Is deep learning a game changer for marketing analytics? – Glen Urban, Artem Timoshenko, Paramveer Dhillon, and John R. Hauser

MIT Sloan Professor Glen Urban

Glen Urban, David Austin Professor in Marketing, Emeritus, and MIT Sloan School Dean, Emeritus

John R. Hauser, Kirin Professor of Marketing, MIT Sloan School of Management

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.

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How companies like United and Wells Fargo can win back consumer trust–John Hauser

MIT Sloan Professor John Hauser

MIT Sloan Professor John Hauser

From The Conversation 

It’s every CEO’s worst nightmare: For whatever reason, the CEO’s company is engulfed in negative publicity that threatens to damage its brand name, harm sales and alienate customers for months or even years to come.

The negative publicity can hit suddenly, seemingly out of the blue, or it can come in relentless waves, over a prolonged period of time, like a series of storms battering a coastal area, one after another. Wells Fargo and United Airlines have both been facing such an onslaught in recent weeks and months.

How does a company respond? How does it go about repairing a damaged brand name and winning back customers?

While I know very little about these particular situations apart from what I’ve read, seen, and heard via various media outlets, I know how difficult it is to change consumers’ minds about a company and its products – and how winning back “trust” is easier said than done.

Five years ago, my colleagues – Gui Liberali of the Erasmus School of Economics in Rotterdam and Glen L. Urban at the MIT Sloan School of Management – and I jointly published a study, “Competitive information, trust, brand consideration and sales: Two field experiments.” Here’s what we learned.

Regaining customer trust

Over two years, we closely tracked four marketing field experiments by an American automaker whose brand had suffered from decades of negative publicity over the quality of its products. The experiments focused on company actions to earn back trust.

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Increasing click-through rates with ad morphing — Glen Urban and John Hauser

MIT Sloan Professor Glen Urban

MIT Sloan Professor Glen Urban

MIT Sloan Professor John Hauser

MIT Sloan Professor John Hauser

From Fortune China

Everyone is trying to make their banner ads and new media more effective. In the banner area, 90% of the effort is spent on targeting. If you click on a link, you’ll get a particular ad. A whole industry has emerged focused on collecting click stream data and making recommendations.

But that is only half the picture. Equally important is the question of how you should talk to consumers once they are targeted. This is what ad morphing is all about.

For example, a car company may target a consumer whose click history indicates he is interested in buying a car. However, instead of just randomly sending him car ads, it can track the consumer’s online behavior to determine his preferred communication style. We also call this his cognitive or thinking style. Does the consumer want a picture of the car at a NASCAR race? Or would the consumer prefer to look at the technical aspects of the engine? Or does the consumer want a fashion shot of a driver pulling up to a country club? What will the consumer best respond to?

This is a multi-arm bandit problem because it’s like a slot machine with many arms. The advertiser needs to choose the ideal lever to pull to match the ad to the consumer’s thinking style. However, it’s more difficult with ads because there is uncertainty as to the consumer’s thinking style.

Our algorithm addresses this issue by monitoring click stream data to determine how a consumer makes decisions on the web. After enough information is gathered, the algorithm determines the consumer’s likely thinking styles and matches the optimal ad to our estimates of thinking styles – all in real time.

Partnering with companies like General Motors to test our algorithm, we found that morphing has tremendous potential to increase banner ads’ productivity. Companies work hard just to get a 1-2% improvement in click-throughs, but we found that morphing ads based on thinking styles can improve that rate up to 83%. We also found that morphing can lead to 30% better brand recognition. These are very significant effects.

While our algorithms (see our paper for the algorithms) can be implemented by any good programmer skilled in the art, morphing can challenge the budget. To use this tool, companies have to design more ads – ads that appeal to each of the various thinking styles of customers. There also may be cross-organizational issues, as the people who create those ads must coordinate with the analysts doing the targeting.

However, this is the only algorithm that we’re aware of that integrates thinking styles and morphing in real time. It’s very cutting edge, but it can help move the market to the next wave of action in banner advertising.

Also see the post in Chinese at Fortune China.

Glen Urban is the David Austin Professor in Management, Emeritus, Professor of Marketing, Emeritus, Dean Emeritus, and Chair of the MIT Center for Digital Business at the MIT Sloan School of Management. 

John Hauser is the Kirin Professor of Marketing and a Professor of Marketing at the MIT Sloan School of Management. 

How to improve products? Survey consumers with "active machine learning"

MIT Sloan Prof. John Hauser

When you buy a house, it would be irrational to search every possible house on the market. Instead, you narrow down your choices based on things like price, location, and number of bedrooms. The same thing happens when you buy a car. You might only look at sporty coupes or hybrid vehicles. Everyone has their own individual methods – or heuristic decision rules — for screening products, usually based on the item’s key features.

This presents a significant question for companies:  How do you determine what these decision rules are? Managers are increasingly interested in this topic as companies focus product development and marketing efforts to get consumers to consider their products or prevent them from rejecting the products without evaluation. If they better understood consumers’ heuristic decision rules, they could use this information in the design and marketing of new products.

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