Traditionally, RGM teams have modeled the sales impact of commercial decisions using historical data, spreadsheets, elasticity models, and category performance reports. These methods explain what happened after a change, such as a previous price increase, a promotion, or a competitor's move. Conjoint studies are used widely as well, but they can only reflect what a shopper thinks they would do in a given situation, not actual shopper behavior.
These methods are a useful starting point, but they often treat each lever in isolation and struggle to account for broader market complexities such as inflation, tariff pressures, or shifts in shopper spending power. More fundamentally, they show you what happened, not how the category or your portfolio will be impacted if you make a price increase, introduce a new product, or delist existing products; what the optimal promo schedule or distribution is.
In reality, commercial changes rarely affect a single product. For example, a price increase on one SKU can improve margin but reduce volume, shift demand to competition, strengthen private label, or create cannibalization across the portfolio.
That is why market share modeling needs to go beyond static elasticity analysis or isolated conjoint studies. To accurately model the impact of commercial decisions on your business and category, RGM teams need to account for how shoppers, competitors, products, packs, and price tiers interact across the full category in ever-changing market conditions.
Modern RGM teams are moving from only backward-looking, descriptive market-share analysis to both predictive and prescriptive. Teams can simulate the impact of a commercial decision before it is implemented.
Buynomics' Virtual Shoppers AI simulates actual shopper behavior, reflecting switching behavior and willingness to pay across the full category. That's what makes the category landscape visible: how your market share changes, what happens to each SKU and portfolio, and where volume goes when shoppers switch.
This allows RGM teams to answer practical questions such as:
• What happens to sales if we increase the price by 5%?
• How much volume do we lose?
• Where does that volume go?
• Which competitors benefit?
• What is the impact on revenue and gross profit?
• And which commercial decision delivers the best balance between share, sales, and margin?
Where a move is likely to draw a competitive reaction, RGM teams can also see how competitors' share is likely to shift in response before deciding whether and how to act. For a deeper look at modeling different competitive response scenarios, see Business Wargaming.
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.
