“With this technology, we can respond to lifestyles that change from moment to moment, quickly discover true individualized needs that may have been overlooked, and devise appropriate measures. In addition to estimating detailed customer profiles, the technology can also be applied to estimating product attributes,” said Akio Yamada, General Manager, Data Science Research Laboratories, NEC Corporation.NEC claims that with this new technology, an analysis that would take conventional experts three months to complete can be carried out in three days, and to a degree of precision that surpasses those experts. The technology will also deploy an artificial intelligence (AI) component to produce a hypothetical detailed profile for each customer from purchase histories. The profile is then cross-verified using another interconnected AI that is fed with questionnaire results obtained from a subset of customers, checking the accuracy of the hypotheses. This cycle of hypothesis generation, verification and feedback is repeated. According to NEC, the use of AI allows objective detailed profile estimation which are not affected by the subjective views of an analyst. By repeating the cycle many times at high speed, the company believes that it can achieve detailed customer profiles to a degree of precision. NEC plans to continue to pursue research and development of the technology, aiming to provide it in the retail and distribution sectors, including department stores, supermarkets, convenience stores, e-commerce sites and point card systems.
Japanese provider of information technology services and products has announced the development of a Customer Profile Estimation Technology that automatically estimates detailed customer profiles on individual customers, including their interests and preferences without the involvement of marketing experts. The new technology is designed to estimate detailed profiles on individual customers completely automatically from basic profile information that is relatively easy to obtain, such as age and gender, combined with purchase histories.
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