Retail is one of the hottest markets for big data analytics. Winning in retail sales is a game of small successes. Most retail margins are small, so keeping close track of overhead and delivery costs is essential to maintaining profitability. Finding new ways to attract and build customer loyalty is also a factor, and that means understanding customer sentiment and desires. Big data can answer these kinds of questions. By pulling together data streams from sales, operations, inventory, revenue, and other sources, big data analytics are helping retailers fine-tune their operations to reduce costs, increase customer satisfaction, and generate more profits.
According to Wikibon, the software growth rate for big data will remain at 45 percent through 2017. McKinsey reports that over a five-year period, companies that are investing in big data as part of their sales and marketing programs are yielding an ROI of 15-20 percent. And retail is one of the biggest sectors taking advantage of big data. Retailers are using big data to improve operations across the board, including merchandising (62 percent), marketing (60 percent), e-commerce and multichannel (44 percent), supply chain (29 percent), store management (25 percent), and operations (14 percent). Some major retailers have already reported real big data results. Macy’s says big data helped boost store sales by 10 percent, and Sterling Jewelers said it increased holiday sales by 49 percent with help from big data.
Retail analysts are finding big data especially helpful for specific marketing and operations use cases. Here are six use cases to consider as part of your retail sales efforts:
Common Retail Use Cases for Big Data
1. Building a 360-degree view of the customer – Customer behavior and sentiment can be determined using Hadoop analytics, which can help retailers refine how they interact with customers in the store, through direct mail, and using other marketing channels. Big data can correlate transaction data, online browsing behavior, in-store shopping trends, product preferences, and more. You also can incorporate external, unstructured data streams such as social media traffic to assess customer sentiment and behavior. The resulting insight can be invaluable in guiding inventory and pricing strategies.
2. Measuring brand sentiment – Brand studies using focus groups and customer polling techniques can be expensive and often aren’t that accurate. Using big data analytics, you can perform a customer brand sentiment analysis based on behavioral trends using sources such as Pinterest, Twitter, and Facebook, for example. The results are less biased and can be used to guide product development, advertising, and marketing programs.
3. Creating customized promotions – Big data analytics can be used to create custom offers based on browsing history and other data sources. These customized promotions can be used for localized marketing, pushing coupons and offers to smartphone users based on their location, or to drive e-commerce sales using real-time offers delivered via online advertising or social media.
4. Improving store layout – Big data can be used to analyze customer traffic flow within the store. Sensor data such as RFIDs or QR codes can be used to track in-store traffic and shopping habits. There also are new technologies emerging that enable in-store mapping for applications such as instant coupons that can tell retailers a lot about store flow.
5. Optimizing e-commerce – Clickstream data and monitoring online behavior can help optimize e-commerce sites. Without the assistance of big data, the sheer volume of clickstream data would be difficult to analyze. And retailers can incorporate other metrics such as social media shares, purchase history, and more to improve performance for e-commerce websites.
6. Order management – Big data can be invaluable for inventory management and tracking. For example, big data can inventory needs in order to facilitate real-time delivery. It can even be used to automate order processing to eliminate “out-of-stock” goods.
Retail Big Data in Action
A number of high-profile retailers have already harnessed big data to improve operations:
Target has one of the most well-known big data success stories. By correlating its baby shower registry with its Guest ID program, the retailer was able to identify other products that pregnant women were most likely to purchase. The Guest ID registry tracks data such as purchase history, returns, Web visits, customer support, and Web clicks. As a result, Target was able to target pregnant women with special offers, such as for moisturizing lotion. The same strategy is being applied to other shoppers using age, education, marital status, and more.
A pizza chain offers customers a mobile phone app for mobile marketing and to deliver special offers. By matching app user data to local weather conditions, the pizza maker was able to offer special delivery coupons to customers unable to cook due to power outages during a storm. The mobile/location-based program yielded a 20 percent response rate.
Kohl’s is testing real-time offers in its stores for shoppers who opt in to offers delivered via smartphone. For example, if shoppers visit the shoe department, the app can correlate their shopping and browsing history and deliver an instant coupon for the shoes they looked at online.
For retailers in particular, the value of big data analytics can apply to any aspect of their businesses. The challenge for VARs is to narrow the potential use cases to those in which the retailer will see immediate value. Identify retailers’ points of pain and develop a pilot program targeted to address those issues. Once you demonstrate the ROI from big data, the sky’s the limit.