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Retail Pricing: Using information to improve profitability of large chain retailers – Part 1

Stephane Bratu discusses how to use information to improve profitability of large chain retailers in part 1 of this 2 part series.

In the 80’s and the 90’s the airline industry spearheaded the effort to optimize costs and revenues by using sophisticated decision support technologies.  It is now well accepted that it would be difficult for an airline to survive without an optimization model to optimize crew schedules or to improve revenues by opening or closing their fares based on demand forecasts.

Retail Revenue Management

 Although the retail industry has also been at the forefront of using decision support technology, both on the cost and the demand sides (e.g., markdown optimization for seasonal fashion articles), a new world has opened:  The online retail revolution to better understand customer behaviors.   Indeed, the world is changing for retailers as information is being mined to improve their understanding of customer-choice behaviors and as availability of market information to make better product, sales and marketing decisions is becoming more available. Major brick-and-mortar retailers have been collecting online information through their own websites (as it is crucial for them to have a strong website to compete head-to-head with pure online retailers such as Amazon and EBay) and by scanning competitors’ sites.

Many retailers have already capitalized on using decisions sciences to improve their sales.  Amazon was a precursor of this phenomenon, with its customer data mining affinity application, which generates customer recommendations based on purchase history.  In addition to internal data mining, major chain retailers have now invested in mining online for other retail competitors’ pricing and product offering in almost real time.

Store retailers have started to compete head-to-head with Amazon, as e-commerce is a fast growing business channel, with $1 trillion worldwide in 2012, up 21% from the previous year according to eMarketer, with strong growth not only in the US but also in Asia and Europe.  But the gap is closing between brick-and-mortar retailers and pure online retailers.

How did major brick-and-mortar retailers respond to online retailers? By building stronger websites, resembling pure online retailer ones, and more recently, by investing in business analytics and building their own data mining teams.  Walmart Lab, based in Silicon Valley, acquired Kosmix in 2011 for $300 million, and Home Depot acquired Austin based Black Locus in 2012.  Both major retailers use data mining and machine learning algorithms on unstructured data streams, using new languages (such as Hadoop by Google) to extract prices for their identical products and collect information about competitors’ product offerings in order to better respond to competitors, better understand customer profiles and find sales opportunities.  Petabytes of information (millions of gigabytes) are scanned every day to collect this information using state-of-the-art computer science technologies and Walmart Lab is able to get competitors’ prices every 20 minutes (2).

In addition to collecting competitors’ online prices, brick-and-mortar retailers have successfully improved their web portal search algorithms and have proposed products that were viewed by other people who viewed the product in the searches, similar to Amazon’s product affinity application. Some brick-and-mortar chain retailers are also working on new apps to help their customers navigate through the stores to find their products and know whether they are available at the store (and give and alternative store if they are not).   Enabling shoppers to know where the products are will entice them to come to the store more often as the experience will be better.  A tremendous amount of information is being collected every day.   To compete against Amazon’s Prime $79 a year subscription that offers unlimited free two-day shipping, Walmart will implement lockers where shoppers can come and pick up their online purchase in the store (1) in the summer.   Finally, the tax disadvantage incurred by retailers with physical presence in a State, has recently been corrected by Congress by passing the internet-use bill, which will require online retailers to collect state taxes for e-commerce.

Retail Pricing: Using information to improve profitability of large chain retailers is a two-part blog series. Check back next week to find out how the world of big data can help large brick and mortar retailers to improve their pricing decisions and ultimately their financial performance.

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