The Future of Retail // How Generative AI is Shaping Assortment Planning

Creating Personalised Assortments for Different Locations and Customer Segments

Retailers have long grappled with the challenge of catering to diverse customer preferences. A store in bustling London might have different needs compared to one in a small town, and even within the same city, preferences can vary widely based on age, income, and lifestyle. Store clustering based on these factors can help customise your assortment and optimise the collection planning process.

Traditionally, retailers have relied on historical sales data or gut feelings to decide what to stock. But this often leads to generic assortments that miss the mark. Generative AI changes all that. It uses data and smart algorithms to create highly tailored assortments for each location and customer segment.

Here’s how AI helps:

• Analyses data from customer feedback, past purchases, local trends, and even weather patterns.
• Offers location-specific recommendations, like stocking more beachwear in coastal stores during summer and workwear in city-centre shops.
• Suggests assortments for different customer segments, such as highlighting eco-friendly products for sustainability-conscious shoppers or trendy items for younger audiences.

With these tailored assortments, retailers can better meet customer needs and boost sales and satisfaction.

Determining Optimal Inventory Levels

Getting inventory levels right has always been a balancing act. Too much stock ties up capital and leads to excess, while too little stock means missed sales and unhappy customers. Finding the perfect balance is tricky, especially with changing consumer preferences and trends.

Generative AI makes this balancing act easier. Unlike traditional methods that might rely solely on past sales or seasonal trends, AI digs deeper. It considers a range of factors to forecast demand accurately:

• Past purchasing patterns
• Social media trends and mentions
• External factors like weather forecasts and local events

With these insights, AI can predict when demand for certain products will rise or fall. For example, it might spot that winter coats sell more in northern cities around mid-October. Retailers can then adjust inventory levels accordingly, avoiding overstock and stockouts.

Reducing Markdowns and Preserving Profit Margins

Nobody enjoys marking down prices without planning, but it’s often a result of poor inventory management. When products don’t sell as expected, retailers resort to discounts to clear out stock. While markdowns help move products, they also cut into profit margins and can hurt brand value.

Generative AI helps tackle markdowns in several ways:

• Proactive demand forecasting: By predicting demand accurately, AI helps retailers stock the right products in the right quantities, reducing the need for drastic discounts.
• Dynamic pricing suggestions: AI can recommend small, location-specific discounts to boost sales without resorting to blanket markdowns. For instance, if a product is selling well in some stores but not in others, AI might suggest targeted price adjustments.
• Lifecycle management: AI can identify when products are nearing the end of their popularity and suggest gradual price reductions to clear stock, avoiding the need for deep discounts.

With AI’s guidance, retailers can sell more products at full price, protect their profit margins, and cut down on markdowns.

A Real-World Example: AI in Action

Let’s look at a real-world example, H&M. A global fashion retailer was struggling with assortment planning. Their approach, based on historical data and intuition, led to mismatched assortments and frequent markdowns. They turned to generative AI to help.

The results were impressive:

1. Personalised assortments: AI tailored product offerings for each location, so coastal stores had more summer apparel, while colder regions got an expanded range of winter wear.
2. Optimised inventory: AI’s accurate demand forecasting helped adjust stock levels dynamically, reducing overstock and stockouts.
3. Fewer markdowns: Improved inventory management meant less reliance on markdowns, preserving profit margins and brand value.

Looking Ahead: The Future of AI in Assortment Planning

Generative AI is still evolving, and its potential is exciting. Here’s what the future might hold:

• Even more personalisation: AI will continue to refine its ability to create highly tailored assortments, based on emerging trends and real-time data.
• Sustainability focus: AI could help retailers make more eco-friendly choices, optimise supply chains to reduce waste, and cater to growing consumer demand for sustainable products.
• Real-time adaptability: Future AI models might offer instant adjustments to assortments and inventory based on sudden market changes or customer preferences.

Generative AI is a powerful tool that’s transforming retail assortment planning. By leveraging AI, retailers can move beyond outdated methods, create more personalised and dynamic assortments, optimise inventory, and reduce markdowns. This not only enhances operational efficiency but also improves the customer experience, making sure shoppers find what they want when they want it.