This article introduces at a conceptual level a system based on AI technologies, able to determine the customer profile, in order to support customer experience design and management accordingly to a customer-centered approach, by extracting information from video stream provided by the security cameras installed in a store. The system collects customer demographic and behavioral information (e.g., age, gender, time spent in determined areas of the store, time spent interacting with the salesperson, etc.) through Deep Learning algorithms, in a completely anonymous way, without saving bio-metric data. To predict the customer profile based on the collected data it exploits a Bayesian Belief Network (BBN). The paper describes the overall system architecture, details the method used to model the BBN and reports, through the description of a use case scenario, some examples of insights useful to guide the choice of possible actions to be taken to improve the customer experience strategy.

A System to Support the Design and Management of Customer Experience Based on a Customer-Centered Approach

Andrea Generosi;
2022-01-01

Abstract

This article introduces at a conceptual level a system based on AI technologies, able to determine the customer profile, in order to support customer experience design and management accordingly to a customer-centered approach, by extracting information from video stream provided by the security cameras installed in a store. The system collects customer demographic and behavioral information (e.g., age, gender, time spent in determined areas of the store, time spent interacting with the salesperson, etc.) through Deep Learning algorithms, in a completely anonymous way, without saving bio-metric data. To predict the customer profile based on the collected data it exploits a Bayesian Belief Network (BBN). The paper describes the overall system architecture, details the method used to model the BBN and reports, through the description of a use case scenario, some examples of insights useful to guide the choice of possible actions to be taken to improve the customer experience strategy.
2022
978-3-030-91234-5
Customer experience
Customer profiling
Machine learning
Predictive models
Video analysis
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12607/48782
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact