Case Study

Buckle is a leading upscale retailer of denim, sportswear, outerwear, footwear, and accessories.

With over 450 stores in 43 states, the company prides itself on its personalized attention to each guest through its customer-centric initiatives such as complimentary alterations, layaways, and a shopper reward program.

Based in Kearney, Nebraska, the retailer sells a wide range of brand and private-label clothing. BKE, the company’s private label, is known for its quality fit, construction, and vintage appeal. Other popular brands sold include Miss Me and Rock Revival.

With company initiatives geared toward personalized customer attention, Buckle has invested time and resources in gathering customer profile data at the retail store level.

Once the customer opts in, the data is captured and stored through the retailer’s point of sale system. However, the company was dealt with the challenge of sorting through these large amounts of data extrapolated from hundreds of stores.

The main task at hand was dealing with all of the duplicate information that had been loaded into their current iSeries DB2 system. The company was looking for an efficient way to deduplicate all of its data, which included approximately 10 million records.