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Prescriptive AI for RGM

Set your goal. Find the best way to reach it.

Scenario testing tells you what could happen. Buynomics' prescriptive layer tells you what's best, ranking actions against your KPIs. Every recommendation is built on actual shopper behavior, with the Virtual Shoppers AI simulating millions of individual buying decisions.

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How Buynomics Prescriptive AI Work for Pricing, PPA, and Promotions

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1. Define

The KPIs you're steering toward, the constraints you have to respect, the guardrails you can't cross. Same input structure whether you're working on pricing, portfolio, or promotions.

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2. Simulate

In a single Discovery, Virtual Shoppers AI simulates millions of individual buying decisions, testing the full range of options.

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3. Explore

The recommendations for pricing and top-scoring scenarios for portfolio and promotions come back with their trade-offs visible, ready to take into a plan or a customer conversation. Addressed across channels and occasions, including cannibalization effects.

Purpose-built Capabilities for Three Core RGM Levers

Actions Discovery

Scope the PPA, price, and availability changes; specify your KPIs, constraints, and guardrails. Every scenario is scored against two selected KPIs, and the top results are returned as recommendations.

  • In a single Discovery, Virtual Shoppers AI simulates millions of individual buying decisions across up to 500 different scenarios.

  • Test single SKUs or entire product groups to see cannibalization and cross-effects at the portfolio and category levels.

Pricing Decision Guide

Define the KPIs you're steering toward, along with the constraints and guardrails you must respect. The decision guide will search through millions of pricing combinations and rank the options most relevant to your KPIs.

  • Receive tailored pricing recommendations that impact your top line with unmatched prediction accuracy.
  • Include competitive scenarios to understand the best response strategy for gaining market share and increasing your profits.

Promotions Discovery

Input your target products, trade budget, and promotional windows. Promo Discovery tests discount depth in combination with frequency, visibility, and distribution to find the optimal promotion calendar for your KPIs.

  • Across up to 500 scenarios, Virtual Shoppers AI simulates millions of individual buying decisions to score every version of the calendar.
  • What comes back is a ranked set of complete promo calendars, each scored and concrete enough to act on.

What Our Customers Say About Buynomics

andrea

"Buynomics stands out for its accuracy, speed of analysis, and rapid implementation. We deployed it in just eight weeks - far faster than other tools that often take three months or more."

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Andrea Pezzillo

Head of Category and Revenue Growth Management at Danone

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"Buynomics makes scenario planning effortless. The ability to compare multiple pricing and portfolio scenarios side by side, right next to our current setup, gives us instant clarity and confidence in our decisions."

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Michael Giambra

Head of Commercial Strategy at pladis Global

Emma Swiers Dr Oetker
"Buynomics helped us to achieve incremental revenue and make faster, quicker, and smarter decisions that are very data-driven."
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Emma Swiers

Executive Manager Net Revenue Management at Dr. Oetker

Base Your Decisions on Your Shoppers' Behavior

Buynomics’ Virtual Shoppers AI was developed to serve as the foundation for all questions of customer insights and revenue management

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Replicate Shopper Decisions with Precision

The Virtual Shoppers AI creates Virtual Shoppers that replicate shopper buying behavior—accounting for cannibalization and interactions with competitor products.

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Leverage All Your Data Sources

Virtual Shoppers AI technology can integrate all your relevant data, from sales and transaction data to customer surveys and behavioral pricing insights, to account for changes in preferences over time or to understand the ongoing influence of external effects (e.g., inflation) on purchasing decisions.

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Up and Running in Less Than 3 Months

Enjoy a world-class onboarding experience: After 3 months, you'll have an AI-powered platform that shows you exactly what your customers will buy. With less than a week of internal resources, guided by a dedicated Customer Success Manager.

 

Buynomics Receives 2025 POI Best-in-Class Category Distinctions

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Make better RGM decisions, faster!

Run agent-based simulations with Buynomics’ Virtual Shoppers AI to optimize all revenue levers, capturing cross-effects, cannibalization, and competition.

2-4%

Profit impact*

95%

Predictive Accuracy*

80%

Faster Decision-making

*Depending on data quality and completeness

FAQs

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.

What is prescriptive analytics in revenue growth management, and how is it different from predictive analytics?

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?

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?

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.