DRAWING ON BIG DATA FOR CUSTOMER INSIGHTS

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The overall trend for retail sales volume is up, despite considerable disruption to the channels consumers use to make purchases. Total retail sales are expected to grow 3.7% to 4.2% in 2017, driven in large part by online sales, which are forecast to grow 8% to 12% this year.[1]

Retail sales growth is being driven by technology advances and data analytics. While chain retailers close stores and refocus sales and distribution for online sales, many smaller businesses are participating in e-commerce platforms such as Amazon Marketplace to access their distribution systems and consumer reach. Deloitte recently reported Amazon Marketplace is growing twice as fast as Amazon’s own direct business.[2]

Big data analytics are helping retailers of all sizes retain customers and win new business. Some of the ways big data is driving sales include:[3]

  • Purchase recommendations – The transaction history of a customer can be compared with the histories of customers with similar purchasing profiles to fuel machine learning models. Accurate purchase recommendations can be generated immediately, while the customer is still on the website.
  • Customer 360 initiatives – Many companies strive to obtain a complete view of their customers by gathering data from the wide range of touch points that customers use to purchase products and obtain support. Big data technology and real-time processing enable retailers to easily merge and manipulate multiple data sets. This helps companies deliver a higher level of customer service by anticipating customer needs, communicating through social media channels and having desired products available immediately.
  • Market basket analysis – Big data technology speeds up this form of traditional marketing analysis that anticipates which products will likely be purchased together, such as diapers and baby formula. Big data automates market basket analysis and enables companies to work with larger data sets for better results.
  • Path to purchase – Both omnichannel selling and multi-channel marketing have increased the ways customers can reach a retailer and make a purchasing decision. Big data technology enables a more precise way to understand customer buying patterns by analyzing data derived from many sources.
  • Trend forecasting from social media posts – Technology platforms like Hadoop enable the handling and analysis of large amounts of data derived from social media sites, such as Facebook posts or Pinterest pins. This helps retailers spot emerging trends earlier.
  • Price optimization – Retailers can check pricing electronically through computer program “daemons” that crawl through competitor websites to gather detailed product pricing information. Combined with machine learning techniques, these tools can automatically set optimal pricing for products.

[1] “National Retail Federation estimates 8-12% US e-commerce growth in 2017,” Business Insider Intelligence, Feb. 10, 2017. Available at: http://www.businessinsider.com/national-retail-federation-estimates-8-12-us-e-commerce-growth-in-2017-2017-2

[2] “Retail, Wholesale and Distribution Industry Outlook 2017,” Deloitte research study, 2016. Available for download at: https://www2.deloitte.com/us/en/pages/consumer-business/articles/retail-distribution-industry-outlook.html

[3] “9 Ways Retailers Are Using Big Data and Hadoop,” by Alex Woodie, Datanami blog, July 20, 2016. Available at: https://www.datanami.com/2016/07/20/9-ways-retailers-using-big-data-hadoop/

This news is provided as a service to you by Marlin Business Services Corp., a nationwide leader in commercial lending solutions for the U.S. small business sector. Marlin’s equipment financing and loan programs are available directly and through third-party vendor programs, including manufacturers, distributors, independent dealers and brokers, to deliver financing and working capital that help build your success.

 

Drawing on Big Data for Customer Insights