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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.
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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).