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™.
Promotion Effectiveness & Optimization focuses on evaluating your trade promotions – e.g. discounts, in-store displays, special offers, retailer features – to determine which activities truly drive incremental volume, revenue, and profit[52]. We use advanced analytics (from regression-based sales lift models to machine-learning forecasts) to separate “baseline” sales from incremental promotional gains, and we deliver these insights via a user-friendly dashboard or app[53]. In practice, this capability has two components:
In short, Promotion Effectiveness & Optimization solutions help ensure every trade dollar is spent wisely – doubling down on high-ROI promotions and eliminating those that don’t pay off.
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.
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.
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.
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 implement psychological pricing enhancements in a methodical way to ensure they genuinely drive results and fit your brand
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).
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 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].
When researching successful dynamic pricing strategies, most case studies fall into one of two camps. One reads like a vendor brochure: uplift numbers from unnamed
This guide provides pricing, RGM, category, and commercial finance leaders with practical methods to measure and address substitution and complement effects across a product portfolio.
Most CPG RGM teams don’t have a data problem. They have a navigation problem. Join Armin Kakas and Enrico Sieni for a 60-minute educational webinar on the modern CPG Pricing & RGM Analytics Navigator. Six modules, five narrow AI agents, the 120-day pilot path. Every registrant gets the whitepaper the week after.
This guide provides CFOs, CCOs, pricing leaders, and RGM teams with a credible approach to demonstrating pricing impact, from baseline design through to booked P&L
A Fortune 500 global data-storage OEM was bleeding margin in its $200M U.S. B2C hard-drive business. One flagship family had taken a substantial net-pricing hit year-over-year, and roughly 45% of historical promos were returning only 0 to 20% ROI. Revology rebuilt the pricing and promotion analytics from the ground up using causal Double Machine Learning, a retailer-math ROI model, and a three-archetype segmentation framework. The target: $3M to $6M of incremental EBITDA (a 10x to 20x return on the engagement) within 12 months.
A Fortune 500 global pharmaceutical manufacturer was making emerging-market pricing decisions by feel. We built a repeatable Pricing Quick Wins engine across four pilot markets, grounded in causal elasticity modeling, automated competitive equivalence mapping, and price-pack architecture and inflation-aware simulators. The pilots identified around $8M of median revenue opportunity, with a best-case of ~$12M. Local teams now own the engine and can repeat the analysis annually as inflation and the competitive set shift.
Centered on proven best practices, Revology Analytics® provides Revenue Growth Analytics advisory services and thought leadership, driving profitable revenue growth for middle-market companies.
Over 225 companies took our scorecard to improve their Revenue Growth Analytics & Management capabilities.
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