Knowledge is power — and in 2017 it can boost your retail profitability.
To accurately anticipate shopping trends this year, more retail companies will invest in predictive analytics, the big data equivalent of a crystal ball. Predictive analytics represent a popular area of big data that uses historical data to forecast future outcomes. In fact, many experts have been talking about it this week at the National Retail Federation (NRF) Big Show 2017 in New York.
It’s a hot topic because it’s transforming the retail industry. Just as financial investors use past data to predict the stock market’s future performance, predictive analytics help companies use historical sales data to make sophisticated guesses about which products an individual shopper will buy in the future.
Gaining a glimpse into the future is a CEO’s dream. As such, experts predict the global predictive analytics market will grow from $2.7 billion in 2015 to $9.2 billion by 2020 at an impressive compound annual growth rate of 27%.1Markets and Markets. Predictive Analytics Market by Business Function – Global Forecast to 2020. December 2015. The US is the world’s largest market for predictive analytics and US demand is expected to reach $3.6 billion by 2020.2The Global Predictive Analytics Market: Trends, Drivers & Predictions. Global Industry Analysts, Inc. October 2015.
Why Retail is Embracing Predictive Analytics
Knowing your customers more intimately can give you a competitive edge and improve your business results by making your strategy more customer-centric. In our digital age, consumers expect brands to intuitively respond to their individual needs almost immediately. This means retail marketing now requires data-driven decisions to enhance the customer experience, boost sales and strengthen loyalty.3Hurley, Sam. Big Data & Predictive Analytics – Beyond the Buzzwords. Spark Lane. September 19, 2016.
Predictive analytics can give retailers and suppliers greater clarity and confidence, and help them make better business decisions on topics such as product assortments, forecasts and marketing strategies. These data insights replace uncertainty with probability with future-oriented predictive intelligence.4The Global Predictive Analytics Market: Trends, Drivers & Predictions. Global Industry Analysts, Inc. October 2015. 5Davenport, Thomas H. A Predictive Analytics Primer. Harvard Business Review. September 2, 2014.
In retail, predictive analytics combine consumer data, product data and business data to help companies connect the dots, and discover patterns related to retail processes, sales and customer behavior.6Hurley, Sam. Big Data & Predictive Analytics – Beyond the Buzzwords. Spark Lane. September 19, 2016. Specifically, predictive analytics can help companies foresee retail trends, forecast demand, optimize pricing and identify customers by making sense of consumer data from across omnichannel retail touchpoints, including in-store sales, online browsing and social media engagement.
Predicting a customer’s spending habits can make their shopping experience more personalized and relevant.7Marr, Bernard. Big Data: A Game Changer in the Retail Sector. Forbes. November 10, 2015. By anticipating what shoppers want, predictive analytics can help companies boost sales and maximize lifetime value per customer, and reduce shipping, inventory and supply chain costs.8Ulanoff, Lance. Amazon Knows What You Want Before You Buy It. Predictive Analytics Times. January 27, 2014.
Diverse Retail Companies Use Predictive Analytics
In the intensely competitive grocery sector, retailers Whole Foods Market and Kroger use predictive analytics to gain a competitive advantage.9Karolefski, John. Not Quite Ready for Prime Time. Progressive Grocer. April 2016. E-commerce leader Amazon uses data to know exactly what products people buy, browse and return. Now Amazon applies those deep, data-driven insights to predictive analytics to make decisions on its product assortment strategy. Using predictive analytics helps Amazon maximize sales by filling its store shelves and endless aisles online with the merchandise shoppers really want.
Suppliers also embrace predictive analytics to determine where their supply chain needs to be “to satisfy the increasing demands of consumers who expect products delivered exactly when promised.” This big data innovation boosts suppliers’ agility and accelerates the speed to market to help them deliver orders on time. A Deloitte study found less than 25% of manufacturers had adopted predictive analytics as of 2015; however, experts expect that number to climb to 70% between 2018 and 2020.10Blanchard, Dave. Predictive Analytics Let Manufacturers See More Clearly into their Supply Chains. IndustryWeek. March 27, 2015.
Even small retail companies are starting to use predictive analytics to broaden their time horizon when developing a strategic plan to achieve more accurate demand forecasting.”11Knudson, Julie. Can Predictive Analytics Help Your Small Business? Small Business Computing. May 18, 2015.
To anticipate your customers’ needs with greater certainty this year, consider how predictive analytics could help you. By deepening your understanding of and relationship with individual consumers, predictive analytics can help your company make shoppers feel special, understood and willing to buy.