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
Combining Discrete And Continuous Representations Of Preference Heterogeneity: A Latent Class Approach
In this article, we will be reviewing a research paper, titled: Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach. Possible methods to incorporate variability in leisure requirements are examined in this research paper. The focus of the paper is on finding alternative methods to incorporate heterogeneity in recreational demand. The researchers…… Continue reading Combining Discrete And Continuous Representations Of Preference Heterogeneity: A Latent Class Approach
Customer Segmentation Based on Buying and Returning Behaviour
In this article, we will be reviewing a research paper, titled: ‘Customer segmentation based on buying and returning behaviour’. The focus of this research paper is based on two aspects, which are: To determine if a general-purpose strategy conforms to the clothing businesses that involve e-commerce To properly assess for certain if customer rates of…… Continue reading Customer Segmentation Based on Buying and Returning Behaviour
SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns
Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that: (a) retrieves performance metrics for different customer segments across…… Continue reading SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns
Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors
We report results using n-grams to model user actions with only aggregated data and knowing little about the user. Employing a data set of 33,860 flight bookings from 4,221 passengers, we evaluate the n-gram model for the precision of predicting next likely actions. Results show that our approach can achieve a precision of 21% overall…… Continue reading Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors