Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities, fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
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Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities, fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
We would love to hear from you.
Let’s chat!
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Subsidizing baseline volumes is where trade spend is most often wasted. Traditional analysis runs once a quarter; by then, the calendar has already moved on. Revology co-designs the promo ROI engine with your trade marketing and sales finance teams and builds it in your own data warehouse and BI tools. The AI agents score each planned promotion against a baseline volume model and incrementality engine, flag events likely to discount volume that would have sold anyway, and close the loop with post-event ROI for the next cycle. Your trade marketing lead approves every recommendation. For mid-market companies ($100M–$2B), the result is a trade workflow your team owns outright, stronger promo governance, and 200–400 bps of gross profit as the typical year-one outcome.
Promo ROI should be scored before launch and audited after the event. Revology co-designs and builds AI promo agents that estimate incrementality, flag dilution, and update the playbook every cycle.
Identify which promotions deliver the highest true lift in sales or profit and cut the underperforming ones. By reallocating budget from low-ROI deals to proven winners, you get more impact from the same spend.
The analysis pinpoints "pass-through" leakage: cases where discounts or rebates never reach the end consumer, for example a funded retailer program that never turns into shopper savings. Plugging these leaks reclaims margin that was being lost in the channel.
Armed with data on what works and what doesn't, your team negotiates with key retailers and distributors on facts. Joint business planning improves as you align promotional calendars with partners on mutual ROI, and trade terms can move toward pay-for-performance.
With an ML-driven promotion simulator, you can predict the likely outcome of a promotion before committing trade funds[58]. This takes the guesswork out of planning – you can forecast, for example, that a 2-for-1 deal in Q3 would cannibalize too much base sales, or that a 15% discount in December would yield a positive ROI given seasonal lift. Such foresight minimizes risk and surprises[59].
We build the promo ROI engine end-to-end in your environment, co-designed with your trade marketing, sales, and finance teams, in these steps:
We start by auditing your current promotional data and processes: where the data lives (shipments, point-of-sale, TPM accruals, trade budgets), which metrics matter (incremental lift, profit uplift, ROI), and the scope of the build. We make sure finance, sales, and trade marketing agree on what "effective" means for your business, including ROI thresholds and incremental volume targets.
We integrate and harmonize all relevant data sources – internal shipment or sales data, syndicated retail sales data, retailer loyalty/POS data, and any available competitor or category benchmarks[61]. This unified dataset provides the 360° view needed to assess promotions accurately.
Depending on data complexity, we apply the appropriate modeling approach to measure promotional lift. For some clients, a regression-based method suffices to estimate baseline vs. incremental sales; for others, we use more advanced machine learning models to capture non-linear effects[62]. We account for factors like seasonality, cannibalization, and competitor activity to isolate each
We then build a scenario-planning module accessible through an intuitive interface (e.g. in Power BI, Tableau, or a custom web app)[63]. Users can tweak promotion parameters – timing, discount depth, product mix, in-store support (features/displays) – and immediately see the forecasted impact on volume, revenue, and profit[64]. This interactive “sandbox” allows your trade marketing or RGM team to test and refine promo plans before execution.
Finally, we train your RGM, sales, and finance teams to interpret the results, run the monthly review, and maintain the models as markets evolve. Because the entire platform runs in your own environment, there is no license fee, no per-seat license, and no black box: your team owns the code, the models, and the IP. If you prefer, an optional managed-services agreement at a modest monthly fee keeps the platform maintained and evolving after handoff.
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