Zero Customer Defection

AI-oriented CRM has a bright future in business transformation. We're living in the age of the customer. Due to the proliferation of data, customers are more informed than ever. Armed with empowerment, customers are demanding that customer experience be put on a pedestal. According to research by Walker, customer experience is slated to overtake price and product as the key brand differentiator by the end of 2020. Quality is a buzz word. In this sharp, opportunistic, and calculating world, one can survive only if it is having not only good quality but a unique quality. As it is very well explained by Darwin that the mantra for success is “survival of fittest.” Every organization has its own procedure of achieving its best quality and to sustain in this tough world. This chapter will not only discuss about the zero customer defection but also emphasize on the issues, problems, and trends of artificial intelligence in CRM and in turn zero customer defection.

Customer Segmentation Using Machine Learning

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.

Customer’s repurchase intensions following service recovery: A conceptual model

An effective strategy of service recovery that prevents customer defection after service failure is a powerful managerial instrument. The values that a customer associates and considers with repurchasing after service recovery has changed due to globalization. Although service recovery has been identified as a key driver to retain the customers, yet the effectiveness of service recovery is unclear. The literature did not offer a comprehensive service recovery strategy that can be implemented universally. Owing to varying comments on the effectiveness of service recovery, its generalizability is questionable. Grudge produced in the response of service failure that cannot be compensated with financial transactions. This study will check whether service recovery has any impact on customer’s repurchase intentions when customer has grudges and available substitutes. The findings of this research will help the managers and practitioners to allocate their resources in a proper way. Furthermore, this study will help to understand money or extrinsic compensation is not everything. Therefore, there will be some consideration beyond the materialist compensation. It will open up new avenues for researchers to view service recovery strategies with a different angle.

Satisfying Service Customer through a Strong Service Strategy

Services require a lot of customer interaction and involvement. Research in the field of service marketing suggests that this particular sector mainly earns profit from customer retention. Therefore the need of the hour is to design a strong service strategy. It is easy to get a customer through strong advertising or publicity efforts but retaining customers and keeping them delighted is almost impossible for any firm. Therefore they have to continuously keep on innovating in order to understand and surpass the rising customer expectations. But still at times despite of the best efforts of company or service staff some customers due to some or the other reason become dissatisfied with the service firm and as a result stop patronizing and change their loyalty and move to other competitive firms. It is very essential to recover such defected customers. There are certain strategies which, if religiously adopted by the service firms can not only help them bring back the defected customers but also equip them in preventing customer defection at first place itself. In this article an attempt has been made to highlight the essential elements and steps involved in designing and delivering effective and efficient customer service strateg

Star power: the effect of star rating on service recovery in the hotel industry

Purpose This study investigated distributive justice (DJ), procedural justice (PJ) and interactional justice (IJ) in the recovery of service in the hotel industry as well as their effects on post-recovery satisfaction and the subsequent behavior of customers. This study also sought to elucidate the moderating effects of star ratings on these influences. Design/methodology/approach A field experiment design with 424 consumers using structural equation modeling was adopted to test the study hypotheses. Findings DJ, PJ and IJ were all shown to have a significantly positive effect on post-recovery satisfaction, which in turn has a significantly positive effect on customer loyalty and significantly negative effect on customer defection. Analysis on the moderating effects of star ratings also revealed that the relationship between DJ and post-recovery satisfaction during recovery efforts is stronger for hotels with higher star ratings than for hotels with lower star ratings. During recovery, post-recovery satisfaction was shown to be more effective in reducing customer defection from hotels with lower star ratings than from hotels with higher star ratings. Practical implications This study provides a reference by which hotels can match their service recovery mechanism to their star rating to retain customers and increase customer loyalty in the event of service failure. Originality/value This study confirms the importance of IJ in the service recovery processes in the hotel industry and provides verification that the star rating of a hotel plays a crucial role as a moderator between recovery effort and recovery effectiveness.

A Study on a Predictive Model of Customer Defection in a Hotel Reservation Website

This paper examines a hotel reservation website’s customer defection. Applying statistic and data mining technology including logistic regression and random forests, we examine customer database to identify the attributes that affect customer attrition and develop a model of customer defection in the hotel reservation website. The empirical evaluation results showed the model has 78.9% accuracy, which suggest that the proposed churn prediction technique exhibits satisfactory predictive effectiveness.