Traditionally, business wargaming meant holding facilitator-led workshops, whiteboard exercises, and hypothetical scenarios that produced directional insights but were difficult to quantify. During these exercises, teams look at what happened the last time prices changed, which products lost volume in a previous competitive cycle, or how shoppers responded to past promotions.
These exercises can generate useful hypotheses, but they remain largely descriptive. In an increasingly volatile, uncertain, complex, and ambiguous (VUCA) environment, historical patterns are becoming a less reliable guide to future outcomes.
You can learn more about VUCA in our webinar, linked below: Data-Driven RGM with BIC: Turning Insights into Action.
Modern AI-driven business wargaming enables teams to analyze what has happened and guide their decision on what to do next. Using agent-based simulations, organizations can test potential commercial decisions before implementation and explore how shoppers, competitors, and retailers will respond to changes such as price changes, promotions, portfolio changes, and distribution.
Organizations are increasingly complementing these approaches with AI-driven decision support. Different AI technologies can be used depending on the problem being solved. For modeling complex market interactions and shopper behavior, Buynomics believes agent-based simulations offer a particularly powerful approach.
When grounded in behavioral economics and trained on real-world market data, agent-based simulations can create a digital representation of shopper behavior.
This allows organizations to simulate how their individual shoppers, products, competitors, and retailers interact within a dynamic market environment. RGM teams can then wargame commercial decisions across all RGM levers and evaluate the impact on units, revenue, profit, and market share, accounting for cross-effects across the full category.
The same logic applies in reverse. If a competitor introduces a new product or adjusts pricing, teams can model that change directly and see how shoppers respond and where volume shifts. They can then evaluate the best course of action, whether that means adjusting their own strategy or doing nothing at all.
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.
.svg){kind=icon}
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).
.svg){kind=icon}
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.
