From MIT Sloan Management Review
Deep learning is delivering impressive results in AI applications. Apple’s Siri, for example, translates the human voice into computer commands that allow iPhone owners to get answers to questions, send messages, and navigate their way to and from obscure locations. Automated driving enables people today to go hands-free on expressways, and it will eventually do the same on city streets. In biology, researchers are creating new molecules for DNA-based pharmaceuticals.
Given all this activity with deep learning, many wonder how the underlying methods will alter the future of marketing. To what extent will they help companies design profitable new products and services to meet the needs of customers?
The technology that underpins deep learning is becoming increasingly capable of analyzing big databases for patterns and insights. It isn’t difficult to imagine a day when companies will be able to integrate a wide array of databases to discern what consumers want with greater sophistication and analytic power and then leverage that information for market advantage. For example, it may not be long before consumers, identified via facial recognition technology while grocery shopping, receive individualized coupons based on their previous purchase behavior. In the future, advertisements may be individually designed to appeal to consumers with different personalities and be delivered in real time as they view YouTube. Deep learning might also be used to design products to meet consumers’ personal needs, which could then be produced and delivered through automated 3D printing systems.
Different types of organizations will try to harness the powers of deep learning in their own ways. An automaker might use them to target new customers, revamp the buying process, or fine-tune product features a specific set of buyers will want. It could draw on a sea of relevant data to do all this, including auto repair data, consumer ratings on vehicle quality and reliability, car registrations, Twitter posts concerning the car-buying and user experience, Facebook posts showing people with their cars, manufacturers’ consumer relationship management data files, and clicks on the internet. A bank, meanwhile, could leverage deep learning to develop new products or services and customize its promotions. By analyzing data on customer loan histories, credit card transactions, savings and checking account records, website clicks, social media behavior, product ratings, and search histories, it could gain insights into the things certain customers value. What do 40-year-old professionals living in urban neighborhoods want most in a credit card? Do they prefer travel rewards, buyer protection, cash back, or low interest rates?
To be sure, a lot of managers already use analytics with statistical models and focused databases to track brand performance, schedule promotions, and make spending decisions. So how is deep learning different? Is it a fundamental leap forward, or will it simply enable marginal gains? In this article, we will examine these questions in relation to a study we conducted involving credit cards. In addition, we will consider what this research suggests about the future direction of analytics.
Although we are still in the early days of deep learning, it isn’t too early to ask: What will it offer companies compared with the existing analytics methods managers are accustomed to? Can it provide better predictions, and if not, how can it be improved? And what kinds of investments in data and technology will companies have to make in deep learning to take advantage of the latest and most powerful capabilities? Our research suggests that, while deep learning may not lead to large gains in predictive accuracy right away or in every setting, there are reasons for optimism.
To compare deep learning with traditional methods for marketing analytics, we studied a large database of click-streams, demographics, and ad exposures relating to the credit card market from NerdWallet, a large online vendor of credit cards, based in San Francisco. We wanted to see if a multilevel deep learning model could predict credit card choices more accurately than traditional models.
The ability to predict customer choice is the first step toward improving decisions that go into product design, media resource allocation, how to promote the product (in this case, a credit card), and whom to target. Knowing what people value most requires experimentation and predictive choice modeling. Our hunch was that deep learning would provide a clearer, more useful picture than a simpler regression model. To test that assumption, we looked at the credit card selection processes of 260,000 individuals, taking into account 25 demographic factors (including obvious things like age, gender, and household income, and more detailed categories such as credit score, cards the consumer currently owns, and ZIP code); 132 attributes for each card (such as interest rate, whether the card offered reward points, travel miles, or cash back, and card fees for annual membership and balance transfers); and the cards each person applied for.
Read the full post at MIT Sloan Management Review.
Glen Urban is the David Austin Professor in Marketing, Emeritus, at the MIT Sloan School of Management.
John R. Hauser is the Kirin Professor of Marketing at MIT Sloan School of Management.
Artem Timoshenko is an assistant professor of marketing at Northwestern University.
Paramveer Dhillon is an assistant professor of information at the University of Michigan.