Customer assortment analytics is the simplest and most cost-effective way to optimize the assortment across a retailer’s entire store or chain. It is a simple measure of the number and mix of SKUs (stock keeping units) that are in a given store, how they vary by region to account for regional culture and preferences, and how that relates to the assortment plan. Most importantly, the analytics can be used to identify and prioritize regions where inventory is low or running low on certain SKUs so that these stores can be replenished in time for the peak holiday buying season. The following are some of the benefits of the customer assortment analytics solution https://www.lynxanalytics.com/hk/retail-solutions including;
1. Improved sales
With the right customer assortment plan, any retailer can achieve improved sales and growth by selling more items that meet customer needs, particularly during peak season. This is achieved by focusing inventory on the products that sell well during peak season and are likely to go out of stock or offer minimal sizes if not replenished in time. In addition, a successful assortment plan will also keep stores from carrying too much stock of products that sell even better outside the peak season, particularly non-grocery/non-general merchandise items.
2. Ease operational complexity
When a retailer’s sales and inventory cycles are synchronous, it is possible to anticipate when specific products will be out of stock and replenish them in order to reduce the risk of inventory shortages and out-of-stocks.
3. Reduced waste
A customer assortment analytics tool will enable retailers to use data more effectively to eliminate waste by design. For example, it can tell them which 50 percent of their SKUs sell better than average and whether there’s a significant mismatch between those items and their assortment plans or which products go out of stock first or go through significant turnover within a store. This information can help retailers improve their assortment plan by finding out which SKUs sell better and in which store, and how long it takes to run through inventory.
4. Better forecasting
Using the customer assortment analytics tool, retailers can also take advantage of historical data to improve forecasts of when they will run out or go out of stock. This makes it easier for retailers to get the right balance between product selection and replenishment in order to reduce the risk of a shortage or an overstock situation, thus ensuring higher sales volumes during peak season.
5. Reduced labor costs
While the most common customer assortment analytics system determines the number and mix of products that are in each store, a retailer can also optimize their assortment based on customer “preferences”, known as “metrics”, by surveying and listening to customers for example online. This reduces labor costs by enabling retailers to adjust the mix of products sold at each store based on what customers have been buying in previous weeks or months. Customer preference can be derived from multiple sources including the web history and sales patterns to identify those items most likely sold in the past.