Is Deep Learning a Game Changer for Marketing Analytics?
Companies are already making sophisticated marketing decisions with data and analytics. Will deep learning enable a leap forward — or just marginal gains?
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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.
References
1. A. Burnap, J.R. Hauser, and A. Timoshenko, “Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach,” working paper, MIT Sloan School of Management, Cambridge, Massachusetts, July 2019.
2. P. Dhillon and S. Aral, “Modeling Dynamic User Interests: A Neural Network Approach,” working paper, MIT Sloan School of Management, Cambridge, Massachusetts, 2019.
3. L. Liu, D. Dzyabura, and N. Mizik, “Visual Listening In: Extracting Brand Image Portrayed on Social Media,” working paper, Social Science Research Network, 2019.
4. A. Timoshenko and J.R. Hauser, “Identifying Customer Needs From User-Generated Content,” Marketing Science 38, no. 1 (January-February 2019): 1-20.
5. G. Urban, J. Hauser, G. Liberali, et al., “Morph the Web to Build Empathy, Trust, and Sales,” MIT Sloan Management Review 50, no. 4 (summer 2009): 53-61.
i. For further information, see I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning” (Cambridge, Massachusetts: MIT Press, 2015); A. Géron, “Hands-On Machine Learning With Scikit-Learn and TensorFlow” (Sebastopol, California: O’Reilly Media, 2017); and R.S. Sutton and A.G. Barto, “Reinforcement Learning: An Introduction” (Cambridge, Massachusetts: MIT Press, 2018).