Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years…… Continue reading Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating
Category: Customer Prediction
Unlock the Power of BERT for Binary Text Classification
In the world of natural language processing, one of the most important tasks is binary text classification. Binary text classification is the process of classifying text into two distinct classes. For example, a binary classifier could classify an email into either spam or not. A new deep learning tool called BERT (Bidirectional Encoder Representations from…… Continue reading Unlock the Power of BERT for Binary Text Classification
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
Intelligent Value Based Customer Segmentation: A Case Study of Automobile Retailer — Summary
In this article, we will be reviewing a research paper titled: ‘Intelligent Value-Based Customer Segmentation Method for Campaign Management: A Case Study of Automobile Retailer’ (2008). The research proposes an approach that blends consumer targeting and customer segmentation for an effective marketing strategy that targets the right kind of consumers. The research paper is authored…… Continue reading Intelligent Value Based Customer Segmentation: A Case Study of Automobile Retailer — Summary
Customer Segmentation Issues And Strategies For An Automobile Dealership With Two Clustering Techniques – A Summary
In this post, we review the customer segmentation research article, “Customer segmentation issues and strategies for an automobile dealership with two clustering techniques” (2015) by authors from Taiwan: Chih-Fong Tsai from National Central University, Ya-Han Hu from National Chung Cheng University, and Yu-Hsin Lu from Feng Chia University. The Business Case for Segmentation The authors…… Continue reading Customer Segmentation Issues And Strategies For An Automobile Dealership With Two Clustering Techniques – A Summary
Performance analysis of keyword advertising campaign using gender-brand effect of search queries
In this research, we analyze the relationship among (1) the performance metrics of a sponsored search campaign, (2) the gender orientation of queries, and (3) the occurrence of branded terms in queries. The aim of this research is to investigate the effectiveness of increased personalization of search engine advertising in order to improve the consumer’s…… Continue reading Performance analysis of keyword advertising campaign using gender-brand effect of search queries
Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising
This research evaluates the effect of gender-targeted advertising on the performance of sponsored search advertising. We analyze nearly 7,000,000 records spanning 33 consecutive months of a keyword advertising campaign from a major US retailer. In order to determine the effect of demographic targeting, we classify the campaign’s key phrases by a probability of being targeted…… Continue reading Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising
Forecasting the Nearly Unforecastable: Why Aren’t Airline Bookings Adhering to the Prediction Algorithm?
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?