JournalTOCs

Journal of Database Marketing & Customer Strategy Management
Number of Followers: 5

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1741-2439 - ISSN (Online) 1741-2447
Published by Springer-Verlag

[2469 journals]

Why to use Poisson regression for count data analysis in consumer behavior
research
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  Abstract: Abstract Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers.
  PubDate: 2022-05-06
Digitalization and its impact on contemporary marketing strategies and
practices
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  PubDate: 2022-04-20
Retail distribution evaluation in brand-level sales response models
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  Abstract: Abstract The effectiveness of a product’s distribution network in retail stores is an important consideration for marketing managers. An effective distribution network typically covers a large number of stores in the geographic area of a market and establishes a continuous presence in the top-selling outlets of a product category at the same time. This study proposes a semiparametric, brand-level version of the SCAN*PRO sales model, to evaluate the impact of retail distribution changes on sales. The model is estimated using the iteratively reweighted least squares method and provides the following outputs: (i) least squares coefficient estimates for the price and promotional drivers in the model specification and (ii) two-dimensional plots of the nonmonotonic relationship between the weighted distribution and sales. The proposed model can be estimated with commonly available retail scanning data and is demonstrated using three laundry detergent brands from The Netherlands.
  PubDate: 2022-03-25
The effects of internet search intensity for products on companies’
stock returns: a competitive intelligence perspective
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  Abstract: Abstract Does internet search intensity (ISI) for a company’s product affect the company’s stock returns' How about the ISI for its rival product' How does ISI for the company’s product affect the ISI for the rival and vice versa' How is the evolution and persistence of these effects over time' To answer these questions, this study examines three pairs of rival products: Apple’s iPhone versus Samsung Galaxy, Intel versus AMD processors, and Netflix versus Hulu. Guided by psychological and marketing theories, Vector Autoregressive models were constructed to estimate the effects of ISI for the rival products (1) on the stock returns, and (2) on each other. Results showed that (1) ISI for the products significantly impacts the stock returns, and (2) the effects of ISI for one product on the other are not only significant but also asymmetrical. This multidisciplinary study integrates marketing analytics and financial phenomena and thus contributes to multiple research streams. It also finds that companies’ stock returns can be affected by the consumer online information search, which is responsive to marketing activities. Thus, marketers stand to benefit from leveraging this study’s findings to elevate their role in enhancing one of their companies’ most critical performance metrics—stock returns.
  PubDate: 2022-03-14
Does demand forecasting matter to retailing'
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  Abstract: Abstract Time series forecasting in retail plays an important role for retailers due to its capacity to aid managers in strategic planning to meet demand. In this article, a comparative experiment between two demand forecasting models is conducted to forecast an aggregate time series in Brazilian food retail on a twelve-year horizon. Performance was analyzed by comparing the accuracy of a linear model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and a non-linear model, and Wavelets Neural Network (WNN). Performance was validated by the Mean Absolute Percentage Error (MAPE) and U-Theil, with a view to achieving the most accurate measurement of demand responsiveness, using the Fill Rate (FR). WNN had the best performance, with a FR of 98.43%, whereas SARIMA achieved a FR of 102.23%. Demand forecasting using WNN can be considered a resource for differentiation between retail organizations when it is incorporated into their operations. This article makes three contributions to the field with an application to retail and strengthening the argument for choosing a promising technique that can be used by managers to aid their decision making.
  PubDate: 2022-03-11
Do E-wallets trigger impulse purchases' An analysis of Malaysian Gen-Y
and Gen-Z consumers
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  Abstract: Abstract This study investigates the factors influencing Generation Y and Z’s satisfaction and perceived enjoyment of using E-wallet. This paper further assesses whether consumers perceived enjoyment and satisfaction with using E-wallet would significantly affect their impulsive buying behavior. PLS-SEM was conducted based on 201 valid responses from active E-wallet users collected through an online survey. The results revealed that perceived interactivity and subjective norm positively influenced perceived enjoyment and satisfaction with using E-wallet, respectively. Perceived risk had no significant impact on perceived enjoyment and satisfaction with E-wallet, whereas visual appeal positively influenced perceived enjoyment but not satisfaction. Moreover, this study found that perceived enjoyment of using an E-wallet positively affected impulse buying while satisfaction with E-wallet had no significant relationship with impulse buying. Implications and recommendations for future research are discussed in this paper.
  PubDate: 2022-03-10
Profiling diverse reviewer segments using online reviews of service
industries
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  Abstract: Abstract Consumers often rely on online review sites (e.g., Yelp, TripAdvisor) to read reviews on service products they may consider choosing. Despite the usefulness of such reviews, consumers may have difficulty finding reviews suitable for their own preferences because those reviews possess significant preference heterogeneity among reviewers. Differences in the four dimensions of individual reviewers (Experience, Productivity, Details, and Criticalness) lead them to have differential impacts on various consumers. Thus, this research is aimed at understanding the profiles of multiple influential reviewer segments in the service industries under two fundamental evaluation principles (Quality and Likeness). Based on the results, we theorize that reviewer influence arises from writing either reviews of high quality (Quality) or reviews reflecting a significant segment of consumers’ common preferences (Likeness). Using two service product categories (restaurants and hotels), we empirically identify and profile four specific influential review segments: (1) top-tier quality reviewers, (2) second-tier quality reviewers, (3) likeness (common) reviewers, and (4) less critical likeness reviewers. By using our approach, product review website managers can present product reviews in such a way that users can easily choose reviews and reviewers that match their preferences out of a vast collection of reviews containing all kinds of preferences.
  PubDate: 2022-03-09
Integrated customer lifetime value (CLV) and customer migration model to
improve customer segmentation
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  Abstract: Abstract Cluster analysis and RFM model are widely used to gain a deeper understanding of customers’ characteristics and needs due to its simplicity and applicability in analyzing customer purchasing behavior. However, the lack of considering the future value of customers or whether current customers exhibit a pattern of likely attrition or switching to a competitor into the original segmentation models is a big concern in segmenting customers for further strategic and personalized campaigns. How can organizations integrate their customers’ lifetime value and customer migration, which refers to the probability that their customers will likely return in the future, as parts of the RFM and cluster analysis to improve marketing decisions' Our modified segmentation models are then validated in the context of complementary and alternative medicine in the healthcare industry to demonstrate the practical validity of our proposed methods.
  PubDate: 2022-03-07
Correction to: Visualization of statistically significant correlation
coefficients from a correlation matrix: a call for a change in practice
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  PubDate: 2022-03-01
Multi-criteria decision-making leveraged by text analytics and interviews
with strategists
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  Abstract: Abstract Strategic decision-making in organisations is a complex process affected by preferences, experiences, perspectives, and knowledge, which, in most cases, are ambiguous, contradictory, and represented in unstructured data. This paper develops a methodological framework to address strategic decision-making processes from a multi-criteria perspective, assisted by text analytics and interviews. The framework comprises five stages and 12 steps, and is empirically tested in a decision scenario involving a strategic focus for future analytics initiatives in order to stimulate value generation from analytics. The proposed framework enables the discovery, validation, and prioritisation of strategic patterns from relevant interview data. Among six decision alternatives discovered in the validation scenario, customer analytics was the strategic focus most relevant to future analytics initiatives. This article contributes to understanding and addressing complex decision-making processes and mixed research in organisations, through a multi-criteria perspective leveraged by a text-driven computational approach.
  PubDate: 2022-03-01
A user-friendly method to merge Scopus and Web of Science data during
bibliometric analysis
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  Abstract: Abstract Bibliometric studies in management and related fields are growing exponentially due to the need to systematize and summarize the growing body of publications. To do so, scholars mostly retrieve publications and metadata from either Scopus or Web of Science. Only a few bibliometric studies merge the two databases to conduct a single integrated analysis. Recent studies demonstrated the benefits of merging data from Scopus and Web of Science and presented methods for the merging. In this paper we build upon a recent method to simplify some of the key steps of merging datasets when using the R package Bibliometrix to perform bibliometric analyses. The result is a user friendly, accessible, three-step method that allows researchers to save time without compromising the integrity of the data, and the analysis. Our method is particularly beneficial for a wider application as it does not require coding skills, and neither proprietary nor shareware software.
  PubDate: 2022-03-01
The effect of sales promotions intensity on volume and variability in
category sales of large retailers
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  Abstract: Abstract Marketing is a system and operational process which includes various interconnected subsystems, harmonising with contributor elements to reach maximum efficiency. In this quest to sustain the most efficient sales trend, “sales promotions” have an essential role in stimuli of sales trends. Although earlier studies treat promotions as an interim activity to dissolve excess stock, starting from the 1980s, promotions had been seen as a critical element in creating a sustainable sales trend for the products. This paper focuses on the significance of the effect of promotional decisions on mean level and volatility of category sales trend. We employed time series methods, including exogenous variables. In our investigative methodology, sales trend considered in two different contexts, first the current level of sales, which corresponds to an increase in average sales trend and later variability of sales, leading to change in the level of uncertainty in category sales. To illustrate this research question, we use data belonging to one of the largest supermarket chains in Turkey. Category sales data span for five years between 2014 and 2018 in daily frequency queried from the company's database. We select the “laundry powder detergents” category sales quantity as a pilot category. The study exhibits that promotion policy variables have a significant effect on the average level of category sales. However, the uncertainty caused through promotional variables has a weakly significant effect compared to their impacts on the mean level of sales. Our study verifies previous studies claiming promotional variables significantly positively affect the mean level of sales trend. Besides, we found concrete evidence that the volatility of sales trend has a diminishing effect on the trend, while most of this adverse effect stemming from sales series own endogenous volatility process, not promotional variables.
  PubDate: 2022-03-01
Learning about the customer for improving customer retention proposal of
an analytical framework
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  Abstract: Abstract The market faces new challenges in retaining customers, since they have very high expectations, which translate into the demand for a swift response and intransigence to empty promises on the part of brands. These requirements result from the ability to disseminate and infuse information, which in turn makes customers more informed, more participative, and more uncompromising. This change in behavior implies redesigning the strategic management of the brands, in terms of the relationship with the customer. In view of this challenge, the relevance of developing an adequate differentiation model for customer retention prevails. Based on this premise, this paper presents a proposal based on RFM and ABC analytical methods applied to customer relationship management and contextualized in a particular case of the printing industry. The proposed model defines a set of metrics aimed at customer segmentation, which improves the customers knowledge. The outcomes will allow to define more assertive marketing strategies for customer loyalty and to increase the volume of a brand's revenue.
  PubDate: 2022-03-01
Marketing to the next generation of casino patrons
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  Abstract: Abstract The expansion of gambling across the United States has made the gaming industry more competitive while the demographics of the gaming industry have evolved. Younger patrons are visiting casinos less frequently than their older counterparts, thus increasing the pressure to market to guests effectively. In this study, we investigate the differences in marketing, gaming, and entertainment preferences under the lens of Generational Cohort Theory (GCT). A survey with a discrete choice design was used to measure each generation’s gaming behaviors and marketing preferences, while a hierarchical Bayes model was used to examine intergenerational differences. The results show that younger cohorts have an increased preference for social-based gambling activities and greater preference for non-gaming offers. Considerations are noted with recommendations for the industry to help cultivate customer loyalty amongst the younger generations, which may ensure firm viability for years to come.
  PubDate: 2022-03-01
Developing a conversion rate optimization framework for digital
retailers—case study
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  Abstract: Abstract To stay competitive against e-commerce, many retailers started to adopt a digital retail strategy, leveraging a myriad of online and offline touchpoints to increase their customer experience and, as a result, their sales. However, currently, no guidelines exist on how digital retailers can identify, evaluate, and influence sales impacting touchpoints along the customer journey. Hence, this study derives key elements of a conversion rate optimization framework, which can be used to increase sales of a digital retailer. Additionally, the derived framework is tested with the Austrian subsidiary of an international sports appeal and equipment retailer giving insights into its practical applicability. Results indicate that the developed framework can indeed be used to identify sales influencing touchpoints, which can be altered by specific marketing actions to increase sales of a digital retailer.
  PubDate: 2022-02-23
  DOI: 10.1057/s41270-022-00161-y
Going (in)conspicuous: antecedents and moderators of luxury consumption
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  Abstract: Abstract As a result of the increasing worldwide luxury expenditure, and consequently, its democratization, consumers in mature markets feel that luxury has lost its uniqueness and exclusivity. Despite academia having deemed conspicuousness and luxury connected, this study intends to determine the main antecedents of (in)conspicuous luxury consumption, and cross-culturally analyze both conspicuous and inconspicuous luxury consumption. A partial least square structure equational model was conducted. The data collection framework followed a traditional SEM data collection and consequent analysis. The results revealed that consumer need for uniqueness is an antecedent of inconspicuousness luxury consumption, while cultural capital has a negative effect on conspicuous luxury consumers, mediated by susceptibility to normative influence. Additionally, between Southern European and Central/Eastern European consumers, cultural capital impacts differently on luxury consumption. By adopting a critical view of the nature of luxury, analyzing its conceptual evolution through the ages, and matching it to its current landscape via identifying antecedents of inconspicuous luxury consumption, this study is a steppingstone for luxury consumption research, as it postulates a new understanding of what constitutes luxury consumption, thus opening up new research avenues for academia.
  PubDate: 2022-02-16
  DOI: 10.1057/s41270-022-00157-8
Customer feature selection from high-dimensional bank direct marketing
data for uplift modeling
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  Abstract: Abstract Uplift modeling estimates the incremental impact (i.e., uplift) of a marketing campaign on customer outcomes. These models are essential to banks’ direct marketing efforts. However, bank data are often high-dimensional, with hundreds to thousands of customer features; and keeping irrelevant and redundant features in an uplift model can be computationally inefficient and adversely affect model performance. Therefore, banks must narrow their feature selection for uplift modeling. Yet, literature on feature selection has rarely focused on uplift modeling. This paper proposes several two-step feature selection approaches to uplift models, structured to cluster highly relevant, low-redundant feature subsets from high-dimensional banking data. Empirical experiments show that fewer features in a selected set (20 out of 180 features) lead to 68.6% of these uplift models performing as well or better than complete feature set models.
  PubDate: 2022-02-11
  DOI: 10.1057/s41270-022-00160-z
Co-creating transformative value in marketing analytics
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  PubDate: 2022-02-07
  DOI: 10.1057/s41270-022-00159-6
The impact of tangible and intangible rewards on online loyalty program,
brand engagement, and attitudinal loyalty
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  Abstract: Abstract This paper investigates customers’ insights about a loyalty program in the online context to examine the impact of tangible and intangible rewards on attitudinal loyalty and brand engagement. The study employed an online survey to collect data from e-commerce customers of a major cosmetics brand. The analysis methods include thematic analysis and partial least squares structural equation modeling. Findings indicated that, while both tangible and intangible rewards have an impact on loyalty and assessment of the overall loyalty program, tangible rewards are much more impactful in generating attitudinal loyalty. Furthermore, both types of rewards had a larger impact on the online loyalty program evaluation than on the attitudinal loyalty construct. The current study provides a systematic look at the relationships between online loyalty program rewards and their impact in bringing about desirable outcomes for online loyalty program managers, utilizing a structural equation model to analyze the relevant relationships. Furthermore, the research focuses upon the cosmetics industry, in which there appears to be a paucity of research in the assessment of both online and offline loyalty program effectiveness.
  PubDate: 2022-01-27
  DOI: 10.1057/s41270-021-00150-7
What’s important for relationship management' The mediating roles of
relational trust and satisfaction for loyalty of cooperative banks’
customers
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  Abstract: Abstract Building on the corporate reputation model, this study investigates the drivers of customer-based corporate reputation. We consider two corporate reputation dimensions (i.e., the cognitive dimension competence and the affective dimension likeability, and their effects on customer satisfaction and loyalty). Adapting the model to the banking sector, we theoretically extend this model by reasoning that customer satisfaction and relational trust are mediators of the relationship between the two corporate reputation dimensions and loyalty. Studying a sample of 675 customers and members of cooperative banks in Germany, we find perceived attractiveness to be the most important driver of corporate reputation. Furthermore, we confirm a positive relationship between corporate reputation and loyalty, and a mediating effect of both satisfaction and relational trust. With our study, we give support for the proposition of customer satisfaction's as well as relational trust’s role as mediators of the relationship between corporate reputation and loyalty. With this research, we expand our knowledge on the well-known corporate reputation model, which has high relevance and important implications for marketing research and relationship management practice.
  PubDate: 2021-12-23
  DOI: 10.1057/s41270-021-00147-2