Marketing Utopia – Individual real time access to consumers for convenient and relevant offers
Marketing has undergone revolutionary changes in the last decade. Virtually all processes involved in marketing can now be automated, from segmentation and targeting to service provision, advertising, distribution, retailing, and pricing. The ability to track individuals’ behavior online and to merge multiple data sources into “big data” sets increasingly allows marketers to target consumers individually. Machine learning-based algorithms can tailor product offers, advertisements, and prices to individuals in real time: Utopia has become real for marketers. Such personalization boosts companies’ profitability from more accurate price discrimination, and consumers enjoy convenience and offers tailored to their needs. However, automating and personalizing interactions may also have less positive economic and psychological consequences for consumers, among them higher individual prices and threats to their perceived autonomy.
Higher individual prices for consumers
Companies can maximize profits when every customer pays a price for a product which is close to his or her willingness to pay (WTP). In the past, individual WTP was impossible to determine, often allowing consumers to shop for less than they would be ready to pay. Today, machine learning-based prediction algorithms can approximate individuals’ preferences and their WTP at ever greater levels of precision, and they can create personalized offers reflecting this knowledge. In one experiment, recruiting company ziprecruiter.com found that it could increase profits by more than 80% when switching from its historical uniform pricing to algorithm-based individualized pricing, using more than a hundred input variables, by which it could characterize each of its customers. Uber’s route-based pricing reportedly uses machine learning to determine route and time-of-day-specific prices that take various demand conditions into account. Uber could easily use customers’ ride histories and other personal data, along with information that machine learning can extract from linking different riders’ data, to derive even more personalized prices. While these possibilities help companies advance their profit and shareholder value maximization objectives, they are alarming for customers. Personalized price discrimination may benefit consumers with a lower WTP who might otherwise be priced out of the market, but, overall, consumers likely end up paying prices closer to their WTP, leaving them with less surplus, especially consumers with a higher WTP.