouse. That stage took about three months.
Analysis came next and added another one to two months. It was skilled, careful work, and it was slow: producing a single analyst insight took about eight hours, a full working day.
Only then did results reach the account teams, at a quarterly readout. Each readout presented a rotating set of accounts rather than all of them.
Even the readout wasn’t the end of the line. The account team still had to update its commercial plan and then sell that plan to the customer’s buying team. Each of those handoffs added time on top of the preparation and the analysis.
What the wait cost
Preparation and analysis alone took four to five months from the moment the data was available. That is longer than a quarter. By the time an account’s results were presented, they described a promotion from well over a quarter earlier, and the plans made in between had been built without them.
The cost was not in the quality of the work. It was in the delay between analysis and action. An insight that arrives after the next plan has been set can inform the plan after that, at best. Meanwhile the analysts’ hours went into validating and loading data and building each insight by hand.
One week from the data
With Alakai, the path from data to recommendation takes a week. Within one week of the required data being available, Alakai produces the results and commercial plan recommendations and writes them back to the manufacturer’s trade promotion management (TPM) system. Producing an analyst insight takes about 10 minutes instead of eight hours.
The clock starts when the data is available, so the preparation that used to take three months is part of that week. Alakai’s commercial data platform brings a manufacturer’s TPM history, syndicated data where it has it, product, sales and financial data, and Alakai’s own retail pricing and promotion data into one model. It connects either through a scheduled data transfer or through federated access to the environment the team already runs, so the data doesn’t have to be rebuilt by hand before every cycle.
On that foundation, Alakai’s post-event promotion insights answer the questions an account team asks after every promotion. How was the offer structured to the shopper? How did it perform against the account’s own historical average, in incremental volume, incremental gross margin and net sales per trade dollar, and how did it look from the retailer’s side of the P&L? What were competitors running at the same retailer in the same window? And should the team stop, continue or accelerate it? Alakai runs that analysis across every account and sub-brand, rather than a rotating few each quarter.
The writeback matters as much as the speed. The recommendation goes into the trade promotion management system, where the team’s promotion plans are kept, rather than into a deck waiting for the next quarterly readout.
What stays with the account team
Alakai shortens the path to a recommendation; it doesn’t make the decision. The account team keeps responsibility for commercial decisions and for the conversation with the buyer. A recommendation is a starting point for that conversation, and the people who know the customer decide what to take into it.
That is also why the one-week clock stops at the recommendation. The customer accepting the plan is a separate step, and it depends on the buyer. The week measures what Alakai delivers, not how long a negotiation takes.
Before and after
| Measure | Before | With Alakai |
|---|---|---|
| From data availability to results and plan recommendations | 4–5 months: about 3 months of preparation, then 1–2 months of analysis | Within 1 week |
| Analyst effort per insight | 8 hours | 10 minutes |
| Recommendations written back to TPM | — | Included |
| Commercial decisions and the buyer conversation | The account team | The account team |
A dash means the historical baseline gives no figure.
What it means
For a team like this one, the difference is less about any single analysis than about when analysis arrives. Results and recommendations that took months after the data come within a week of it with Alakai, in the system where the plan is built. The account team receives recommendations that can change the commercial plan, with less delay between analysis and action, and the next promotion at an account can be shaped by what the last one showed.
How to read these numbers
The historical baseline covers a three-month data preparation stage and a one-to-two-month analysis stage across the top 30 account banners, which together make the four to five months. Analyst effort is the time to produce a single analyst insight, before and with Alakai. The one-week Alakai clock starts when the required data is available and ends with results and commercial plan recommendations, including TPM writeback; the customer accepting the plan is a separate step. This one week and the one week in the promotion evidence case study measure different deliverables, so they don't add up to a single timeline. "With Alakai" figures describe what Alakai delivers today.