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CAPABILITIES

Promotion Effectiveness Optimization — Incrementality You Can Defend

Where Mid-Market Trade Spend Often Goes to Die

Promotional incrementality is where mid-market trade spend often goes to die. Traditional analysis runs once a quarter; by then, the calendar has already moved on. Revology co-designs AI agents alongside your trade marketing team that 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 analysis for the next cycle. For mid-market companies ($100M–$2B), the result is a trade workflow your team owns, with stronger promo governance and 200–400 bps gross profit upside when paired with channel pricing.

What it is

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.

Woman using tablet for dynamic pricing analysis in retail setting.

How It Benefits Clients

Maximized ROI

Identify which promotions deliver the highest true lift in sales or profit and cut the underperforming promotions[55]. By reallocating budget from low-ROI deals to proven winners, you get more impact from the same spend.

Reduced Wasted Spend

The analysis pinpoints “pass-through” leakage – cases where discounts or rebates never really reach the end consumer (for example, funding a retailer program that doesn’t translate to shopper savings)[56]. Plugging these leaks can immediately reclaim margin that was being lost in the channel.

Stronger Retailer Partnerships

Armed with data on what works and what doesn’t, your team can collaborate with key retailers and distributors using facts[57]. Joint business planning improves as you align promotional calendars with partners based on mutual ROI, which strengthens those relationships.

Future-Ready Planning

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].

Our Approach

We implement psychological pricing enhancements in a methodical way to ensure they genuinely drive results and fit your brand

1
Diagnostic & Requirements Workshop

We start by auditing your current promotional data and processes. This includes mapping where all the data resides (shipments, point-of-sale, spending budgets, etc.), defining key metrics (incremental sales lift, profit uplift, ROI), and outlining the scope of the TPE/TPO solution[60]. We make sure everyone agrees on what “effective” means for your business (e.g. ROI thresholds, incremental volume targets).

2
Promotional Data Integration

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.

3
Analytical Model Development

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

4
Optimization Engine & Dashboard

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.

5
Training & Ownership

Finally, we train your RGM, Sales, and Finance teams to interpret the results and maintain the solution as markets evolve[65]. Because the entire platform runs in your IT environment, you avoid recurring vendor fees or black-box tools – your team can adjust assumptions or add new promotions and continue to get value as conditions change[65].

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Who We Are

Who We Are

Analytics Platform for Dynamic Pricing & RGM.

Promotion Effectiveness & Optimization

Frequently Asked Questions

How does AI-built incrementality differ from traditional uplift analysis?

Traditional uplift analysis compares promo periods to a simple baseline. AI-built incrementality controls for confounders — seasonality, competitor activity, pantry loading — using methods like Double Machine Learning or causal forests. The result is a defensible incrementality number, not an inflated one.

What does the promo agent recommend?

Allocate, redesign, or kill — for each planned promo, the agent estimates incremental volume, margin impact, and confidence band, and surfaces the recommendation to the trade marketing analyst.

How fast does the engine learn?

Each promotional cycle adds new training data. Most clients see meaningful recommendation sharpening by cycle three or four.
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