Artifical Intelligence and CRM

Disruptive technologies such as IoT, big data analytics, blockchain, and AI have changed the ways businesses operate, with AI holding immense marketing transformation potential. AI is influencing marketing strategies, business models, sales processes, customer service options, and customer behaviors. AI-CRM's improving ability to predict customer lifetime value will generate an inevitable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. CSPs are working through the challenging process of digital transformation, driven by the need to compete with fast-moving OTT and consumer tech players. CSPs need to move quickly and can advance digital transformation with solutions that leverage AI which can drive value across the business from network optimization and data analytics through to customer care and marketing engagement. The chapter tries to identify how AI is impacting the CRM in the telecom industry and leveraging the benefits of this technology for better customer management and growth.

Artificial Intelligence in Retail

This chapter gives an overview of how artificial intelligence is used by the retail sector to enhance customer experience and to improve profitability. It provides information about the role of the pandemic in stimulating AI adoption by retailers. It deliberates on how AI tools help retailers to engage customers online and in stores. Firms gain better understanding of customers, design immersive experiences, and enhance customer lifetime value using cost-effective technology solutions. It discusses popular AI algorithms like recommendation algorithm, association algorithm, classification algorithm, and predictive algorithm. Popular applications in retail include chatbots, visual search, voice search engine optimisation, in-store assistance, and virtual fitting rooms.

Customer Lifetime Value of Supplementary Health Insurance: An Analytical Model

Background: With the number of insurance customers growing, insurance companies are trying new ways to retain customers and streamline communication channels to avoid loss of revenue. The present study set out to develop a model for a reliable analysis of customer lifetime value. Methods: The present study was exploratory mixed method in design. The study took place in Jundishapur University of Medical Sciences, located in Ahvaz, Iran. A total of 402 insurance experts and university staffers participated in the study. A cross-sectional data collection was done using semi-structured interviews (n = 22) and a questionnaire (n = 380). The latter was validated via a panel of content area experts, criterion-dependent validity (second-order confirmatory factor analysis), and divergent validity (cross-sectional load test and Fornell-Laker). Cronbach's alpha and combined reliability were - 0.8 and 0.8, respectively. A structural equation approach was employed to analyze data using Smart PLS software. Results: Customer loyalty with an impact factor of 0.60 and T-statistic of 5.79, profitability with an impact factor of 0.55 and T-statistic of 3.75, customer co-creation with an impact factor of 0.28, and T-statistic of 2.7 have been identified as dimensions of customer lifetime value. Conclusion: Measuring customer lifetime value to implement various strategies requires a deep understanding of such value dimensions as loyalty, profitability, and value creation.

Customer Digital Engagement and Lifetime Value: An Empirical Study of Telecom Services in India

Digitalization has posed severe challenges to traditional businesses. Traditional firms are still not sure of the benefits of digitally engaging their customers. In contrast, the new-age firms have successfully leveraged digital media. This article examines the relationship between digital adoption by customers and customer lifetime value (CLV). This study analysed the mobile recharges by 13 million rural and urban prepaid telecom customers over 60 million transactions from January 2019 to June 2019 in the Indian telecom industry. The computed predictive CLV has been computed and compared across various customer segments (digitally engaged, partially digitally engaged and digitally unengaged customers). The studied data were statistically validated using the Kruskal–Wallis test. The data proved to be non-normally distributed as per the Kolmogorov–Smirnov test. The results supported that digital adoption helps increase customer engagement, loyalty and CLV. The study presents several managerial implications, such as digitally engaged customers being a surrogate for high value and more profitable customers. Also, digitally engaged customers are relatively more loyal, contributing higher CLV than digitally unengaged customers.

MEASURING CUSTOMER LIFETIME VALUE: APPLICATION OF ANALYTIC HIERARCHY PROCESS IN DETERMINING RELATIVE WEIGHTS OF ‘LRFM’

ABSTRACT The limited availability of resources drives retailers to tailor their resources to identified profitable customers. In the present scenario, when the ROI of marketing is being questioned, the satisfaction of the profitable customers is of utmost importance as it drives their loyalty towards the retailer and the retailer’s brand. This research has considered Length of association with customers (L), apart from variables like Recency (R), Frequency (F) and Monetary-value of the purchase (M) in measuring customers’ relative-worth based on the calculation of Customer Lifetime-value (CLV). The contribution of this article lies in calculating weights of these variables – L, R, F, and M and demonstrating the calculation of CLV using weighted LRFM based on data collected from a leading apparel retailer in India. The obtained results for the customer base using the proposed approach is more reliable when compared with traditional non-weighted approaches of RFM based CLV. This methodology will provide a new and better option to retailers for measuring CLV of their customers, thus aiding their decision making about customer-friendly profitable marketing strategies and attaining optimum returns on their investments.

An investigation into customer accounting in customer-focused organisations

<p>Management accounting information should aid management in the design and implementation of strategy. Firms adopting a customer-focused strategy need customer accounting (CA) metrics. Yet accounting literature provides limited insights into what CA metrics are used, how they are used, or what factors influence CA measure choice or hinder more widespread adoption of CA practices. This thesis enhances knowledge of actual CA practices as they operate in firms with a customer-focused strategy and uses contingency theory to explain the choice of CA practices and their use in three exploratory case studies consisting of two national banks and a global courier company. The two strategic business units in Alphabank employ locally-developed, activity-based costing systems to produce CA information. Personal Banking incorporates a ‘customer needs met’ variable into a customer lifetime value measure used to segment customers based on potential profitability. Business Banking is smaller and currently uses historical customer profitability analysis at the individual customer level. Despite Alphabank’s overall customer-focused strategy, only product profitability is reported at executive level, and tensions between finance and operations potentially hinder more widespread CA usage. Betabank offers excellent customer service, but despite being very customer-focused they do not measure customer profitability. Executives use predominantly aggregate financial figures with a focus on net interest margin. Service excellence is paramount and Betabank do not consider financial CA useful as they do not segment customers. However, they extensively use non-financial customer related measures to monitor excellent customer service provision in order to enhance future profitability. The courier company uses activity-based costing to produce historical customer profitability analysis which reports direct margin, gross margin and earnings before interest and tax. The analysis discloses significant profitability differences between customer segments, and even between individual customers within segments where customer relationship management is employed. They do not measure full customer lifetime value but the next year’s customer profitability can be modelled using historical cost drivers. Financial CA measures drive initiatives to enhance customer profitability and/or trigger price negotiations. Non-financial CA measures are used to drive the customer-focused strategy and enhance profitability. The three cases demonstrate a considerable diversity in their usage of financial CA practices, with Betabank choosing to use no financial CA at all. Competitive intensity and the use of customer relationship management are found to be key drivers of CA usage at the individual customer level. Segmental customer profitability analysis is used when a large number of customers receive standard services at standard prices. No individual customer profitability analysis is needed for such homogenous customers as they can be efficiently managed using revenue. Non-financial CA measures were found to be widely used and hence a key contribution of this study is that in practice customer-related, non-financial performance measures are a key component of CA practices and may be extensively used to drive a customer-focused strategy. From case analysis a contingency-based framework has been develop which identifies combinations of factors with strong interrelationships and common influences on the choice and usage of CA measures. This framework provides three main groupings of contingent factors (type of competitive advantage, level of customer heterogeneity, and stage of organisational development) which together potentially have strong predictive power in relation to the nature of CA measures which benefit firms with a customer-focused strategy.</p>