Traditionally, commercial strategy testing meant relying on spreadsheets that only look backward, or on methods that examine a single RGM lever in isolation, such as static price-elasticity lists. Taking this approach alone provides limited visibility into cross-effects, cannibalization, or interactions with competitors.
Analyzing past performance is a necessary starting point; the challenge is that market dynamics continuously shift, driven by changing shopper preferences, competitive moves, and economic pressures such as tariffs. Tools that are only backward-looking struggle to account for that context and accurately predict what will happen.
Modern RGM teams are moving beyond commercial strategy testing with traditional tools toward AI-driven simulation. This advancement in RGM maturity allows teams to test commercial strategy holistically across all RGM levers, account for cross-effects and cannibalization, and tailor RGM strategies to actual KPI targets, taking into account business constraints and strategies.
Where traditional methods draw on past performance to explain what happened, AI-driven approaches guide what to do next and how to reach your goals.
At Buynomics, we believe agent-based simulation is the best way to test commercial strategies with minimal risk. Agent-based simulation simulates large numbers of individual agents, each with its own preferences and rules, making their own decisions, which allows for forecasting shopper decisions at scale with the highest accuracy
The Buynomics platform combines Virtual Shoppers AI, a natural language interface, and agentic AI, enabling autonomous commercial decision-making and helping organizations move from “what if” to “what is best.”
Buynomics' Virtual Shoppers AI is grounded in behavioral economics and decision theory. The model is trained on real-world data, including transactional data, surveys, and conjoint studies, to create 100,000s Virtual Shoppers that replicate how your shoppers make purchasing decisions while accounting for market context, competitive dynamics, and cross-category effects.
RGM teams define the hypothesis they want to test, whether it's a change to their pricing, PPA, promotions, mix, or trade terms, or a competitor action, such as a new product launch, price cut, or increased promotional spend. Both can be modeled directly, allowing teams to evaluate their own next move and anticipate how the market will respond to a competitor's. During each simulation, these virtual shoppers make purchasing decisions through the following process:
The model breaks down products into key value drivers and attributes that influence purchasing decisions.
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The model analyzes the data and identifies the willingness-to-pay (WTP) distribution for product value drivers.
Through data updates, it continuously learns and adapts to different market factors.
Each time you run a “what if…” simulation in the Buynomics platform, virtual shoppers are making decisions holistically - accounting for all RGM levers, competitors, and trends/seasonality (incl. special purchasing moments, e.g., Christmas).
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Accurate shopper behavior prediction results in 2-4%* profit impact.
When presented with product offers, Virtual Shoppers can replicate actual buying patterns with 95%*accuracy.
Reduce the time spent on manual analysis by up to 80%. Integrate various data sources and get best-in-class predictive accuracy.
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