Generation Y vs. Baby Boomers: shopping behavior, buyer involvement and implications for retailing

https://isiarticles.com/bundles/Article/pre/pdf/22096.pdf

Segmentation approaches in data mining: a comparison of rfm, chaid, and logistic regression

During the past decade, there have been a variety of significant developments in data mining techniques. Some of these developments are implemented in customized service to develop customer relationship. Customized service is actually crucial in retail markets. Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in customer behavior. In the dynamic retail market, understanding changes in customer behavior can help managers to establish effective promotion campaigns. This study integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at different time periods. The proposed approach for mining changes in customer behavior can assist managers in developing better marketing strategies.

http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/miningcustomerbehavior.pdf

Mining changes in customer behavior in retail marketing

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.

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

It has been reported that family businesses perceive excellent customer service as critical to the future of their businesses. However, little research into the customer relationship management (CRM) practices of family businesses has been performed. In this study, we examine CRM implementation among 82 family and 370 nonfamily firms. Family and nonfamily businesses report similar attitudes toward the importance of CRM, their knowledge of CRM, and their success when they do implement it. However, using a logit regression model, we find that the actual implementation strategies of family businesses are significantly different from those of nonfamily businesses. These results remain constant when controlling for size and industry sector.

Customer Relationship Management: A Comparative Analysis of Family and Nonfamily Business Practices*

This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well‐suited for small retail and service businesses. The discussion offers insights into the reasons for the development of this practical approach, a concrete methodology for its implementation, and strategic and tactical applications of the concept. The material is supported with strong evidence from “real‐world” examples featuring a variety of small retail and service businesses. The paper concludes with a discussion of the managerial implications for companies that manage chains of small retail or service businesses as to how they can take advantage of local relationship marketing.

A practical yet meaningful approach to customer segmentation

Systems and methods for the optimized allocation of content within a universe of inventory are described. For example, a method may include receiving content comprising at least one characteristic and at least one goal. A universe of inventory may be accessed that comprises a plurality of targets which may be assigned into a plurality of segments based on at least one demographic vector. An allocation optimization model may be generated based on the at least one content characteristic, the at least one goal and the plurality of segments. The content may be presented to the targets based on the allocation optimization model. Viewing data may be received that comprises data associated with target consumption of the content and the allocation optimization module may be re-optimized based on the viewing data. Additional factors, such as resource constraints and/or filtering rules, may be used when re-optimizing the allocation optimization module.

Dynamic content allocation and optimization

Systems and methods for the optimized allocation of content on a content distribution system according to a content distribution plan are described. In some embodiments, a management system may be configured to generate an optimized schedule for content distribution, such as an advertising campaign. For a television distribution schedule, an impressions forecast may be generated based on the available inventory and historical audience measurement information, such as television ratings for a television advertising campaign. An optimized schedule may be generated based on the impressions forecast and content may be distributed according to the optimized schedule. Measurement information relating to the distribution according to the optimized schedule may be analyzed and used to generate a re-optimized schedule, for example, that may be used to schedule the advertising campaign on a subsequent day.

Methods, systems, and computer-readable media for dynamic content allocation

Systems and methods for frequency capping of content are described herein. For example, frequency cap information may be configured to indicate a content exposure limit for a content presentation. The number of exposures to the content presentation at a particular household may be monitored. If the number of exposures is equal to or greater than the content exposure limit, the content may be prevented from being presented to the household. The frequency cap information may be used alone or in combination with other information, such as household demographic, timeframe, and day-part information.

Frequency capping of addressable content

Systems, methods and computer-readable storage media for determining an outcome of a content promotional message are described. For example, a media network may be configured to transmit content to a plurality of content playback devices, such as a television or a computing device. The content may include a content promotion message configured to promote content, such as a television program or a website, available through the media network. The content promotion message and the content may be associated with network identifiers generated by the media network for tracked content. When the content is played by the content playback device, a device identifier may be generated for the content. The device identifiers may be compared with the network identifiers to determine which, if any, content tracked by the media network has been played by the plurality of content playback devices.

Claudio Marcus

Papers

A practical yet meaningful approach to customer segmentation

Dynamic content allocation and optimization

Methods, systems, and computer-readable media for dynamic content allocation

Frequency capping of addressable content

Systems, methods and computer-readable media for determining outcomes for program promotions