AI in Retail Analytics: How to Master Product Assortment Optimization
Localized assortment planning is the key to customer loyalty and revenue growth. Leveraging AI and ML transforms product mix strategies, ensuring hyper-personalization and optimizing profitability across channels and locations with actionable insights driven by real-time data analytics.
Why Product Assortment Is a Retailer’s Secret Weapon Today
Customer loyalty: The link between assortment and personalization
The fastest way to achieve customer loyalty is through localized assortment planning in retail operations. By leveraging artificial intelligence (AI) and robust data analytics, cross-channel retailers can truly optimize assortments designed to drive revenue and enhance customer loyalty. Customer preferences are constantly changing — and shoppers expect their favorite retailers to consistently respond to their evolving demand. In fact, 81% of shoppers prefer companies that offer a personalized experience, according to “The State of Customer Service and CX,” a study from customer experience expert Shep Hyken.
Revenue growth: How localized assortments maximize profitability
However, retailers relying solely on historical data and “gut instinct” merely feature generic, lackluster assortments, at best. Aligning assortments with customer preferences is key to solving this challenge — and achieving huge returns on their investment. Enhancing assortment planning efforts enables retailers to determine exactly which products to carry in their stores. By jump-starting analysis of customer insights and product movement data across their most profitable channels, grocers, for example, can make informed decisions related to specific merchandise mixes and optimal inventory levels. Not only do these localized assortments improve customer satisfaction, but they also reduce inventory costs, optimize store space, enhance supply chain management, and most importantly, dramatically reduce inventory turnover. In fact, the right merchandise mix can increase sales by between 2-5% and boost gross margins by 5-10%, according to a McKinsey & Company report.
What Are the Challenges Retailers Face Without AI and ML?
The pitfalls of intuition-based assortment planning
However, many companies are not yet positioned for success. Retailers are drowning in growing volumes of big data generated from customer interactions, supply chain activities, and digital channels. This data stems from collecting customer and sales information from various sources ranging from new digital platforms to multiple media formats. Worse, this data deluge is often stored in disparate repositories, making it difficult to access actionable insights and optimize demand forecasting.
The impact of big data overload on decision-making
All factors contribute to redundant, erroneous information. Further, users cannot accurately understand — or leverage — customer preferences, making it impossible to forecast future demand in real-time or adapt to ever-changing market trends. Big data volume is increasing exponentially. The total amount of global data created, captured, copied, and consumed is forecasted to grow to more than 394 zettabytes by 2028, according to Statistica. That’s 21 zeroes, or 1 trillion gigabytes. To put it in perspective, one zettabyte is equivalent to the data on about 250 billion DVDs.
Why historical data alone is no longer enough
Calculated decisions — from initial assortment planning through retail execution — are mission-critical. And this depends on clean, actionable information, timely insights, and powerful analytics that can be easily analyzed and applied across sales channels and store operations.
How AI and ML Revolutionize Retail Product Assortment
Real-time analysis of customer preferences
Enter the value of AI and machine learning. Generative AI solutions are designed to automatically and rapidly manipulate massive amounts of unstructured customer and inventory data across multiple sources. AI expertly analyzes — and predicts — shoppers’ shifting demand, enabling retailers to optimize product mixes dynamically and localize them across different store locations, formats, and even digital sales channels.
Localized assortment planning across stores and channels
Not only does AI ensure that each store’s — or channel’s — assortment is unique and tailored, but decision-makers can consistently update brands, package sizes, pricing strategies, and even introduce new products in real-time. As a result, these optimized, hyper-local assortments are purely designed with the intention of boosting customer engagement, improving marketing effectiveness, and maximizing profitability.
Dynamic assortment updates: Staying ahead of demand
Specialty grocery retailer Fresh Market, Greensboro, N.C., can attest to these benefits. Leveraging AI integrated within the Captana computer-vision solution from VusionGroup, a world-leader in Retail IoT, supply chain intelligence, and category optimization solutions, the supermarket operator analyzes shopper behavior to expertly characterize portfolios, regionalize assortments, and target in-store promotions. Since applying AI across center store and fresh departments in all 166 stores, Fresh Market has improved merchandise availability by accurately predicting out-of-stocks, optimizing supply chain management through shelf management, and prioritizing waste reduction, among other critical business benefits.
Case Study: How Fresh Market Optimized Their Assortment with AI
Overview of Fresh Market’s AI implementation
It is these types of data-driven benefits that will push AI spending to more than double by 2028, reaching a threshold of $632 billion, according to the International Data Corporation (IDC). Retailers increasingly recognize the importance of technology-powered solutions to stay competitive and customer-centric in today’s rapidly evolving retail landscape.
Key performance improvements observed
Retailers can no longer afford to rely on outdated, unreliable assortment methods in hopes of creating profitable inventory assortments. Now more than ever, it is beyond ideal — it is essential — to feature personalized, unique product mixes across specific channels, stores, and physical locations supported by strong data insights and real-time analytics capabilities.
Getting Started: How Retailers Can Implement AI for Assortment Optimization
Setting up clean, actionable data foundations
Successful AI-driven assortment optimization starts with the right data. Without a clean, structured, and actionable data foundation, even the most powerful AI models cannot deliver meaningful insights. Retailers must centralize customer, product, sales, and inventory data into a unified platform, ensuring consistency, accuracy, and accessibility. Solutions like VusionCloud offer seamless data integration and real-time synchronization across store networks, providing retailers with a single source of truth. By implementing robust data governance policies, automating data collection through IoT sensors like Captana, and continuously monitoring data quality, retailers lay the groundwork for AI systems to operate efficiently — delivering faster, smarter, and more reliable assortment decisions.
Choosing the right AI and ML tools for retail
Retailers face a crowded market when it comes to AI and ML technologies. Choosing the right solutions means selecting platforms specifically built for retail’s operational realities: localized assortments, stock optimization, and real-time promotions. VusionGroup’s Retail IoT and AI solutions, including Captana and VusionCloud, are designed to meet these exact challenges. They combine machine learning, computer vision, and predictive analytics to continuously track shopper behaviors, detect inventory issues, and recommend proactive assortment adjustments. Retailers should prioritize tools that integrate effortlessly with their existing tech stack, scale across store formats, and offer explainable AI models — making insights not only actionable but also trusted by decision-makers.
Best practices for scaling AI across channels
By leveraging robust AI and ML tools and an advanced Cloud Retail Solution, retailers gain deep insights into customer preferences, inventory trends, and market shifts — the foundation needed to create dynamic yet tailored assortments that not only reduce out-of-stocks and markdowns but also drive customer engagement, sales growth, and higher profitability.
Key Takeaways to Drive Retail Success with AI and ML
- Importance of dynamic, localized assortments to boost customer loyalty
- Role of AI/ML and predictive analytics in boosting profitability and operational excellence
- Practical steps to leverage AI solutions in retail today
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