The Disruptive Potential of Algorithmic Marketing

Stephen DeAngelis

July 17, 2012

“A massively disruptive transformation is taking at place leading companies,” writes Gunjan Soni, Joshua Goff, and Paul McInerney, “a disruption that could rewrite the entire traditional marketing canon.” [“By the numbers: Unleashing the power of algorithmic marketing,” McKinsey & Company, May 2012] The disruptive force behind this transformation is what Soni and her colleagues call “algorithmic marketing.” They describe it as “a new, scientific form of marketing.” They explain:

“Instead of gut instinct and experience, companies are turning to ‘big data,’ algorithms, and automation to win and retain customers. … It is redefining how companies interact with and sell to customers, and it will require companies to consider new strategies, technologies, and talent to cope with competitors who are skilled at leveraging ‘big data’ and fast computers.”

All companies want to get the most bang for their advertising buck. Algorithmic marketing holds the promise of allowing companies to target potential consumers in a much more enlightened way. To explain how, the McKinsey analysts begin their report with a brief history of how algorithms have been used on Wall Street. Because “algo-trading” or “robo-trading” has become so prominent, Wall Street offices are no longer filled only with economists, traders, and bankers. Physicists, mathematicians, and computer scientists now rub shoulders with financiers. Soni, Goff, and McInerney write, “As a result, algorithmic trading transformed Wall Street in less than a decade, made firms much more nimble, and provided competitive advantages to the ones with the best data, technology, and advanced analytics teams.”

 

Although “algo-trading” has not been without it glitches, its overall success has been noticed in other economic sectors. That is why McKinsey analysts believe “that something similar is about to happen in the world of marketing; leading companies are beginning to embrace algorithmic marketing much as Wall Street embraced algorithmic trading.” They continue:

“Algorithmic marketing here translates to an approach for scientifically managing a full range of marketing issues, including targeted offers, communications, pricing, and more. It employs an evolving set of advanced analytical methods that 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.”

Some people are wary of such targeted advertising, but for businesses it makes a great deal of sense to spend less and get better results. Algorithmic marketing can also be a benefit to customers as well. Soni and her colleagues explain that “it also helps to optimize customer processes and experiences, such as customer service, and plays a role in core operational processes like cell call routing, payment processing, and risk assessment.” You hear terms like the “information age” or “era of Big Data” discussed all the time. What is becoming ever more apparent is that we are entering the “age of algorithms.” In a popular talk given during TEDGlobal in early 2012, Kevin Slavin “argues that we’re living in a world designed for — and increasingly controlled by — algorithms.” To listen to his talk, see my post entitled The Big Data Dialogues, Part 5: Algorithms. Algorithms are used for many tasks and the McKinsey analysts want to make sure that we understand that “algorithmic marketing is distinct from data mining practices, which form the basis for most of today’s customer lifecycle management (CLM) and related data-driven marketing practices.” They explain:

“A key differentiator is that current data mining practices usually rely on retrospective data analyses, which lead to static campaigns and are not typically automated or executed in real time. There are already some early signs that suggest that the algorithmic marketing transformation is underway, especially in industries that rank high in terms of big data value potential and ease of capture indices, such as financial or information services. As algorithmic marketing takes hold, it is likely that big winners – and potentially even bigger losers – will emerge in the near future.”

Most companies want to “win big” but no company wants to “lose big.” If the analysts are correct, and the game is about to change in the “near future,” then companies can’t afford to wait to determine how they are going to use algorithmic marketing to enhance growth. The analysts believe that Amazon.com provides clear anecdotal evidence that change is coming. They write:

“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, in the case of Circuit City, Borders, and Linen’s ‘N Things, went out of business. While Amazon’s success isn’t news, how they achieved these results may be surprising. 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. The results of this spending and focus include:

  •  Sophisticated recommendation engines that deliver over 35 percent of all sales.
  • Automated e-mail/customer service systems (90 percent are automated, versus 44 percent for the average retailer) that deliver best-in-class customer satisfaction (Amazon’s American Customer Satisfaction score is 87).
  • A sophisticated and highly efficient supply chain that has reduced Amazon’s cost of goods sold by 3 to 4 percent.
  • 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.”

 

Soni, Goff, and McInerney also discuss a Latin American bank that marked similar growth by investing “disproportionately in big data, technology, and advanced analytics.” They point out that Citibank announced earlier this year “that it will partner with IBM to explore how IBM’s Watson, a powerful artificial intelligence computer system capable of answering questions in natural language and quickly processing huge amounts of structured and unstructured data, can help analyze customer needs and process vast amounts of up-to-the-minute financial, economic, product, and client data.” They also note that telecommunications companies are now pursuing the benefits of algorithmic marketing.” The conclusion drawn by Soni, Goff, and McInerney is:

“Over time algorithmic marketing will become a new type of corporate asset that cuts across business units and functions and determines the success and perhaps the very survival of companies across consumer sectors. As a result, leaders need to start thinking right now, about whether they have the right strategy, technology, and talent to exploit its full potential.”

Soni, Goff, and McInerney believe that recruiting and retaining the best talent is one of “three imperatives” that organizations must pursue if they are “to thrive and potentially survive in the new world of big data and algorithmic marketing.” In numerous past posts, I’ve stressed the importance of people, processes, and technology. Success, however, always begins with the right people. The authors write:

“In order to successfully hire and retain top mathematicians, computer scientists, and other technical experts, organizations must help them thrive in an environment that has not traditionally leveraged these skills. The ability to compete for and retain this talent will likely form a key source of competitive advantage, since it is likely that these skill sets will be in short supply during the next three to five years.”

The next imperative that organizations must pursue is leveraging big data and algorithmic marketing to drive innovation. The authors write that company executives must ask themselves, “How can [our] organization leverage algorithmic marketing to make better decisions faster and manage the business in ways that weren’t possible before?” They go on to provide several examples of how companies have leveraged big data and algorithmic marketing to improve business.

 

The final imperative that companies must pursue is building the infrastructure required to enable algorithmic marketing and inn0vation at scale. The authors explain:

“Technology clearly plays a key enabling role in making algorithmic marketing possible. Firms must therefore invest in creating integrated information systems that not only transcend organizational silos but also align with upstream and downstream suppliers and partners. In addition, organizations need to introduce automated computing systems and algorithms capable of delivering real-time products and services. Big data and intelligent, self-learning algorithms offer the potential for companies to conduct controlled real-world testing in order to minimize errors of judgment and interpretation.”

As President/CEO of a company that is developing a general cognitive reasoning platform that can be applied to problems in the manufacturing, CPG, and retail industries, I agree with study’s conclusion that “algorithmic marketing will become a new type of corporate asset that cuts across business units and functions and determines the success … of companies across consumer sectors.”