Breakthrough Machine Learning Study Published in Marketing Science
A new study published in Marketing Science describes how to use machine learning to expedite the discovery of customer needs from user-generated content.
WALTHAM, Mass., March 9, 2019 /PRNewswire-PRWeb/ -- "Identifying Customer Needs from User-Generated Content" by Artem Timoshenko and John Hauser of the MIT Sloan School proposes a new machine-learning algorithm that identifies customer needs for marketing strategy and product development. This approach, combining machine-learning methods and human analysts, identifies relevant content and removes redundancy from user-generated content (UGC). As a result, machine learning, when partnered with professional analysts, leads to better and faster innovation across a myriad of industries.
The authors' research shows that UGC, such as online reviews, message boards or social media posts, is just as valuable, if not more valuable, than conventional methods of market research, such as interviews and focus groups. The machine-learning process greatly improves the efficiency of identifying customer needs from large sets of UGC so that brands can better understand what consumers want.
Boston-based market research firm Applied Marketing Science (AMS) was instrumental in the development, testing, and refinement of the machine-learning algorithm, and has used Timoshenko and Hauser's approach various times to help clients identify robust consumer insights.
"AMS has always been on the cutting edge of new market research techniques. We are excited about this application of machine learning because it allows us to improve the process for identifying unmet customer needs, quickly and at a lower cost to our clients than traditional research", said President and Managing Principal of AMS, John C. Mitchell. "As people continue to share their thoughts about product and brands online, the ability to quickly and thoroughly examine UGC with machine learning will become even more critical" said Mitchell.
Get access to the full Marketing Science article "Identifying Customer Needs from User-Generated Content" on the INFORMS website: https://pubsonline.informs.org/doi/abs/10.1287/mksc.2018.1123.
For more information on Applied Marketing Science and their consulting services, please visit ams-insights.com.
Contact Info:
Name: Rachelyn Provencher
Organization: Applied Marketing Science
Address: 303 Wyman Street, Waltham, MA 02451
Phone: +1-781-250-6300
Email: RProvencher (at) ams-inc (dot) com
SOURCE Marketing Science
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