Adaptive learning engine for driving marketing channel performance: a machine learning approach

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.