Customer segmenting is the process of dividing a group of people into homogeneous subgroups that differ from other subgroups, typically based on behaviors and demographics, grounded on some product, brand, advertisement, or content, with many factors affecting product engagement by customer. The identification of customer segments has been important in marketing and advertising for some…… Continue reading Why is segmentation analytics important?
We compare a data-driven persona system and an analytics system for efficiency and effectiveness for a user identification task. Findings from the 34-participant experiment show that the data-driven persona system affords faster task completion, is easier for users to engage with, and provides better user identification accuracy. Eye-tracking data indicates that the participants focus most…… Continue reading Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud
Using an interactive persona system, user behavior and interaction with personas can be tracked with high precision, addressing the scarcity of behavioral persona user studies. In this research, lead by Soon-gyo Jung and Joni Salminen, we introduce and evaluate an implementation of persona analytics based on mouse tracking, which offers researchers new possibilities for…… Continue reading Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System
In this research, my co-researchers and I propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to ﬁrst identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two…… Continue reading Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data
Online companies face large user populations, making segmentation a daunting exercise. In this research, we demonstrate an approach that facilitates user segmentation. The approach leverages product dissemination and product impact metrics with normalized Shannon entropy. Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the…… Continue reading Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact
Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it…… Continue reading Forecasting the Nearly Unforecastable: Why Aren’t Airline Bookings Adhering to the Prediction Algorithm?
We develop a framework to reduce the number of customer segments to the smallest quantity without losing essential information of the underlying population in the electronic marketplace. We evaluate our approach in a case study using more than 21 million online flight bookings of a major airline company resulting in a 57.5% decrease from 1194…… Continue reading Too few, too many, just right: Creating the necessary number of segments for large online customer populations