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™.
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™.
Advanced Price Elasticity Modeling quantifies how sensitive customer demand is to price changes, while accounting for competitor actions, promotions, and other market dynamics. We employ techniques ranging from classical econometric models (e.g. log-log regressions or Elasticity via ElasticNet) to cutting-edge machine learning (e.g. random forest or causal ML approaches) to isolate the impact of price on volume and revenue[20]. In practice, these models answer questions like “If we raise price 5% on Product X, how much volume might we lose?” or “Which competitor’s price drop would steal significant share from us?”[21]. By understanding these demand curves at a granular level, your team can set optimal list prices, discount thresholds, and promotional strategies with far greater confidence.
Know exactly how each product, customer segment, or channel will respond to different price points. This clarity empowers data-driven decisions on when to take markups or markdowns, backed by quantified demand responses.
Avoid harmful price moves by quantifying “volume hurdles.” Elasticity models reveal when a price cut would require unrealistic volume gains to break even, or conversely, identify how much volume loss a price increase would likely cause. This helps you set safe boundaries for pricing actions to protect margin.
A sophisticated elasticity analysis lets you anticipate competitor reactions and plan defense or offense accordingly. Knowing your own-price and cross-price elasticities means you can predict how a competitor’s price change might affect your sales – and prepare a response in advance.
Our modeling doesn’t stop at just “own-price” elasticity. We incorporate cross-price elasticity (how substitutes affect your demand), competitive price index impacts, and even promotional lift factors for a full picture of market dynamics. This holistic approach ensures your pricing strategy considers all major demand drivers, not just your own pricing in isolation.
We build elasticity models in a collaborative and transparent manner so that your organization truly owns the insights:
We start by examining all relevant data – e.g. transaction sales data, historical pricing and discount records, competitor price tracking, promotional calendars, and market data from retailers or syndicated sources. Based on this, we define the scope of modeling (which product lines, time horizons, competitor set, etc.) and ensure data quality and granularity are sufficient for robust analysis.
Next, we co-develop a modeling blueprint outlining candidate variables, the model methodologies to be used (regression vs. machine learning, or a hybrid), and the specific business questions the model will answer. This step secures stakeholder alignment and buy-in before any heavy analysis begins.
Using open-source tools (Python, R) or integrated analytics within your BI platform, we build the elasticity models according to the agreed blueprint. We favor transparent, reproducible code so that your analysts can understand and adjust the model over time. Each model is tailored to your data availability and can range from simple regression to complex non-linear ML models as appropriate.
We rigorously validate each model’s accuracy against holdout historical data and real-world outcomes. Once validated, we conduct scenario analysis (“what-if” simulations) – for example, modeling the impact on volume and profit if prices were 3% higher or lower – to ensure the model’s predictions are sensible and to help calibrate decision rules.
Finally, we deliver the models along with detailed documentation and hands-on training sessions. Because you own the underlying model code and it runs in your environment, you won’t be dependent on external vendors for updates or ongoing analysis – your team gains the capability to maintain and extend the elasticity modeling in-house.
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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