How Can AI Help With Better Assortment Planning?
In the early years of retail, assortment management was limited to a handful of variables. Mostly, the populace was relatively localized, with most people having similar roots and ideas and spending their whole lives in the same town. Local business owners, therefore, had a good idea of what their customers desired. Today, retailers run national and international chains. A one-size-fits-all strategy would lead to “stock outs” on popular goods and markdowns on others, which would lose the store’s hard-earned revenues. When customers cannot locate the items they want to purchase, customer satisfaction and loyalty are negatively affected. To keep pace with evolving needs, retailers must develop more complex methods of matching consumer needs. A failure to provide a varied assortment that meets the demands of a broader customer base will lead to higher billings, while a failure to do so would leave the door open for competitors. AI makes assortment management and optimization more timely, aligned, and lucrative by precisely forecasting how many variations to provide, how many of a given item are needed to minimize stockouts and markdowns, the storage and display capacity required, etc. How Does AI Help? The right stock at the right store AI models can look at factors like past sales, retail display space, local trends, internet activity, weather forecasts, and more to determine which products would be best for a specific retail location. This AI-based optimization ensures that items are displayed where they can be sold at full price, thus helping to cut down on markdowns. Real-time data analysis also allows retailers to respond instantly to changes in demand, reducing stockouts by moving more items to where they are most likely to be sold. Also, AI models can move goods from one store to another so that businesses can take advantage of local trends. By generating shopper-focused, trend-appropriate assortments, the company can meet customers’ short-term and long-term needs across every category and even predict them. Predictive capabilities AI-enabled technology and systems intelligently mimic human behavior to improve outcomes. Using machine learning, automatic triggers detect periodic trends, peaks, and dips in data and notify merchants and suppliers. Retailers can predict future market behavior by researching past buying patterns, resulting in more precise forecasting and inventory alignment. By better understanding client preferences, intentions, and behaviors, AI enables shops to collect shopper information in an automated and predictive manner. It can also prevent under or overstocking that affects the bottom line and, in the case of perishable commodities, causes spoilage. Furthermore, the complicated mathematics inherent in AI allows it to provide credible recommendations for upselling and cross-selling more effectively. Better curation Curation helps clients find their needs without overwhelming them with options, brands, or packing sizes. It also increases shelf space. Traditional curation (micro merchandising) is primarily concerned with margins, volume, shop size, location, and what shoppers within a specific zip code purchase. While these are necessary prerequisites, they lack the analytical capacity of artificial intelligence to cross-reference a massive array of data points across various consumer indices. Using AI will allow merchants to understand, for example, if shoppers prefer the brand over price or whether they prioritize pricing over packaging size. This data demonstrates to a retailer what alternatives a client will accept within a specific category or price range. It can help eliminate “dead inventory” and discover items that perform poorly but are frequently purchased by a chain’s highest-volume customers. On the other hand, traditional curation is concerned with gross profit or volume and often overlooks opportunities to retain valuable clients. Respond before competition Because of its extensive supply networks, massive product assortments, and poor profits, retail is regarded as one of the most competitive industries. Traditional offline stores, as well as e-commerce, face stiff competition. AI can help retailers stay relevant by constantly refining product assortments and improving business operations. Product selection is one of the most critical factors that merchants can control to differentiate themselves from the competition. With sophisticated analytics and artificial intelligence, retailers can make better decisions about which things to stock in their stores and adjust their product assortments to local client preferences and store sizes. By leveraging AI’s ability to foresee upcoming trends and identify shortcomings before negatively impacting the market, retailers can create a more profitable and competitive private-label strategy. Needs-driven assortment optimization AI employs data mining to examine data samples in real-time and make recommendations based on what works. There is no need to wait until the following calendar review to understand whether an item has experienced a temporary dip or a massive drop. This allows retailers to choose products with proven results. In terms of what customers genuinely want, AI enables merchants to differentiate between perception and reality. For instance, customers may desire to purchase a specific product, but pricing or other factors may discourage the purchase. More likely, probabilities can be generated using advanced modeling and forecasting approaches. AI can also accelerate product success by projecting SKU-level customer preferences and affinity using demand patterns and buyer propensity modeling. Over time, this approach would lead to increased sales and margins and improved retailer and supplier collaboration. As a result, there is a more level playing field with products that provide tangible benefits and are in line with actual client demands. Conclusion According to research from IDC, 65% of retailers say AI is essential for merchandise analytics, and 54% cite improving ecosystem collaboration with suppliers as a top priority. As more and more retailers get on board with AI, it will not be a differentiator but a necessity. Making suitable investments early on helps you get an early start and leverage your first-mover advantage. Talk to Valiance to understand how our AI solutions can help you optimize the assortment at your retail outlet or e-commerce site.
