When evaluating the performance of social media content, user engagement is a key performance indicator (KPI). Specifically, the interaction by the user with the content, such as viewing, liking, commenting, and sharing. From the mindset of a social media content producer, users with more public expressiveness of their interest have higher impactful engagement with the…… Continue reading VLCP: A four-level framework for social media user engagement metrics
Category: Customer segmentation
The Illusion of Data Validity: Why Numbers About People Are Likely Wrong
This reflection article addresses a difficulty faced by scholars and practitioners working with numbers about people, which is that those who study people want numerical data about these people. Unfortunately, time and time again, this numerical data about people is wrong. Addressing the potential causes of this wrongness, we present examples of analyzing people numbers,…… Continue reading The Illusion of Data Validity: Why Numbers About People Are Likely Wrong
When mere correlations are not enough: The Granger Causality test
In most data science-related problems, datasets consist of multiple variables, in which independent variables might depend on other independent variables. When the variables in datasets represent observations at different times, we call this dataset a time series set. The time interval in these data sets may be hourly, daily, weekly, monthly, quarterly, annually, etc. One…… Continue reading When mere correlations are not enough: The Granger Causality test
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
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