Need to Know

Press enter to search
Close search
Open Menu

Need to Know

By Adam Blair - 01/04/2013
In today’s complex retailing environment, the smart use of data is becoming as valuable as top-notch product, great store locations and well-trained personnel. Apparel retailer Buffalo David Bitton believes mixing and matching a variety of data types is critical to its continued growth.

The Montreal-based retailer operates 30 stores in Canada, just opened its first U.S. store in New York’s SoHo district and also sells through major department stores. It enriches its business intelligence and customer analytics by combining “information from multiple data sources – traffic counters, payroll systems, weather data,” says IT director Stephen White.

The retailer has recently begun to perform more detailed customer analytics, beginning with its e-commerce operations and using a BI solution from Cognos. For example, “we can see if this customer bought this big-and-tall style over the last six months,” says White. The ability to individually identify customers makes segmentation simpler in the digital realm, but the retailer is also looking to transfer its benefits to brick-and-mortar channels. A loyalty program operating across multiple channels set to debut in April 2013 will be critical to Buffalo’s efforts.

Expedited Reporting
Another important data resource is the operational data available from the retailer’s Raymark solution. “Everything to do with sales is captured there, including merchandising, sales by store, conversion rates, units per transaction – that’s all being pulled from Raymark,” says White. “It’s an expedited tool for us to do exception-based reporting.”

Buffalo’s ability to use multiple systems to coordinate a variety of data types will prove helpful as it pilots performance optimization, which requires not just reporting and analysis but also predictive analytics capabilities. “We’re going to benchmark different areas of the business and set Key Performance Indicators (KPIs),” explains White. “For example, we might have a store that we would classify as an ‘A’ store versus another that’s a ‘B’ store. But it might not really be a fair comparison: a store could be an ‘A’ store in sales but only a ‘B’ store in terms of customer traffic.

“Performance optimization allows us to see where we might be missing opportunities, and discover areas where we should be doing better,” White adds. “By benchmarking store to store, or region to region, we can say ‘This should be the units per transaction in this store.’”