1. Assortment Gaps 

• Identifying what is missing in your product mix is vital as it helps you ensure that you’re meeting the needs of your customers. With an AI-driven retail intelligence platform, you can see insights into your own, and your competitors’, assortments and visualize where you might be missing the mark or where there are whitespace opportunities. Tracking this insight allows you to fill those gaps strategically and offer a comprehensive range of products that appeal to your target audience.

2. Pricing Gaps 

• Pricing plays a pivotal role in the purchasing decisions of customers. Monitoring gaps across your pricing architecture versus competitors’ helps you stay competitive. By understanding where these gaps are (your t-shirt price architecture is ~ $8-$12, but your competitor’s is $10-$15), you can adjust your pricing strategy accordingly. Utilizing retail intelligence to surface these insights ensures your products are both attractive to shoppers and profitable for your business.

3. Price Increase Candidates

• Identifying products with the potential for price increases is crucial for maximizing profitability. By analyzing this data, you identify items that can bear higher price tags without deterring customers, ultimately boosting your bottom line.

4. Boost/ Bury Candidates 

• Where you position your products on your website is a significant factor contributing to sales and overall business performance. The decision on whether to boost or bury products needs to be dynamic, driven by the product’s inventory position and personalized based on the customer’s attributes. With AI technology, you can see products that have low views but high conversion rates, or high views but low conversion, and determine which products need an adjustment in exposure accordingly.

5. Reactivating Lapsed Customers 

• Analyzing which products bring back previously lapsed customers (400 days without buying) is vital for retaining your customer base. This information helps you determine which product categories or promotions are effective in rekindling interest so you can create a more customer-centric merchandising strategy and ultimately increase sales.

6. Product Profit per Unit Sold 

• Sales revenue doesn’t reflect your product’s profitability. By considering the marketing costs associated with each product sold, you can identify which products are worth promoting and which might require reevaluation or cost reduction to improve overall profitability. For example, if you have $500 in sales from a product, but spent $1,000 on marketing, then you need to reevaluate your strategy. 

7. Views Availability

• Amazon introduced an innovative metric to the industry that shows your customers’ experience of product availability by displaying the percentage of in-stock product views per product view. Without this data, you can see if a product you have in stock is performing well, but you cannot see insights into products that have high interest but no stock. You need access to this data to determine which products would be driving sales if you re-stocked.   

8. Inventory Value – Not on Site 

• The lag between getting a product delivered to your warehouse and having it advertised on your website is a very real struggle in retail. Knowing what is actually available to your customers is crucial. With an AI-driven retail platform, you can identify product(s) that are in-stock at your warehouse, but not active online or in stores. For example, $100k of inventory for ready-to-go products might be in the warehouse, but not online, as it is missing product images. Uncovering this “low-hanging fruit” is critical for converting inventory into sales, and minimizing missed opportunities. 

9. New Customer Order % 

• Knowing which products attract new customers and drive their initial purchases is key to targeting your marketing efforts effectively. With retail intelligence, you have more access than ever to data on customers’ buying behavior and preferences. By tracking the percentage of orders completed by customers who had never purchased previously, you can allocate resources to the products that have the most significant impact on customer acquisition and personalize promotional activity to customer demand.

10. Return Rate (Four Weeks) 

• AI-driven retail analytics software allows you to track the percentage of units returned compared to the number of units shipped during the last four weeks. Utilizing a four week average for return rate removes peaks and valleys associated with holidays or promotional periods, allowing you to focus on consistently high return rate offenders. The ability to spot high return rates, even during a sales surge, indicates potential issues that need addressing.