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?
Too few, too many, just right: Creating the necessary number of segments for large online customer populations
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
Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization
Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences. Applying data-driven persona generation on online customer statistics from a real organizational social media channel, we demonstrate how personas can be deployed to understand online customer patterns over time. We conduct 32 monthly rounds of…… Continue reading Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization