SegmentSizeEstimator, a research tool of the Acua Platform

Wondering what factors contribute to high levels of online engagement? In our research, we have found that one of the most reliable predictors of level of engagement for an ad, online content, or social media post for a given channel is simply size of the target population. For example, we’ve ranked viewers of YouTube channels…… Continue reading SegmentSizeEstimator, a research tool of the Acua Platform

 Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment

For segmentation, one often need to use sentiment analysis services. Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use…… Continue reading  Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment

What Really Matters?: Characterizing and Predicting User Engagement of News Postings Using Multiple Platforms, Sentiments, and Topics

This research characterizes user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes,…… Continue reading What Really Matters?: Characterizing and Predicting User Engagement of News Postings Using Multiple Platforms, Sentiments, and Topics

Measuring 9 emotions of news posts from 8 news organizations across 4 social media platforms for 8 months

Using Plutchik’s wheel of emotions framework, we identify the emotional content of 133,487 social media posts and the audience’s emotional engagement expressed in 2,824,162 comments on those posts. We measure nine emotions (anger, anticipation, anxiety, disgust, joy, fear, sadness, surprise, trust) and two sentiments (positive and negative) using two extraction resources (EmoLex, LIWC) for eight…… Continue reading Measuring 9 emotions of news posts from 8 news organizations across 4 social media platforms for 8 months

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and…… Continue reading Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websitesThis research compares four standard analytics metrics from Google Analytics with SimilarWeb using one year’s average monthly data for 86 websites from 26 countries and 19 industry verticals. The results show statistically significant differences between the two services…… Continue reading Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites

Geographic Mobility and Market Segmentation

Introduction and the Research Problem Alan R. Andreasen proposed a study titled “Geographic Mobility and Market Segmentation,” published in the Journal of Marketing Research in 1966. The study highlights the value of geographic mobility as a dimension for consumer market segmentation. Besides, the author discusses a tentative theory related to geographic mobility and shows evidence…… Continue reading Geographic Mobility and Market Segmentation

Customer Data Mining for Lifestyle Segmentation

Introduction and the Research Problem V.L. Migueis, A.S. Camanho, and Joao Falcao e Cunha proposed a study titled “Customer Data Mining for Lifestyle Segmentation” published in the Journal of Expert Systems with Applications, published by Elsevier in 2012. The study discusses that maintaining a good relationship with the customer base ensures companies gain a competitive…… Continue reading Customer Data Mining for Lifestyle Segmentation

What Happened to Strategic Segmentation?

Introduction and the Research Problem Angus Jenkinson proposed a study titled “What happened to strategic segmentation?” published in the Journal of Direct, Data and Digital Market Practice in 2009. The study discusses segmentation performed from descriptive, predictive, and operational analysis presents the gold standard for a market analysis for marketers. The author highlights that 20…… Continue reading What Happened to Strategic Segmentation?

Market Segmentation – A Response to Retail Innovation

Introduction and the Research Problem David L. Appel conducted a study titled “Market Segmentation- A Response to Retail Innovation” published in the Journal of Marketing by Sage Publication in 1970. The study investigates how innovative institutions disrupt the current patterns of market segmentation. In addition, the author highlights that very little is known about market…… Continue reading Market Segmentation – A Response to Retail Innovation