Market scenario planning helps commercial teams prepare for changing market conditions before they commit to a commercial plan. For RGM teams, this means evaluating external scenarios such as rising input costs, category volume pressure, competitor price moves, retailer margin demands, new channel dynamics, or shifts in shopper demand.
Unlike commercial strategy testing, which usually starts with a planned action, market scenario planning starts with a change in the market. The question is not only "what happens if we change price?" but "what should we do if the market changes around us?"
Traditionally, market scenario planning has relied on historical performance, spreadsheets, and planning assumptions. Teams use last year's volume as a baseline, apply expected growth or decline rates, and adjust for known cost, pricing, or category trends. This can create a structured view of possible outcomes, but it often depends heavily on assumptions about the market, shopper behavior, and competitor moves. The challenge is that market conditions rarely change in isolation. Input costs may rise while volume is under pressure. A competitor lowers prices while retailers push for higher promotional intensity. When the market is volatile, and you need to model several simultaneous changes, traditional planning tools struggle to model how shoppers respond and which commercial actions best protect performance.
AI software lets RGM teams model changes to pricing, promo, portfolio, distribution, and trade terms before committing to an action. Instead of relying solely on historical trends or assumptions, teams can compare scenarios, understand where exposure lies, and identify the commercial actions that best protect performance.
Buynomics’ agent-based simulation, Virtual Shoppers AI, is the best approach to this.
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.
