Identifying Customer Needs from User-Generated Content

By Artem Timoshenko, John R. Hauser. Marketing science (Providence, R.I.), 2019

437 citations recorded by the source corpus.

We evaluate user-generated content as a source of customer needs and propose and test a machine-learning approach for identifying customer needs more efficiently.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ed636bc7b976b64e7113bf219df563ce33d1cddf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Roland T. Rust
2. Ming-Hui Huang
3. Daniel Belanche
4. Luis Vicente Casaló
5. Carlos Flavián

In Marketing

1. Ming-Hui Huang
2. Roland T. Rust
3. Xueming Luo
4. Peter C. Verhoef
5. Carlos Flavián
6. Luis Vicente Casaló
7. Daniel Belanche
8. Christian Homburg

In Sociology and Political Science

1. Roland T. Rust
2. Ming-Hui Huang
3. Anil Bilgihan

Citations in the corpus

• A strategic framework for artificial intelligence in marketing (2020, background): “…conversation ” with AI (Avery and Steenburgh 2018), consumer consideration heuristics can be understood by machine learning (Dzyabura and Hauser 2011), and customer needs can be identified from user-generated content using convolutional neural network machine learning (Timoshenko and Hauser 2019).”