What is prescriptive analytics in revenue growth management?
Predictive analytics answers "what happens if." You define a change, such as a 5% price increase or a new pack size, and the model forecasts its impact on volume, revenue and profit. Prescriptive analytics works the other way round. You start with the outcome you need, such as a revenue target or a margin floor, and the system recommends the actions most likely to get you there.
Buynomics does both on the same model. Virtual Shoppers AI simulates millions of individual buying decisions based on actual shopper behavior. That makes every recommendation account for how shoppers really respond, including cannibalization across your portfolio and reactions to competitor prices. RGM teams can test their own hypotheses in scenario planning, then switch to prescriptive mode to find the options they hadn't thought to test.
How does Buynomics recommend the best pricing actions?
You set the KPIs you want to improve, for example, revenue and profit, and the business rules you have to respect: price ranges, KPI thresholds, and product groups that must stay unchanged. Buynomics then searches the possible price combinations within those rules. It runs hundreds to thousands of scenarios, far more than a team could build by hand, and shows you the best-performing options along an efficient frontier.
Each option shows its impact on your KPIs, which products change price and by how much, and where volume is gained or lost. Because the search stays within your constraints, every recommendation is a move you could actually take to a retailer or your leadership team. Work that used to mean building dozens of scenarios in spreadsheets becomes a single run with a ranked set of answers.
Does Buynomics give one answer, and can I trust how it got there?
Buynomics gives you a ranked set of options rather than a single number, and the final decision stays with your team. RGM decisions involve trade-offs between revenue, profit, volume and market share, so seeing several strong options side by side lets you choose the balance that fits your strategy, channel or customer.
Every recommendation can be traced back to the simulated shopper decisions behind it. You can see which products drive the result, how much volume moves between your own SKUs, and how shoppers switch to or from competitors. That transparency is what lets commercial teams defend a recommendation with sales, finance, and retail partners, not just accept a black-box output.
.svg)
