Firms must take consumer psychology into account and resist the temptation to maximise short-term profits at the cost of consumers
Precise targeting boosts companies’ profitability, while letting consumers enjoy convenience and offers fitting their needs
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From segmentation to pricing, virtually all processes involved in marketing can now be automated. The ability to track individuals’ behaviour online and to merge data sources increasingly allows marketers to target consumers at a granular level. Thanks to machine learning-based algorithms, individuals can receive tailored product offers and advertisements – all in real time.
Such precise targeting boosts companies’ profitability, while letting consumers enjoy convenience and offers fitting their needs. However, it may also lead to negative economic and psychological consequences for consumers. The question becomes, how to make sure that marketing automation doesn’t create a dystopia?
In one experiment, recruitment company ZipRecruiter.com saw it could boost its profits by more than 80 percent by adopting algorithm-based individualised pricing, using more than a hundred consumer variables. Uber reportedly uses machine learning to set route- and time-of-day-specific prices. Uber could easily use customers’ ride histories and other personal data, to personalise prices even further.
These developments can be alarming for consumers. While personalised pricing may benefit consumers with a lower WTP who might otherwise be priced out of the market, many consumers are likely to end up paying prices closer to their WTP.
In research I conducted with INSEAD’s Daniel Walters and Geoff Tomaino, consumers were found to systematically underprice their private data when they bartered it away for goods or services as opposed to selling it for money. Take users of social media platforms. They “pay” for these services with private data, which the platforms use to generate advertising profits. Our experiments suggest that consumers undervalue their private data in such non-monetary exchange settings, despite knowing how profitable social media platforms are. This uneven exchange of value likely contributes to the extraordinary valuations of dominant tech firms.
Further experiments I conducted with Wharton’s Rom Schrift and Yonat Zwebner showed that consumers act as if they experience a threat to their autonomy when they understand that algorithms can predict their choices. When participants learnt that an algorithm could predict their choices, they chose less preferred options to re-establish their sense of autonomy. To maximise acceptance of prediction algorithms, marketers will need to frame them such that they don’t threaten consumers’ perceived autonomy.
GDPR Articles 13 through 15 require firms to provide customers with “meaningful information about the logic involved” in such automated decisions. In another set of experiments, informing rejected consumers about the goals of an algorithm was just as meaningful to them as knowing how the algorithm arrived at its negative assessment. Consumers derived a sense of fairness from understanding the purpose of the algorithm.
Avoiding a marketing automation dystopia is in the best interest of all market participants – at least in the long term. With that horizon in mind, companies must take consumer psychology into account and resist the temptation to maximise their short-term profits at the expense of consumers.
This article is an adaptation of an original piece published in the NIM Marketing Intelligence Review.
Klaus Wertenbroch is the Novartis Chaired Professor of Management and the Environment and a Professor of Marketing at INSEAD. He directs the Strategic Marketing Programme, one of INSEAD’s Executive Education programmes.
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