Cognitive analytics management of the customer lifetime value: an artificial neural network approach

Marco, Marco De; Fantozzi, Paolo; Fornaro, C.; Laura, Luigi; Miloso, Antonio

doi:10.1108/jeim-01-2020-0029

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…As we have already mentioned in the Introduction, the business value of cognitive analytics is an under-researched topic in academia. Recent years have seen a sharp rise in interest around how cognitive analytics can add value to businesses in cases such as emergency management (Tarhini et al , 2021), customer lifetime value (De Marco et al , 2021), procurement (Handfield et al , 2019), contract renewals (Simsek et al , 2020), healthcare (Behera et al , 2019), e-government services (Osman et al , 2019), smart service systems (Hirt et al , 2018) and others.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Business value of cognitive analytics technology: a dynamic capabilities perspective

Majhi

Mukherjee

Anand

2021

VJIKMS

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Purpose Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization. Design/methodology/approach This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization. Findings Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring). Research limitations/implications This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring. Originality/value To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Business value of cognitive analytics technology: a dynamic capabilities perspective

Majhi

Mukherjee

Anand

2021

VJIKMS

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“…Conceptually, the methods of assortment planning optimization (Alfandari et al, 2021; Alptekinoğlu & Grasas, 2014; Chen & Jiang, 2019; Gallego & Topaloglu, 2019; Rusmevichientong et al, 2014) have worked on the assortment optimization. The ANN model (De Marco et al, 2021; Kalaiselvi et al, 2017; Kuo et al, 2002; Mohammadi et al, 2020) has used ANN for various facets of the retail analytics. The present study uses the algorithm of ANN for predicting the decision and discerning a better idea about the purchase intention of the consumer for direct to consumer brands.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Prasad¹,

Ghosal

2021

FIIB Business Review

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The direct-to-consumer (DTC) brands are emerging to reach more number of consumers with more potential to meet their expectations. They are characterized through their metamorphosis as the vertical brands sell their products from the manufacturer to consumers directly without any interruptions from distribution channels as in traditional mode of doing business. They are annihilating themselves in the virtual platforms and later disrupting their existing linear sales models. This empirical investigation is targeted to construct an algorithmic model through a deep learning process which has been instrumental to predict the purchase decision. This investigation has churned a predictive model that is based on the attributes of the buying behaviour of the consumers. The attributes of online buying behaviour like safety of transaction, availability of innovative products and quality of products have been considered to build a predictive model through artificial neural network (ANN). The accuracy of training and testing data are closer, which infers about the consistency and validity of the predictive model. There are several consequences arising from the predictive model obtained that can be seeded from customer-centred marketing and further stemmed from the framing of business strategy, gaining insights into market architecture and choice of customer

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“…Marco et al [25] show that the use of cognitive analytics management is a valid tool to describe new technology implementations for businesses. They found that a self-organizing map better classifies the customer base of a retailer by paring two machine learning algorithms.…”

Section: Edmondson and Mcmanusmentioning

confidence: 99%

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

2021

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Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

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Cognitive analytics management of the customer lifetime value: an artificial neural network approach

Cited by 20 publications

References 32 publications

Business value of cognitive analytics technology: a dynamic capabilities perspective

Business value of cognitive analytics technology: a dynamic capabilities perspective

Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

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