Need for speed: Algorithmic marketing and customer data overload
Joshua Goff, Paul McInerney, Gunjan Soni
Big data has grabbed headlines primarily because of its quantity and complexity. But what often gets lost in the discussion is the nature of speed. Not only do instant-gratification consumers today want responses in real time; the sheer mass of data also requires speedy processing so companies can do something useful with it. It’s no use getting a great piece of insight after the customer has walked out the door. Algorithmic marketing is already starting to solve that speed-to-market conundrum. Employing advanced analytical methods, algorithmic marketing provides real-time offers targeted to individual customers through a “self-learning” process to optimize those interactions over time. That can include predictive statistics, machine learning, and natural language text mining. It harnesses big data such as customer location and behavioral information along with powerful computing systems to match customers with context-sensitive products and services. To go algorithmic, companies need to move from batch systems (where work is done at regular intervals) to algorithmic systems (real time updates). The way a batch system works, for example, is a retailer tracks keywords on a spreadsheet and uploads them once a week or once a day. Alg orithmic marketing, however, tracks keywords automatically and makes updates every 15 seconds based on changing search terms, ad costs, customer behavior, etc. It can make price changes on the fly across thousands of products based on customer behavior, price comparisons, inventory, and predictive analysis. Algorithmic profits Algorithmic marketing is allowing companies to do things they couldn’t do before, and some early signs show it can deliver big value, especially in financial or information services. In North America, Amazon.com grew 30 to 40 percent, quarter after quarter, throughout the United States’ 2008-2012 recession, while other major retailers shrank or went out of business. From 2006 to 2010, Amazon spent 5.6 percent of its sales revenue on IT, while rivals Target and Best Buy spent 1.3% and 0.5%, respectively. That investment and focus has yielded increasingly sophisticated recommendation engines that deliver over 35 percent of all sales, an automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that are a key component of its best-in-class customer satisfaction, and dynamic pricing systems that crawl the Web and react to competitor pricing and stock levels by altering prices on Amazon.com, in some cases every 15 seconds. 20