Using Machine Learning To Cocreate Value Through Dynamic Customer Engagement In A Brand Loyalty Program

2018 ◽  
Vol 43 (1) ◽  
pp. 78-100 ◽  
Author(s):  
Ajay Aluri ◽  
Bradley S. Price ◽  
Nancy H. McIntyre
Keyword(s):  
Machine Learning ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Time Data ◽  
Loyalty Program ◽  
Theoretical Understanding ◽  
Dynamic Decisions ◽  
Automated Processes ◽  
Automated Procedures

Hospitality venues traditionally use historical data from customers for their customer relationship management systems, but now they can also collect real-time data and automated procedures to make dynamic decisions and predictions about customer behavior. Machine learning is an example of automated processes that create insights into cocreation of value through dynamic customer engagement. To show the merits of automation, machine learning was implemented at a major hospitality venue and compared with traditional methods to identify what customers value in a loyalty program. The results show that machine learning processes are superior in identifying customers who find value in specific promotions. This research deepens practical and theoretical understanding of machine learning in the customer engagement-to-value loyalty chain and in the customer engagement construct that uses a dynamic customer engagement model.

scholarly journals TOWARDS EFFECTIVE CUSTOMER RELATIONSHIP MANAGEMENT IN OMAN: ROLE OF BIG DATA

2017 ◽  
Vol 5 (10) ◽  
pp. 92-100
Author(s):  
Tarek Khalil ◽  
Al-Refai Mohammad ◽  
Amer Nizar Fayez ◽  
SharafQudah Mohammed
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Qualitative Evaluation ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Time Data ◽  
Analytical Tools

We established a framework to explore the feasibility of enabling big data within the customer relationship management (CRM) strategies in Oman for creating sustainable business profit nationwide. A qualitative evaluation was made based on predictive analytics convergence and big data facilitated CRM. It was found that the big data analytics can meticulously alter the competitive industrial setting, and thereby proffered notable benefits to the business organization in terms of operation, strategies, and competitiveness. Results revealed that companies must introduce analytical tools, real-time data, and hire talented as well as skilled employees to improve the productivity in consistent with the new business model. Furthermore, depending on the customer engagement, an assemblage and analysis of enormous data volume together with analytical tools was discerned to assist companies towards efficient resource allocation and capital spending. The implications of using big data for CRM in Oman and way forward were emphasized.

Engagement, satisfaction and customer behavior-based CRM performance

2018 ◽  
Vol 28 (5) ◽  
pp. 682-707 ◽  
Author(s):  
Vittoria Marino ◽  
Letizia Lo Presti
Keyword(s):  
Customer Relationship Management ◽  
Instant Messaging ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Emotional Engagement ◽  
Content Type ◽  
Behavior Based ◽  
And Behavior ◽  
The Relationship

Purpose In recent years, marketers have adopted new technologies to engage customers and better meet customer needs throughout the customer journey. The purpose of this paper is to investigate the impact of consumer engagement on satisfaction and behavior-based CRM performance generated by mobile instant messaging (MIM) services. The objective is to verify which aspects of consumer engagement generate satisfaction and optimize customer relationship management. Design/methodology/approach Data were made available for analysis from an online survey on customers who had been contacted or had contacted an organization by means of MIM. Based on literature analysis, relations between customer engagement dimensions, satisfaction and behavior-based CRM performance were studied by using structural equation modeling. Findings The cognitive engagement dimension and the emotional engagement dimension affect the level of satisfaction, but only the emotional engagement dimension has an effect on the behavior-based CRM performance, while social engagement does not affect satisfaction and CRM performance. Moreover, this study confirms the relationship between customer satisfaction and customer behavior-based relationship performance. Practical implications MIM used as support to the relationship with customers contributes to generating customer satisfaction and increases the value of service performance revealing it an excellent marketing tool in support of the customer journey. Originality/value This research extends our understanding of customer engagement in the ambit of the instant messaging application used for business that so far has not been investigated. This work shows how instant messaging can be a valid instrument for customer relationship management in optimizing the benefits deriving from the adoption of disruptive innovations.

Shopping for loyalty: an executive's wish list

2009 ◽  
Vol 26 (1) ◽  
pp. 49-51 ◽  
Author(s):  
Bruce Kerr
Keyword(s):  
Customer Loyalty ◽  
Customer Relationship ◽  
Loyalty Programs ◽  
Customer Engagement ◽  
Strategy Development ◽  
Loyalty Program ◽  
Content Type ◽  
The Future ◽  
Loyalty Program Design ◽  
Past Experiences

PurposeThe purpose of this paper is to present a blueprint for loyalty strategy development, and to inform the reader of the basic and not so basic elements that should be considered by companies, academics and executives when determining the future effectiveness and success of a customer loyalty program.Design/methodology/approachThe paper draws on author expertise and know‐how, as well as past experiences and market trends, to present a compelling review of necessary elements for a customer relationship program.FindingsBefore embarking on a loyalty program design companies should be wise to set long‐ and short‐term goals to drive the initial strategy. One should avoid launching a “me too” program by differentiating from one's close competitors. Implement a strategy from top to bottom across the organization. Data and customer engagement are the most important elements to foster.Practical implicationsA well‐planned and executed loyalty strategy can boost growth in incremental sales, increase in‐store traffic, drive higher impact from promotions and significantly boost overall revenues. Customer relationship marketing can also lead to richer data insights about current customers and high‐value potential customers.Originality/valueAn executive with decades of experience building and maintaining customer loyalty programs cites trends for the future of the loyalty discipline.

“Custolytics”

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samir Yerpude ◽  
Tarun Kumar Singhal
Keyword(s):  
Real Time ◽  
New Product Development ◽  
Emerging Market ◽  
Customer Engagement ◽  
Time Data ◽  
Loyalty Program ◽  
Content Type ◽  
Real Time Data ◽  
Customer Analytics ◽  
The Impact

Purpose The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a classical retail business to customer domain. Design/methodology/approach The study presented is twofold. First, it empirically tests a theoretical model where the impact of different parameters influencing customer engagement are validated, and its influence on the resultant parameters, i.e. brand loyalty and brand ambassador, is analyzed. Second, it emphasizes on the use of real-time IoT origin data in customer analytics to determine a customer engagement strategy. Findings Results indicate that the four parameters, i.e., value propositions basis the buying patterns, loyalty programs, personalized communication and involving the customer in the new development process are influencing customer engagement positively, whereas the parameter loyalty program scores the maximum regression weight. IoT plays a crucial role in generating the real-time data used for generating customer analytics that proves to be vital for the longevity of the organization. Practical implications The organizations need judicious blend of four parameters such as value proposition based on buying patterns, participation in new product development, personalized communication and loyalty program while designing the customer engagement strategy. Results drawn from the focused group interview highlight the power of IoT origin real-time data in the customer analytics further strengthening the need of customer centricity in an organization. Originality/value Identified need of building a customer engagement strategy for an emerging market with the help of IoT data is addressed in this paper that is identified as an unexplored area and a research gap.

scholarly journals Managerial Responses and Customer Engagement in Crowdfunding

2020 ◽  
Vol 12 (8) ◽  
pp. 3389 ◽  
Author(s):  
Yan Song ◽  
Xin Tian
Keyword(s):  
Empirical Research ◽  
Customer Behavior ◽  
User Generated Content ◽  
Customer Engagement ◽  
Significant Positive Effect ◽  
Linguistic Features ◽  
Advertising Strategy ◽  
Effective Advertising ◽  
Positive Effect

Despite the widespread recognition of the importance of customer behavior in crowdfunding performance, empirical research concerning the importance of managerial responses in user-generated content is scarce. How do managerial responses affect backers’ comments? Does user-generated content affect following backers’ behavior? Using a dataset of backers’ comments and creators’ managerial responses from Kickstarter.com, we attempt to clarify the relationships among creator responses to comments, comment volume, linguistic features of comment text and crowdfunding performance. Our results show creator responses have a significant positive effect on customer engagement and crowdfunding performance. Moreover, creator response is an effective advertising strategy to improve crowdfunding performance.

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

Understanding the Relationship Between Customer Satisfaction, Customer Engagement and Repeat Purchase Behaviour

2021 ◽  
pp. 097226292199259
Author(s):  
Devika Rani Sharma ◽  
Balgopal Singh
Keyword(s):  
Customer Satisfaction ◽  
Relationship Building ◽  
Customer Relationship ◽  
Mediation Effect ◽  
Customer Engagement ◽  
Mediating Role ◽  
Repeat Purchase ◽  
Building Activities ◽  
The Relationship

Emergence of technology has not only boosted the growth of customer engagement but has also paved way for customers to become active co-creators with the firms. Customer engagement activities are taking over the customer relationship building activities in the present scenario. Customers’ experience with a particular brand has its impact on satisfaction levels and their repurchasing intention in future as well. According to Rosetta Consulting report an engaged customer is likely to buy 90% more frequently and may spend 300% more than other customers. Hence, the present has tried to understand the mediating role of satisfaction on customer engagement in retaining the customers or persuading the customers to repurchase. The results show that there exists a significant mediation effect of customer satisfaction in influencing their repeat purchase behaviour.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

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