Data and AI-driven revenue growth secrets for retail

Companies spend billions on trade promotions annually, but 61% of leaders can’t execute their retail strategies as planned. Here’s how data and the right AI tools change the game for RGM. 

Consumer goods organizations spend up to 15% of annual revenue on trade promotions, but according to a 2025 report, 61% of CPG leaders say it’s difficult to execute retail promotions as planned. The disconnect often comes down to limited visibility, where legacy POS systems and syndicated data fail to provide the granularity and timeliness needed to guide effective decisions.

Retailers are beginning to modernize their data infrastructure, building more efficient, collaborative, and omnichannel-ready systems. For CPGs, this creates an opportunity to access sharper forecasting, real-time insights, and AI-powered tools that improve planning, pricing, and promotion strategies.

Best of all are cost-savings and efficiency gains, boosting top-line revenue, when teams including finance, category management, RGM, and sales and marketing, are all operating from the same accurate intelligence. Intelligence that also matches exactly what retailers themselves have on record, driving trust, strategic alignment, and expansion opportunities. Having the right retail data foundation in place unlocks a world of opportunity for optimization and unlocked revenue growth. Especially when combined with AI, and now agentic AI, which can bring greater clarity and deeper analysis, fast, for teams to act on. Here are our tips to stay profitable and responsive with a technology-focused outlook.

1. Harmonized insights and the AI foundation

Manual data ingestion, cleansing, modeling, and integration can take months, even years, of time from internal CPG and retail teams already stretched thin. It’s not practical, and it’s also expensive – costing north of $250K annually for even mid-sized organizations.

Automated and harmonized insights are the cornerstone of modern retail data strategy. Structured, centralized data delivered into cloud platforms and BI tools eliminates manual data upkeep and ensures every team operates from the same reliable source of truth. Beyond daily analytics, the foundation is essential for scalable AI adoption. Awarded with a 2025 AI Innovation Award, Crisp’s embedded semantic layer technology is built specifically for the retail industry. It provides the structure and business context teams need to query and interpret instant insights with natural language, and to power AI models – like data science-ready AI Blueprints – that deliver accurate, actionable insights across functions. A semantic layer is also essential for the emergence of agentic AI, adding the logic required to provide personalized recommendations or take autonomous actions.

Structured, centralized data delivered into cloud platforms and BI tools eliminates manual data upkeep and ensures every team operates from the same reliable source of truth.

2. View performance in 3D with cleaned-up attribution

Think you have hundreds of products? In reality, it could be thousands of disconnected identifiers. Misaligned naming conventions all prevent a single product from being seen as one item across systems, and create chaos for RGM and demand planning teams trying to build accurate strategies.

Master data management (MDM) solutions, increasingly driven by AI, can streamline attribution across:

• Store cluster identifiers

• Channels

• Identifiers

• Pack sizes

• Ingredients

The ability to sort through insights with what can be considered a data ‘tagging’ system can surface granular performance trends, link real-world demand to precise product traits, and enable fast pivots across planning and promotion strategies.

For example, the rise of GLP-1 medications is influencing consumer preferences toward smaller portion sizes, higher protein content, and reduced-sugar options. With AI-powered attribution in place, RGM teams can identify SKUs aligned with these traits, monitor their performance, and optimize pricing or promotional spend accordingly – before competitors, who could be relying on category data alone, catch on.

The ability to sort through insights with what can be considered a data ‘tagging’ system can surface granular performance trends, link real-world demand to precise product traits, and enable fast pivots across planning and promotion strategies.

3. Auto-target promotions with precision

When retail media spend and digital advertising budgets meet real-time POS and inventory data, it becomes possible to target promotions with unprecedented accuracy. Rather than relying on past campaign performance or static demographic targets, modern solutions enable brands to align spend with availability, optimize bids based on real-time sales velocity, and shift strategy mid-campaign.

For example, by leveraging Crisp to integrate their sales and inventory data into Pacvue’s media automation platform, brands advertising with Target’s Roundel can prevent wasted budget on out-of-stock SKUs and optimize media investment down to the regional level.

Campaigns become smarter, faster, and more profitable, and ensure media and merchandising efforts work in sync.

4. Measure trade lift in real-time

Campaign performance can’t be optimized after the fact. Measuring lift and performance against a forecast while a promotion is live allows CPG teams to adjust tactics mid-flight to salvage underperformance or double down on what’s working.

With integrated real-time sales data, brands like poppi have bridged the gap between digital advertising and in-store results. After running TikTok campaigns in specific regions, poppi leveraged Crisp’s sales and inventory insights to confirm an 80% lift in its regionally-targeted in-store sales.

5. Get to the point on pricing

To arrive at the most profitable pricing for both parties, suppliers and retail buyers have to balance volume, margin, competitive pressures, and the heightened price consciousness of today’s consumers. In short, unit economics. The questions must be asked:

• What discount will drive volume without eroding margin?

• How much is the customer willing to pay?

• How will demand respond to discounting?

• What is current competitor pricing?

The difference with an AI-powered data solution, built upon the most granular data available to a brand, is that it’s easy to leverage robust modelling capabilities to generate reliable scenarios and comfortably predict ROI.

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AI Blueprints that make dollars and sense

Crisp AI Blueprints are pre-built, customizable data science templates that help teams uncover actionable insights around retail’s most pressing challenges. They’ve been developed to eliminate technical heavy-lifting, and accelerate advanced analytics and AI adoption across everyday workflows.

Here are a few that drive measurable revenue outcomes:

• Price Elasticity Estimation – Models how changes in price impact demand so you can fine-tune pricing strategies that boost sales without sacrificing margin.

• Assortment Optimization – Identify which SKUs are underperforming in which stores, and build optimized assortments tailored to regional demand and performance – eliminating resource drains and re-focusing on revenue-boosting velocity.

• Distribution Expansion – Spot missed opportunities by clustering stores with similar sales patterns and recommending high-velocity SKUs for expanded placement. Buyers will appreciate the data-backed rationale and can confidently greenlight your plans.

• Demand Forecasting – Perform a baseline demand forecast based on SKU and geographic aggregation to optimize inventory management and maximize sales opportunities. Further, Crisp enables scenario planning around promotional data and lift to further equip RGM teams with the right intel to fine-tune inventory and spend allocation – avoiding stockouts and costly waste.

AI Blueprints plug directly into cloud platforms like Databricks, Snowflake, or Google Colab, offering instant access to powerful models without the bottlenecks of custom data science. On-platform AI assistants such as Genie and Cortex enable users to custom interact with the results.

What’s next: Agentic AI applications can take AI-driven analytics a step further, proactively surfacing insights, sending alerts, and even triggering workflows to make continuous revenue-boosting improvements. And it all starts with an AI-ready retail data foundation.

Agentic AI applications can take AI-driven analytics a step further, proactively surfacing insights, sending alerts, and even triggering workflows to make continuous revenue-boosting improvements.

Get ready for serious ROI. Crisp customers report 60% distribution growth; $500K OOS savings; 39% category share increases; and more.

Book a demo to get started with data-driven and AI-powered insights.