Merchandisers must ensure that their products are available at the right place, at the right time, and at the right price to maximize sales and profitability for retailers. Category managers and merchandisers work to deliver a product mix that resonates with target customer segments, ensure optimal stock levels, utilize store space effectively, and enable well-timed pricing and promotions. Coupled with addressing evolving customer preferences, long buying and setup cycles, and supply disruptions, it is a highly complex challenge for merchandisers to deliver consistent, responsive, and profitable outcomes across thousands of stores, hundreds of thousands of SKUs, and millions of price points.
With rapidly changing consumer trends influenced by social media, volatile geopolitics creating supply chain challenges, and the need to balance seamless omnichannel requirements, coupled with operational inefficiencies of legacy systems and processes, merchandisers face a race against time to get their merchandising plans correct and on time. Demand volatility, misaligned assortments, and inventory issues can derail a merchandising plan. We explore key trends and priorities shaping the future of retail merchandising and how agentic AI can help merchandisers stay ahead and create sustainable, successful merchandising plans.
Assortment Optimization:
Moving beyond traditional regional level assortments is key to cultivating a loyal customer base for retailers. Focusing on dynamic assortments and micro-assortments, which cater to specific customer groups or even individual customers, can mean the difference between success and failure. Agentic merchandising, enabled by a curated set of AI agents listed below, allows merchandisers to achieve their data-driven, customer-centric, and growth-driving assortment goals.
- Dynamic assortment agents enable merchandisers to proactively analyze sales across seasons, demographics, customer behavior, and channel types, recommending dynamic SKU mixes through data-driven and dynamic assortment optimization.
- Localized merchandising agents help create micro-market assortment plans by analyzing local demographics and regional trends, as well as event data (such as weather, sports, and concerts), to provide a tailored and hyper-localized assortment plan. This also helps in efficient inventory management and reduces waste.
- New product agents analyze category strategy, social media trends, competitive assortment intelligence, global supply chain dynamics, sustainability goals, and local regulations to recommend new products with a high probability of success. This helps retailers launch accelerated and optimized assortment plans with faster time to market.
Dynamic Pricing and Promotions
Continuous adjustments of pricing based on demand, customer behavior, inventory levels, competition, and market dynamics add complexity to merchandisers. Pricing changes no longer occur in fixed-time cycles; instead, they are more responsive to market conditions, adding fluidity to assortment plans, buying plans, and markdown/promotion decision-making. Agentic AI is transforming the way merchandisers address these challenges by enabling automatic pricing adjustments through AI-led, data-driven pricing decisions.
- Dynamic pricing agents help monitor and develop competitive price benchmarking, assess pricing and demand elasticity, and perform scenario modeling of specified constraints, such as cost, margin requirements, brand perceptions, and price agility, to provide continuous and real-time price recommendations. With autonomous pricing execution capabilities at scale and speed, supported by specified guardrails, these agents enable merchandisers to respond more quickly to market and/or demand shifts, while optimizing sales growth and profit margins.
- Promotion effectiveness agents actively analyze the effectiveness of both live and completed promotions across a brand, category, or channel type, gathering data autonomously from various data sources to analyze, reason, and recommend changes to promo types and constructs for increased sales. With autonomous execution, these agents can make changes across channels and platforms simultaneously.
- Personalization agents analyze individual and group-level shopper behavior data to recommend targeted discounts and cross-sell or up-sell products to specific groups of customers based on their location, purchase history, and loyalty membership tiers. This helps improve conversion rates for retailers by optimizing and personalizing promotions focused on sales maximization, reducing board-based markdowns, and growing their loyalty customer base.
Competition and Market Intelligence
Merchandisers often must deal with a lack of real-time market data and an overload of fragmented data of unreliable quality. This results in benchmarking without context and at the wrong levels of granularity, making it challenging to track competitor and channel-level strategies, and an inability to distinguish between trend detection and trend noise. AI agents can help merchandisers translate data into actionable insights at an accelerated pace, providing them with the decision frameworks they need to make data-driven decisions.
- Price monitoring agents help merchandisers track price movements across competing brands in specified categories across competition, marketplaces, and channels. With dynamic price benchmarking and real-time promo impact analysis, these agents automatically establish competitive price points, define base prices, and track promo changes within specified guardrails to help retailers stay competitive, protect margins, and remain relevant in the market.
- Trend monitoring agents scan real-time social media and market trends for the early and proactive identification of trends related to products, brand sentiment, sustainability, and evolving customer preferences. This enables them to provide assortment change recommendations, optimize marketing spend budget allocations, and inform promotional strategies.
Store Space Optimization
Driving store productivity and effective shelf space utilization are crucial for maximizing sales, maintaining optimal inventory levels, and enhancing the customer experience. The advent of agentic AI has moved store space planning from traditional to continuous optimization, from dynamic planogram effectiveness and compliance monitoring to data-driven space optimization.
- Shelf monitoring vision agents analyze the share of shelf space for competing brands, identify shelf space issues, and ensure adherence to planned shelf allocations to provide recommendations for corrective actions, cross-sell/upsell changes, resulting in higher product availability for sale.
- Shelf value density agents help merchandisers analyze stock movements to provide recommendations for optimizing shelf share of better-performing SKUs/brands, thereby maximizing sales.
- Dynamic shelf plan agents utilize historical sales data to create dynamic shelf share plans that cater to seasonal and event-based product demands.
- POG compliance agents utilize data from in-store CCTV, ESLs, and sensors to monitor and flag non-compliance with planogram plans, recommending and executing corrective actions as needed.
Conclusion
The future of merchandising is not about AI replacing merchandisers, but about augmenting human judgment and the speed of execution, enabling merchandisers to focus on what is relevant to their business – brand curation, creatively aligning merchandising plans with business goals, and designing an immersive experience for the customer. Agentic AI-led merchandising has enabled merchandisers to adopt a dynamic, self-learning, and autonomous execution engine, facilitating higher sales, margin uplift, and shorter cycle times for seasonal assortment resets, space optimization, and dynamic pricing. Human-in-the-loop design ensures that agent decisions consider continuous feedback and learning, and are governed to be accountable. AI-led merchandising planning, optimization, and execution enable category managers and merchandisers to deliver market-responsive and profitable merchandising at scale, with speed, and at a cost-effective rate.
