Personalized digital marketing recommender engine

Abstract

E-business leverages digital channels to scale its functions and services and operates by connecting and retaining customers using marketing initiatives. To increase the likelihood of a sale, the business must recommend additional items that the customers may be unaware of or may find appealing. Recommender Engine (RE) is considered to be the preferred solution in these cases for reasons that include delivering relevant items, hence improving cart value, and boosting customer engagement. The paper describes a model for delivering real-time, personalised marketing information concerning the recommended items for online and offline customers, using a blend of selling strategies: up-selling, cross-selling, best-in-class-selling, needs-satisfaction-selling and consultative-selling. The model further defines the e-marketplace by clustering items, customers and unique selling proposition (USP), and then gathering, storing, and processing transactional data, and displaying personalised marketing information to support the customer in their decision-making process, even when purchasing from large item spaces. An experimental study using a quantitative research methodology was conducted in a mid-size healthcare retailer, based out of India, to determine the tangible benefits. The model was tested with 100 online customers and, with the adoption of the proposed methodology, the results indicated growth in average monthly revenue (33.49%), Average Order Value (AOV) (32.79%) and Items per Order (IPO) (1.93%).