With big data basking in the limelight, it is no surprise that large retailers have been closely watching its development… and more power to them! By learning to effectively utilize big data, retailers can significantly mold the market to their advantage, making themselves more competitive and increasing the likelihood that they will come out on top as a successful retailer. Now that there are open source analytical platforms like Hadoop, which allow for unstructured data to be transformed and organized, large retailers are able to make smart business decisions using the information they collect about customers’ habits, preferences, and needs.
As IT industry analyst Jeff Kelly explained on Wikibon, “Big Data combined with sophisticated business analytics have the potential to give enterprises unprecedented insights into customer behavior and volatile market conditions, allowing them to make data-driven business decisions faster and more effectively than the competition.” Predicting what customers want to buy, without a doubt, affects how many products they want to buy (especially if retailers add on a few of those wonderful customer discounts). Not only will big data analytics prove financially beneficial, it will also present the opportunity for customers to have a more individualized shopping experience.
This all sounds very promising but the difficulty lies in the fact that there are many channels in the consumer business now, such as online, in-store, call centers, mobile, social, etc., each with its own target-marketing advantage. In order for retailers to thrive in the market, they must learn to manage and hone in on all (or at least most) of these facets of business, which can be difficult if you keep in mind the amount of data that each channel generates. Sam Sliman, president at Optimal Solutions Integration, summarizes it perfectly: “Transparency rules the day. Inconsistency turns customers away. Retailer missteps can be glaring and costly.” By making fast market decisions, retailers can increase sales, win and maintain customers, improve margins, and boost market share, but this can really only be done with the right business analytics tools.
One impressive example of analytics usage is @WalmartLabs, which deals with the social and mobile aspects of retail to redefine commerce for Walmart and help its customers have a more positive shopping experience. Through its Social Genome knowledge base, @WalmartLabs zones in on entities, relationships, and events in the social world (for instance, a tweet about a specific movie title) in order to send out appropriate suggestions to customers. “We do this using public data on the Web, proprietary data, and a lot of social media. From such data we identify interesting entities and relationships, extract them, augment them with as much information as we can find, then add them to the Social Genome.” @WalmartLabs uses its own, in-house data platform called Muppet that is meant to process data at lightning speed.
Sears is another retailer that is focused on the advantages of big data and is using Hadoop to develop its business. If you were able to make it to Hadoop Summit 2012, you had the chance to see Phil Shelley speak about the company’s use of Hadoop and provide some interesting insight about the benefits of the open source platform (If you couldn’t make it, you can find the session slides here). Through Hadoop, Sears is able to compare and organize information about product availability, competitor’s prices, local economic conditions, etc. Before Hadoop, Sears was only using 10% of the information it had in store and was using most of its money and resources on running price elasticity algorithms. Rachael King of the CIO Journal explains, “The company now offloads data from its mainframe computers onto servers using Hadoop to run algorithms that analyze the data and feeds the results back into the mainframe. The retailer is able to use 100% of the data it collects.”
Eric Williams, CIO at Catalina Marketing, offered some helpful information in an interview by Alison Bolen of SAS. According to Williams, retailers can use big data and business analytics to answer questions like, “what products are selling, what’s the association of one product to another, what do my consumers look like, what is the marketplace doing?” With 20,000 new products being introduced in the United States annually it is essential for companies to sort the information about all of these products in order to gauge which ones worked great and which ones were a total flop.
The sorting of information through platforms like Hadoop will allow for extensive feedback in finance, marketing, operations, sales, and other areas of a business, which, in turn, will offer a more “per-customer profitability” approach. Sales associates will be able to access information on the spot (through a mobile device, for example) about which products are up-and-coming or which items a customer may be interested in based on the questions they might ask in the store. So, not only will online shopping continue to become more and more personalized but in-store experiences will also be highly sensitive to what each customer is looking for.
A white paper by Keplar LLP goes through the process of using Hadoop for retail business analytics and offers a list of ways to use the information that is collected through different channels:
The white paper explains that both consumer and product analytics are significantly affected by the presence of big data and to manage both of these, Hadoop is a great solution. Since it uses a parallel structure, Hadoop can run various analyses on smaller data sets which makes it easy for retailers to compare and contrast various products, customer feedback, and the mass of social information that is generated every minute of every day. The possibilities for a thriving retail business are endless.
Without a platform like Hadoop, retailers have to spend big bucks on designing the appropriate data warehouses for the information they collect. Hadoop doesn’t require a pre-defined schema, so storing and interpreting unstructured data like product descriptions or social media conversations between users becomes considerably easier.
In such a consumer-driven society it seems almost necessary to establish a system of organization that could help make sense of consumer behaviors and trends; Apache Hadoop is a smart (and affordable) way to do this. With the social and technological worlds advancing at such an incredible speed, online, mobile, and social consumerism is becoming more of a norm rather than an option. Retail companies can truly receive the most from their business (and provide a positive experience for customers) if they happily open their arms to the big data coming their way and simultaneously understand how to transform this data into a positive business model.