Inventory Management Demands Granular Product Attributes
Where a customer sees a single product, a retailer sees a set of product attributes. Each attribute is a potential influence on a purchase decision. In fashion retail, the goal is to customize and personalize the customer experience and better predict demand for certain products based on the granular attributes shoppers engage with. But context is key. With the right partner in granular product attribution, online retailers can dive deeper into the data to reveal not only what a customer wants but why they want it.
The Conventional Demand Forecasting Method Isn’t Working
Retailers have traditionally used historical sales data to forecast demand, but year-on-year planning is far too slow to respond to a fashion calendar that spans 52 seasons for clothing, as the documentary Minimalism calculated. There are also worse concerns to address.
Where Retailers Need More Insight
- Historical data alone does not reveal the complex and hidden relationships between shoppers and clothes
- Segmenting customers by age, gender, location and other broad demographics only capture the bare minimum needed to resonate with today’s shoppers who crave hyper-personalized, omnichannel experiences.
- Online return rates are as high as 50% in fashion retail, implying a mismatch of customer expectation.
- Even established fashion houses can no longer guarantee brand loyalty.
In short, fashion retailers need deeper insight to better manage demand, and artificial intelligence offers the solution. AI in retail will be a $23 billion business by 2027, and of the 23 uses identified by Gartner for AI, at the top of the list was demand forecasting. Here’s how it can work.
Discover the “Why” Behind Product Performance
Better demand forecasting starts with a deeper understanding of the connection between your products and customer behavior. What was it about that pair of jeans that made it fly off the shelf in size 6, but languish in the warehouse in size 12? Traditional inventory management tools don’t know the particular attributes of those jeans that made them more attractive to size 6 women than to size 12 women, so they can’t help you predict the right size allocation for the next pair of jeans you buy.
Each purchase or engagement generates insight that, analyzed at scale, can help you identify which specific attributes impact sales across various customer segments, and which trends are picking up speed or are falling away. Use these insights to:
- Forecast demand for certain types of products in real-time and react immediately to shifting consumer trends, so you have enough of the inventory that will sell like hotcakes next season and you’re not overstocked in products that are beginning to go out of style.
- Get allocation right by understanding the complex relationships between product attributes and sales performance in various sizes or colors.
In simple terms, enriched product data gives you more detailed information to assess in your inventory planning, an area that 58% of fashion executives in one McKinsey study highlighted as a key priority in 2021.
Leverage Personalized Recommendations to Optimize Your Inventory
Some of the most powerful words in online retail are “You might also like.” Nearly all retailers have invested in recommendation engines to boost AOV and repeat purchases, but many struggle to leverage this tool to make the most of their inventory. Most recommendation strategies rely on historical data about items frequently purchased together by other shoppers. But by gaining a deep understanding of product similarities across many dimensions such as fit, occasion, or style, retailers can produce predictive recommendations without explicit “purchased together” history. This is a crucial breakthrough that enables retailers to prioritize products they need to move out of inventory while still providing the most relevant recommendations that are likely to convert for each individual shopper, so deep discounting can be a last resort.
Start Improving Inventory Management With Granular Product Data Today
According to Forbes, AI can reduce demand forecasting errors by up to 50 percent, but the system is only as good as the data. Lily AI uses more than 1 billion trained data points and a structured taxonomy of over 15,000 product attributes to give retailers an unbeatable demand forecasting and inventory management solution. In addition, our algorithms are supervised by seasoned industry experts with an acute sensitivity to shifting trends, so our data accurately reflects new and upcoming trends. Reach out to learn more today.