Hudson’s Bay: Big Data for the Perfect Fit
This Canadian retailer has used data analytics to not only help customers find the perfect outfit, but also find an edge in the competitive retail landscape.
Many of you might not have heard of Hudson’s Bay since it is a Canadian department store retailer. However, you probably know two of the companies they operate in the U.S. – Saks Fifth Avenue and Lord & Taylor. Additionally, Hudson’s Bay owns 6 other banners with a total of 460+ stores around the world. The company has been one of the notable retailers in Canada who have embraced big data to enhance operations.
Value Creation Using Data Analytics
Last year, the company partnered with True Fit, a big data company that offers fit personalization software. Hudson’s Bay is the first Canadian retailer to provide True Fit technology to its customers. This enables customers at the Company’s flagship Canadian stores and Lord & Taylor in the U.S. to get a personalized fit rating and size recommendation while shopping online. Customers provide information about their favorite styles and also answer questions about their body type. In turn, the system provides a personalized size recommendation creating a more seamless online shopping experience. True Fit partners with other retailers including Nordstrom, Macy’s, Guess, Oscar de la Renta and has more than 1,000 brands under its database. In turn, True Fit uses machine-learning algorithms to sort through its database, analyze data and make recommendations based on customer’s needs and taste.
Value Capture Using Data Analytics
On the value capture side, Hudson’s Bay has partnered with Saferock to use big data to improve the profitability of its marketing investments. Saferock uses data analytics to calculate the incremental ROI on an item-by-item and offer-by-offer basis. Saferock has baseline data on sales and profit patterns associated with each product and information on Hudson’s Bay’s promotional plans. Using this data, Saferock is able to classify how products perform when advertised versus not advertised. Merchandising, marketing, and company executives get these results in near-real time to measure the efficiency of marketing investments. Obviously, there can be other factors that cause changes in sales patterns apart from advertising, but this data give Hudson’s Bay’s management some insight to drive decisions making around marketing.
The company has also partnered with Teradata to implement a centralized enterprise data warehouse to consolidate and share data almost instantaneously across its stores. Teradata has worked with U.S. based retailers, but Hudson’s Bay is the first major Canadian retailer to partner with the company. Teradata’s system has helped Hudson’s Bay reduce fraud by creating an electronic link between customer purchases and returns, making it more difficult to return merchandise fraudulently. The Teradata data analytics system has also ensured that Hudson’s Bay employees make buying and inventory decisions based on analysis of detailed transaction data and market-based analysis, ensuring the company has the right inventory at the right location. The tool has come to play a vital role in daily decision making at Hudson’s Bay, with the company identifying a 300%+ return on investment on the Teradata technology.
Given the competitive nature of the retail industry, using big data will be increasingly important to create a competitive edge. Marketing spend and inventory management account for a large portion of dollar spend, so data analytics has been a valuable tool for Hudson’s Bay to improve margins. The retailer has been able to gain an edge over its Canadian competitors by being at the forefront of adopting data analytics tools, but it will be interesting to see how the landscape evolves over the next few years as other Canadian retailers invest in some of the same technology.
Student comments on Hudson’s Bay: Big Data for the Perfect Fit
Interesting post! I wonder if companies like Hudson’s Bay could leverage the data to bring the analytics in-house, to prevent its data and trends to be used by other competitors that are using these data analytics companies as well. It must be a challenge given how scarce analytics resources are and how difficult it can be to build this competency.