Don't turn your marketing function over to AI just yet

Imagine a future in which a smart marketing machine can predict the needs and habits of individual consumers and the dynamics of competitors across industries and markets. This device would collect data to answer strategic questions, guide managerial decisions, and enable marketers to quickly test how new products or services would perform at various prices or with different characteristics.

The machine learning algorithms that might power such a device are, at least for now, incapable of producing such promising results. But what about tomorrow? According to a group of researchers, the envisioned “virtual market” machine could become a reality but would still require one missing ingredient: a soul.

The “soul” is our human intuition, scientific expertise, awareness of customer preferences, and industry knowledge—all capabilities that machines lack and intelligent marketing decisions require.

“Without a soul, without human insight, the capabilities of the machine will be limited,” a group of 13 marketing scholars write in their working paper, Soul and Machine (Learning), which takes a high-level view of the present and future role of machine learning tools in marketing research and practice. “We propose to step back and ask how we can best integrate machine learning to solve previously untenable marketing problems facing real companies?”

Combining man and machine
A product of the 11th Triennial Invitational Choice Symposium held last spring, the paper explains how machine learning leverages Big Data, giving managers new tools to help unravel complex marketing puzzles and understand consumer behavior like never before. Tomomichi Amano, assistant professor in the Marketing Unit at Harvard Business School, is one of the paper’s authors.

We tend to think that when we have all this rich data and this machine learning technology, that the machines are going to just come up with the best solution,” says Amano. “But that’s not something we’re able to do now, and to have any hope of doing that, we need to be integrating the specialized domain knowledge that managers possess into these tools and systems.”

Marketers have long envisioned the potential for technology to bring about a “virtual market”—an algorithm so sophisticated that multiple departments within the firm could query it for answers to questions ranging from optimal pricing to product design. What prevents this from materializing? After all, machine learning is delivering self-driving cars and beating human players on Jeopardy!

The answer: context specificity, says Amano.

The factors that influence consumer behavior are so varied and complex, and the data that companies collect is so rich, just modeling how consumers search a single retail website is a monumental task. Each company’s data are so firm and occasion-specific that building and scaling such models is neither feasible nor economical. Machine learning technology today excels at self-contained tasks like image recognition and content-sorting.

“The kind of tasks that we want to do in marketing tend to be more challenging, because we’re trying to model human behavior,” Amano says. “So the number of things the model cannot systematically predict is much larger. In other words, there’s lots of noise in human behavior.”

Instead of working to create the virtual market, marketers and marketing researchers are trying to break it down into more manageable pieces. Amano approaches this from an economic perspective, using basic economic principles—assuming customers prefer lower-priced products, for example—to build models that can begin to explain how consumers approach online search. (See Large-Scale Demand Estimation with Search Data.)

Other researchers are developing machine learning tools that can leverage content from customers’ product reviews to identify their future needs. But here the human analysts are key players. They must review the selected content and formulate customer needs, because natural language processing technology still lacks the sophistication to infer them. Increasingly, this hybrid approach is allowing companies to replace traditional customer interviews and focus groups, according to Amano and his colleagues.

Understanding what prompts a customer to purchase a product—a concept known as attribution—is an area ripe for new hybrid tactics, says Amano. For example, a customer exposed to three different ads for a cell phone—on a bus, on TV, and online—talks to his or her friends about cell phones and then buys the phone from the ads a week later.

Regardless of how much data is collected, “we don’t know how much that bus ad you saw contributed to your purchase of the cell phone,” Amano says. “We don’t know how to model that, and we don’t know how to think about it, but it’s a really important question, because that informs whether you run another ad on the bus.”

Here’s where managerial insight and behavioral theory can guide firms’ use of data and machine learning to gain new knowledge about current and potential market segments. “It might be that people on the bus use their cell phones more,” Amano posits, “so they just tend to buy cell phones more often.”

Personalization vs. privacy
Managers who implement marketing tactics and analytics that meld human capital and the machine learning toolbox stand to improve decision-making and product development. But doing so requires careful consideration of the balance between personalization and privacy. At what point do curated online product recommendations become so creepy or intrusive that they sour customers on the brand?

Amano points out that the benefits of personalized marketing are often overshadowed by the creepiness factor. “There definitely are a bunch of benefits that we reap from the fact that firms and governments have access to more of our data,” he says, “even though some of those benefits are hard to see.”

Receiving information about available products is one benefit to consumers. In the case of government, the marketing scholars who attended the Choice Symposium contend that machine learning will soon augment or replace expensive survey-based data gathering techniques to keep important indices, such as unemployment rates, up to date.

“Machines can scrape at high frequency to collect publicly available information about consumers, firms, jobs, social media, etc., which can be used to generate indices in real-time,” the scholars write. “With careful development, these measures will be more precise and able to better predict the economic conditions of geographic areas at high granularity, from zip codes to cities, to states and nations.”

But privacy concerns among consumers are real and growing, and marketing professionals and scholars are still trying to understand the implications.

“Facebook and Google—these services are free from a monetary perspective, but I think there’s some recognition that we are paying some cost in using them, by giving out some of our data, and from that perspective, there is some more research we have to do on the academic front to make sure we understand how firms ought to be responding to these concerns,” Amano says.

Managers, in the meantime, must rely on their own insight and experience to find the answer to that question and others. They also need to keep their expectations realistic when it comes to the capacity of machine learning tools, says Amano, and employ people who can communicate effectively about data-based approaches. Ultimately, managers who have the foresight to collaborate with data analysts to design data collection efforts and stagger promotions will be well positioned to harness the power of new machine learning tools in marketing.

“You can’t do something in business, and then collect the data, and then expect the machine learning methods to spit out insight for you,” Amano says. “It’s important that throughout the process you consult and think about your goals and how what you’re doing is going to influence the kind of data you can collect.”

About the Author
Kristen Senz is a writer and social media creator for Harvard Business School Working Knowledge.

This article was provided with permission from Harvard Business School Working Knowledge.