NEC Develops AI-based Customer Profile Estimation Technology

NEC Corporation announced the development of a Customer Profile Estimation Technology that automatically estimates detailed customer profiles on individual customers, including their interests and preferences to a high level of precision, without the involvement of marketing experts.

Based on NEC’s unique relationship mining technologies, the newly-developed technology estimates detailed and hard-to-obtain profiles on individual customers completely automatically and to a high degree of precision from basic profile information that is relatively easy to obtain, such as age and gender, combined with purchase histories.

NEC has verified the effectiveness of the technology using public data. As a result, NEC has confirmed that 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 (*).

“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.

Moving forward, NEC will 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.

Main features of the new technology include the following:

1. Automatically estimates detailed profiles based on basic profile data and purchase histories. Through NEC’s unique relationship mining technologies, detailed profile information for each customer can be estimated by simply inputting their basic profile information and purchase histories. As the fully automated process completely eliminates tasks such as manual product labeling, detailed profiles on each individual customer can be estimated in a short time, thereby cutting three months down to three days.

2. A process of hypothesis generation and verification is repeated to achieve an estimation precision that surpasses an expert. An artificial intelligence (AI) component references product purchase histories to produce a hypothetical detailed profile for each customer. This is interconnected with another AI that references questionnaire results provided by a subset of customers to verify the accuracy of the hypotheses. This cycle of hypothesis generation, verification and feedback is repeated. The use of AI allows objective detailed profile estimation untainted by the subjective views of an analyst. By repeating the cycle many times at high speed, the estimation of detailed customer profiles to a degree of precision that surpasses the analysis results of an expert is achieved.