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This paper aims to develop and implement a machine learning recommendation engine – an adaptive learning engine that drives business revenue through the ranking and recommendation of offers at a granular customer level across the inbound marketing channels.

A data set of over 300,000 unique sample of mobile customers was extracted and prepared. The gradient boosting machine (GBM) algorithm was developed, consolidated, deployed and experimented on two inbound marketing channels.

Research examining machine learning implementation and operationalisation within the large consumer base is seemingly silent. This paper bridges this gap by developing and implementing a machine learning adaptive engine across two inbound marketing channels. The performance of the inbound channels revealed the significant importance of digital campaigns that are driven by machine learning algorithms. Machine learning techniques can be well positioned as an integral part of a large consumer base marketing operations with real-time one-to-one marketing capability.

The study explores the use of machine learning, a cutting-edge subset of artificial intelligence (AI), to drive consumer business revenue across different marketing channels. Further research should explore these marketing channels in greater depth by considering other branches of AI in driving consumer business revenue.

This study demonstrates the value, ease and application of a machine learning deployment in a consumer business with a large customer base in driving business revenue. It also shows customers' practical response to offerings across channels and the importance of the digital channel to firms with a large customer base.

The paper defines how machine learning extracts can be deployed and operationalised by marketers to drive business revenue. This approach is unique, realistic, easy to deploy and will guide future research in this space.

The perceived electronic-service quality (e-SQ) has become a relevant research area, not only in developed but also in smaller, less-developed countries. The purpose of this paper is to provide a description of an analysis into the relationship of the dimensions of perceived e-SQ and satisfaction as well as WOM intention in case of an online bookstore in Hungary where technical development and internet penetration is emerging; however, it is developing from an economic perspective. Beyond this a potential segmentation is introduced in the Hungarian market.

The direct effect of perceived e-SQ’s dimensions on satisfaction and on traditional WOM were analysed using the PLS-SEM method, which was followed by the segmentation approach. The paper also demonstrates differences of the identified consumer segments, using multivariate analysis of variance.

According to the research only the dimension of efficiency and responsiveness have a significant positive effect on satisfaction, and beside these the quality perception of fulfilment has a significant influence on WOM intention. Using the relevant latent variable scores segmentation was conducted and four clusters were identified.

Due to peculiarities of e-services, quality measurement needs a constant revision and adoption. Extent amount of research has been dedicated to analyse the relationship of quality and satisfaction, but the direct effect of relevant quality dimensions on word-of-mouth intention is a new research field. Segmenting customers based on latent variable scores of the proposed model has not been conducted before in case of an online bookstore in Hungary. According to the results the evaluation of the technology-based components has the greatest effect on satisfaction and WOM intention. However, web-shops managers should focus not only on online characteristics but also on offline, human-based interactions and the service quality of their delivery partners.