ISG Provider Lens™ Specialty Analytics Services - Retail and CPG - Retail and CPG - Global 2025

Specialist providers enable precision in pricing, promotions and personalization with GenAI

Advanced analytics and AI are fundamentally reshaping retail and consumer packaged goods (CPG) businesses. Analytics leaders are increasingly embedding data-driven capabilities across the entire value chain, from product innovation and supply chain to merchandising, sales and customer engagement. While implementation is still scaling up, ISG anticipates that comprehensive AI-driven transformation can deliver substantial profit uplift across retailer/CPG operations. This report explores key areas where analytics leaders are integrating advanced analytics and AI as highlighted below:

Personalization and customer lifecycle management

Retailers and consumer brands are using AI-driven analytics to refine customer segmentation and deliver personalized
experiences. Advanced customer data platforms and ML models identify high-value segments, predict lifetime value and automate targeted offers. By analyzing purchase histories, social media interactions, web and mobile behavior, and other metrics, AI quickly generates detailed customer personas and clusters, enabling precisely tailored promotions. Predictive analytics is used to power recommendation engines that leverage cross-channel data (in-store, website and app) to offer personalized product suggestions, promotions and search results.

Some of the key use cases include:

• AI-driven customer segmentation: Automated clustering and persona modeling from big data (POS, CRM, loyalty and clickstreams) help identify high-life-time value (LTV) customer groups.

• Targeted promotions and offers: Dynamic campaign orchestration using predictive models targets discounts or bundles for customers most likely to respond.

• Predictive lifecycle analytics: ML models forecast customer lifetime value (CLV) and churn risk, guiding loyalty programs and retention strategies.

Commercial analytics and revenue growth management (RGM)

Advanced analytics underpin modern pricing, promotion and assortment strategies. Retailers are increasingly moving beyond intuition-based decision-making to AI-driven price optimization models that consider various factors such as
demand elasticity, margins and competitive pricing. ML models evaluate thousands of pricing and promotion scenarios to recommend optimal price points and promotional mixes that maximize revenue and margins. This commercial analytics approach creates a data-driven feedback loop through pricing, promotions, trade, assortment and channel
planning, as described below.

• Data-driven pricing: AI models dynamically optimize price points (for example. time-of-day or demand-sensitive pricing) and recommend optimal price packs.

• Promotion and trade spend optimization: Algorithms analyze trade-promotion effectiveness and forecast ROI, reallocating budgets to the highest-yield discounts and displays.

• Assortment and pack architecture: Analytics is used to optimize the product mix, including stock-keeping units (SKUs), pack sizes, price tiers, for each channel, reducing cannibalization and maximizing portfolio margin.

• Integrated channel analytics: Cross-channel revenue dashboards continuously monitor online and in-store sales, enabling rapid response to market shifts.

• Scenario planning and what-if analysis: Tools that model the impact of pricing or promotion changes on both volume and revenue enable agile decision-making across commercial functions.

Demand forecasting and inventory optimization

ML demand models now incorporate historical sales data and external indicators, such as weather, local events and social media trends, to improve accuracy. The result is tight inventory control, minimized overstock situations and significantly reduced stockouts. Retail and CPG companies leverage these forecasts to optimize replenishment and network inventory. AI algorithms calculate safety stocks across warehouses and stores to balance availability against cost. Rapidresponse analytics detect anomalies, such as sudden spikes in demand or supply disruptions, alerting planners to make necessary adjustments. Together, these capabilities enable lean supply chains with reduced markdowns, less spoilage and waste and improved service levels. Some of the key use cases include:

• Automated inventory optimization: AIdriven calculation of optimal stock levels and reorder quantities across facilities dynamically balances service levels and inventory carrying costs.

• Anomaly detection and exceptions: Systems that flag unusual demand swings or supply issues in real time enable planners to intervene before stockouts occur.

• Autonomous replenishment: Workflows are integrated with the outcomes from analytics (which might be in response to a real-time or a predicted situation) to trigger downstream actions to prevent, minimize or act accordingly.

• End-to-end supply-chain alignment: Data integration from demand signals through procurement and logistics allows materials planning and transportation scheduling adapt quickly to forecast updates.

Planning, assortment and in-store execution

In strategic planning, category and assortment decisions are now largely data-driven, as retailers use regional and store-level sales data to tailor assortments to local preferences. On the store floor, computer vision and IoT sensors are used to ensure execution and availability. For instance, some grocers now use AI-powered shelf scanners to detect out-of-stocks and automatically trigger restocking. Real-time instore analytics (foot-traffic patterns, heatmaps and checkout queue data) help managers deploy staff and resources where needed and enable the following use cases:

• Planogram and space management: Computer vision and shelf-tracking tools verify shelf compliance and dynamically adjust product placement for maximum sell-through.

• In-store promotion analytics: The best in-store locations and timing for featured items, such as endcaps and displays, are determined by analyzing data on shopper flow and response rates.

• Execution monitoring: Real-time dashboards for store managers highlight stock issues or compliance gaps, such as out-of-stock alerts and expired price tags, enabling prompt corrective action.

Generative AI (GenAI) and agentic AI in retail and CPG workflows

New AI frontiers — GenAI and autonomous agentic AI — are creating additional opportunities in retail and CPG. GenAI models (LLMs and multimodal models) are being used to automate creative tasks, design marketing collateral and knowledge-based work. For example, marketing teams employ GenAI to draft personalized emails or social media content, design teams use it to mock up packaging concepts, and product teams use it to create new product ideas. In retail and CPG companies, GenAI is being used to summarize trends or generate reports from raw data, accelerating market research and insights to deliver executive dashboards.

In practice, forward-looking retailers are piloting AI agents that automatically monitor and adjust elements of the supply chain and commerce. For example, an agent might continuously monitor inventory levels and sales rates, then autonomously reorder stock or reroute shipments when needed. Agents can also coordinate omnichannel consistency (synchronizing online and in-store promotions and inventory) or proactively handle service issues such as notifying managers of deliveries stuck in transit and their economic impact. Some of the GenAI-infused enablers are as follows:

• AI-driven supply chain agents: Autonomous agents continuously optimize inventory (reordering or reallocating stock) and logistics (rerouting shipments) based on real-time data.

• Dynamic pricing/promotions agents: Automated systems monitor market conditions and automatically adjust prices or launch promotions without manual input.

• Store and field assistants: Mobile AI assistants help sales associates with information, such as inventory lookup, and route sales forces in the field using demand and traffic analytics.

Leading retailers and consumer brands in advanced markets such as North America and Western Europe are at the forefront of scaling these innovations. Many of these companies have already developed enterprise personalization and demand planning platforms and are piloting agentic AI for key processes. Overall, data-driven transformation is becoming table stakes; companies that integrate AI across innovation, supply chain, commerce and service gain significant competitive advantages.

Specialist analytics providers’ role in retail and CPG transformation

In the evolving retail and CPG landscape, generic technology solutions often fall short in solving nuanced, industry-specific challenges.

Specialist analytics providers bring a distinct advantage by coupling advanced AI and ML capabilities with deep domain expertise, business acumen and a problem-solving mindset. This unique combination enables them to address  complex operational and customer analytics challenges that traditional vendors struggle to resolve.

These providers operate with a consultative, end-to-end transformation approach, integrating seamlessly with enterprise ecosystems and enabling scalable, sustainable impact across the value chain. Their focus extends beyond just deploying tools; they help embed intelligence into the fabric of decision-making through intuitive, user-friendly interfaces and natural language-driven experiences.

Specialist providers differentiate themselves through:

• technical accelerators and reusable components that reduce development cycles and lower TCO.

• GenAI use cases, vertical AI agents, agentic AI-powered data modeling and adaptive learning pipelines.

• prebuilt functional data models to facilitate rapid schema creation, automated ingestion and cleansing frameworks and streamline the integration of internal and third-party data sources.

• strong technology partnerships with major cloud and data platforms (AWS, Azure, Google Cloud and Databricks), ensuring agility, interoperability and future readiness. 

By collaborating with such specialists, retail and CPG enterprises gain more than just analytics solutions; they gain a trusted partner capable of shaping data into meaningful, measurable outcomes.

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