Post by account_disabled on Dec 19, 2023 22:43:11 GMT -5
Of choices closer relationships can boost loyalty and help consumer goods companies maintain an edge against growing rivals. An academic’s perspective on mining online content with the help of machine learning to meet customer needs. An academic’s perspective from MIT Sloan School of Management marketing professor and Ph.D., MIT Sloan School of Management Insight into customer needs is critical for helping companies identify new product opportunities. and improving new product designs, existing products and services is critical. Today, consumers are creating a wealth of new content that addresses their product needs as they search, buy, review and discuss their purchases online.
But is this data a useful source of information for product innovators? Is it as valuable as traditional focus groups and experiential interviews? In recent research from , we found that, yes, important customer needs can be found efficiently and cost-effectively in online user-generated content by combining machine learning techniques with human analysts. We did ask two questions: What is the value of the information Job Function Email List presented in the content? Can machines augment human capabilities to make this process fast and efficient? To examine the first question, we worked with professional consultants who are experts in identifying customer needs from experiential interviews and had them review information from randomly selected interview transcripts and user-generated online product reviews.
While one might think online reviews are biased due to self-selection, with people only bothering to write reviews if they are either very happy or very unhappy, we found that almost every need related to a particular product was covered. We learned. We also discovered some needs that did not appear in the interviews. The second question is whether machine learning can successfully augment the human task of sifting through large amounts of content to extract meaningful information for review. The answer is also yes. Powerful algorithms identify content that is rich in information about customer needs, as well as content that is redundant.
But is this data a useful source of information for product innovators? Is it as valuable as traditional focus groups and experiential interviews? In recent research from , we found that, yes, important customer needs can be found efficiently and cost-effectively in online user-generated content by combining machine learning techniques with human analysts. We did ask two questions: What is the value of the information Job Function Email List presented in the content? Can machines augment human capabilities to make this process fast and efficient? To examine the first question, we worked with professional consultants who are experts in identifying customer needs from experiential interviews and had them review information from randomly selected interview transcripts and user-generated online product reviews.
While one might think online reviews are biased due to self-selection, with people only bothering to write reviews if they are either very happy or very unhappy, we found that almost every need related to a particular product was covered. We learned. We also discovered some needs that did not appear in the interviews. The second question is whether machine learning can successfully augment the human task of sifting through large amounts of content to extract meaningful information for review. The answer is also yes. Powerful algorithms identify content that is rich in information about customer needs, as well as content that is redundant.