Ron Kahan

Bio: Ron Kahan is an academic researcher. The author has contributed to research in topics: Marketing effectiveness & Marketing science. The author has an hindex of 1, co-authored 1 publications receiving 94 citations.

Using database marketing techniques to enhance your one‐to‐one marketing initiatives

Abstract: Asserts that there are two approaches to successful database marketing: cognitive and behavioral analysis. In this way, marketers can garner a clear understanding of what customers and prospects “look like”. Reviews the processes involved in database marketing. Suggests to marketers the best processes to adopt. Describes the advantages of the mathematical computation RFM (recency, frequency, and monetary value) in consumer behavioral analysis. Provides a description of how the system can be implemented by practitioners.

Knowledge discovery on RFM model using Bernoulli sequence

Abstract: The objective of this paper is to introduce a comprehensive methodology to discover the knowledge for selecting targets for direct marketing from a database. This study expanded RFM model by including two parameters, time since first purchase and churn probability. Using Bernoulli sequence in probability theory, we derive out the formula that can estimate the probability that one customer will buy at the next time, and the expected value of the total number of times that the customer will buy in the future. This study also proposed the methodology to estimate the unknown parameters in the formula. This methodology leads to more efficient and accurate selection procedures than the existing ones. In the empirical part we examine a case study, blood transfusion service, to show that our methodology has greater predictive accuracy than traditional RFM approaches.

The influence of reviewer engagement characteristics on online review helpfulness: A text regression model

Abstract: The era of Web 2.0 is witnessing the proliferation of online social media platforms, which develop new business models by leveraging user-generated content. One rapidly growing source of user-generated data is online reviews, which play a very important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. In this paper, we develop and compare several text regression models for predicting the helpfulness of online reviews. In addition to using review words as predictors, we examine the influence of reviewer engagement characteristics such as reputation, commitment, and current activity. We employ a reviewer's RFM (Recency, Frequency, Monetary Value) dimensions to characterize his/her overall engagement and investigate if the inclusion of those dimensions helps improve the prediction of online review helpfulness. Empirical findings from text mining experiments conducted using reviews from Yelp and Amazon offer strong support to our thesis. We find that both review text and reviewer engagement characteristics help predict review helpfulness. The hybrid approach of combining the textual features of bag-of-words model and RFM dimensions produces the best prediction results. Furthermore, our approach facilitates the estimation of the helpfulness of new reviews instantly, making it possible for social media platforms to dynamically adjust the presentation of those reviews on their websites.

Adaptive marketing using insight driven customer interaction

Abstract: A system and method for adaptive marketing using insight driven customer interaction. The invention uses a closed-loop process for developing insight that may be used to refine further customer interactions. Results of a first customer interaction such as a marketing campaign are stored in a database. The results may be used to retrain predictive models and gain new insights regarding how customers are responding to marketing campaigns. The insights may be used to refine the offers delivered to customers or to extend additional offers in an effort to increase the likelihood that customers will redeem the offers. After each marketing campaign, the results are stored in the database. New and/or modified offers are created based on insights provided by the results of past campaigns. This process may be repeated such that subsequent campaigns are based on insights generated by the predictive models. The insight enables businesses to better target customers with better offers. These offers can be delivered through ensuing marketing campaigns or, through any form of interaction that the business has with the targeted customers.