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NIQ Launches AI-Powered Platform to Help Brands Test Pricing Decisions Before They Go to Market

New AI-enabled solution replaces fragmented tools with simulation-driven pricing and promotion decisions before execution.
´º½ºÀÏÀÚ: 2026-05-19

CHICAGO -- As manufacturers face mounting pressure to protect margins, justify trade spend, and respond faster to shifting consumer demand, NIQ (NYSE: NIQ), a global leader in consumer intelligence, today announced the commercial launch of Price & Promo Optimizer, a next‑generation, AI‑enabled Revenue Growth Management (RGM) platform built to fundamentally change how pricing and promotion decisions are made.

Today, many pricing and promotion decisions are still made across disconnected tools, manual workflows, and siloed teams—slowing execution, increasing risk, and limiting the ability to validate strategies before they reach the market. Price & Promo Optimizer replaces this fragmented approach with a united platform that brings pricing, promotion, and trade strategy into a single workflow.

Powered by NIQ’s trusted store‑level measurement data and advanced analytical models, Price & Promo Optimizer enables category managers, RGM leaders, marketing, and commercial teams to simulate scenarios, quantify trade‑offs, and understand the impact of decisions on volume, revenue, margin, and category performance, before they enter retailer negotiations or go to market.

“Pricing and promotion decisions have become one of the most critical growth levers for manufacturers, yet too many teams are still forced to make those calls with fragmented data and outdated tools,” said Martin Hernandez, SVP NIQ. “With Price & Promo Optimizer, NIQ is helping brands move from reactive decision-making to proactive, scenario-based planning. By bringing price elasticity, promotional effectiveness, and trade spend optimization into one AI-enabled platform, we enable teams to move faster, protect margins, and approach retailer conversations with greater confidence.”

In a landscape where decisions must be made with increasing speed, accuracy, and precision, Price & Promo Optimizer enables a shift from backward-looking analysis to forward-looking decision-making. Teams can test assumptions, evaluate multiple scenarios, and identify the most effective strategies before committing resources in-market.

The launch of Price & Promo Optimizer reflects another key milestone in NIQ’s strategy to build AI-enabled decision systems that help clients move from insight to action faster. It also builds on NIQ’s broader work in understanding how AI is transforming commerce and decision making across the consumer landscape.

With more than 23,000 clients across 90+ countries, 22.2 million stores, 220M product categories, and collaboration programs across more than 50 retailers worldwide, NIQ continues to expand its ecosystem of data, analytics, and AI-powered solutions—helping manufacturers navigate complexity and unlock new growth opportunities.

Price & Promo Optimizer is now commercially available to manufacturers globally.

FAQs

Q: What is Price & Promo Optimizer?

A: Price & Promo Optimizer (PPO) is NIQ’s next-generation Revenue Growth Management (RGM) platform. It is a unified, AI-enabled solution that consolidates pricing and promotion workflows into a single, intuitive interface, powered by NIQ’s trusted store-level data and proven analytical models. PPO allows manufacturers to simulate pricing scenarios, optimize trade terms, run promotion analyses, and plan annual strategies — all within one consistent platform.

Q: What problem is it solving for manufacturers?

A: Manufacturers have long struggled with fragmented toolsets: pricing analytics in one system, promotion planning in another, and scenario simulation often done manually in spreadsheets. This fragmentation slows decision-making, increases execution risk, and limits the ability to validate strategies before they reach the market. PPO was built specifically to solve this problem — replacing disconnected tools with a unified, AI-enabled platform purpose-built for RGM and cross-functional teams.

Q: What does “AI-enabled” mean in the context of PPO?

A: PPO uses AI and machine learning in two ways. First, it automates time-consuming data preparation and analysis tasks that previously required significant manual effort. Second, it enables scenario simulation, allowing teams to model the impact of pricing and promotion changes before executing in-market. This includes elasticity modelling, threshold analysis, promo depth simulation, and portfolio optimization, all grounded in NIQ’s real, granular store-level purchasing data rather than aggregate estimates.

Q: Who is this solution designed for?

A: PPO is designed for manufacturer teams involved in pricing and promotion strategy, including RGM Account Managers, Brand and Marketing Managers, Sales Teams, and Senior Leaders. It is purpose-built for cross-functional use and does not require deep technical expertise. RGM teams use it for strategy and simulation; Sales teams use it to build retailer-ready plans; Brand and Marketing teams use it to understand the impact on equity and share; Finance teams use it to model cost effects and margin scenarios.

Q: What results can manufacturers expect?

A: PPO is designed to reduce the time and effort required to make pricing and promotion decisions, enabling teams to act faster and with greater confidence. Common expected benefits include: reduced time on data preparation and manual analysis; more consistent decision-making across RGM, Marketing, Sales, and Finance; faster preparation for retailer negotiations; and the ability to validate strategies through simulation before committing to in-market execution.

Q: How does PPO connect to NIQ’s broader data platform?

A: PPO is integrated with NIQ’s Discover platform, drawing on the same store-level data that clients already rely on. NIQ’s global data footprint — covering approximately 82% of the world’s population and more than $7.4 trillion in consumer spend — provides the analytical foundation underpinning PPO’s models, ensuring every recommendation is grounded in comprehensive, real-world purchasing data.



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