Sport Chalet is not looking to be the biggest retailer specializing in selling all types of sports-related merchandise—only the best.
The company has achieved a 63% increase in sales growth in the past five years and rakes in an average of $9.4 million annually per store, at $238 per square foot in a prototypical 42,000-sq.-ft. store. The stores carry product in 17 distinct merchandise divisions, and also offer scuba training and employ about 75 associates each.
It is a retail vision built on the concept of creating an extremely exciting shopping environment, offering a broad assortment of products, ease of shopping, and a merchandise strategy arranged by lifestyle and performance as well as specialty concepts, such as surfing and separate skateboard shops. Hard lines account for 52% of sales, apparel, 29%, and footwear, 19%.
But with all its success and a banner well-known for both quality and a find-anything-you-want SKU assortment, the retailer still felt it was not doing nearly enough to maximize customer information. It wanted to base buying decisions on what the shopper wanted heading forward, or forecasting customers needs, rather than basing all or most of its buying decisions on sales history.
The issues Sport Chalet was encountering with its old systems were typical of many retailers: shipments not necessarily equaling demand, problems with store and vendor on-time replenishment, and the resulting poor in-stock positioning, excess inventory, slow turns and sub-optimal product flow.
In other words, as well as the retailer was doing, it saw a ripe opportunity to vastly improve operations, cut costs, spur sales and definitely hike bottom-line profits. To do so, Sport Chalet decided to up the ante and invest in the infrastructure and applications needed to much better predict customer demand and then act on those predictions in its buying and merchandising decisions.
The forecasting customer-demand initiative was sweeping, is still ongoing and involved numerous phases. Phase 1 focused on customer-demand replenishment, including seasonality, promotional demand, lost sale, lead time and demand forecast. A second phase delved into areas such as merchandise, store and in-season planning, performance analysis and allocation.
But even with all that, it was still a leap to get to the point where Sport Chalet could begin leveraging what it terms “high-performance forecasting” to make its buying and other critical decisions based in large part on new science rather than simply human analysis. But it finally did make that leap.
The results, according to Jason Gautereaux, director, inventory management for the chain, have already included increased inventory productivity, customer and promotional service levels, and customer satisfaction as measured by comment cards. The impact also extended to improved product flow, new-store inventory productivity, reduced replenishment systems and, perhaps most important, he said, enhanced customer shopping experience.
Sport Chalet is now forging ahead into Phase 3 with a focus on vendor collaboration, merchandise allocations, CRM, work-force management and assortment planning, Gautereaux said, noting that the payback of working with SAS on the project has been exciting and substantial, and there are still numerous opportunities to optimize operations based on serving the customer.
Gautereaux made his presentation, along with Diana McHenry, director, Global Retail Practice, SAS, during the Technology & Operations Store Summit (TOPSS) in Las Vegas in October. TOPSS is produced by Chain Store Age and Retail Technology Quarterly.