The goal of the research presented here is to describe an innovative approach to predicting the impact of a business messaging campaign, by estimating the percentage of message recipients who will engage with a message. The motivation is to facilitate business marketers to address the problem of estimating the return on investment coming from a potential messaging campaign. The presented solution relies on the processing of large scale business data, taking into account state-of-the-art predictive algorithms, GDPR compliance requirements, and the challenge of increased data security and availability. In this paper we discuss the design of the core functional components of a system that could make this possible, which encompasses predictive analytics, data mining and machine learning technologies in a cloud computing environment.

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One of the main goals of customer relationship management is to reduce or eliminate "customer churn", i.e. loss of existing customers. This paper introduces a prototype algorithm to estimate a continuously updated indicator of the probability of an existing customer to cease purchasing from a subscription commerce business. The investigation is focused on the case of repeat consumers of subscription commerce products which require regular replacement or replenishment. The motivation is to help marketers to make targeted proactive retention actions by categorizing regular customers into groups of similar estimated churn risk. The proposed algorithm re-computes the probability of churn for each customer at regular intervals using past purchase transaction data and incorporating subscription-based business logic. We describe the detailed process from data collection and feature engineering to the algorithm's design. We also present evaluation results of the algorithm's performance based on a pilot test that took place on a consumables ecommerce business. The results suggest a significant capability of the proposed algorithm in capturing the purchasing intentions of repeat customers, regardless of the risk group they belong to.

Most of today's digital marketing campaigns which are sent through email and mobile messaging are bulk campaigns which deliver the same message at the same time to all customers, regardless of their needs and preferences. The outcomes are bad customer experience, low engagement and low conversion rates. Modern marketing automation tools aim to facilitate personalized communications, such as scheduling of individual marketing messages based on each individual subscriber's profile. This research focuses on the problem of automatically deciding on the optimal date and time for sending consent-based personalized marketing messages. We specifically focus on the case of repeat consumers of consumer packaged goods (CPG) which require regular replacement or replenishment. The objective is to timely anticipate the needs of consumers in order to increase their level of engagement as well as the rate at which they repurchase products. The proposed solution is based on a regression model trained with transactional data and instant messaging metadata. We describe the way such a model can be created and deployed to a scalable high-performance environment and provide pilot evaluation results that suggest a significant improvement in marketing effectiveness.