Authors: Giulia De Poli, Maria Angélica Lobo Paulino, Stefania Tola, Manuela Bazzarelli, Leone De Marco, Matteo Bregonzio
Optimisation in digital advertising is a complex task deemed to increase customers engagement and satisfaction. In more detail, optimisation involves not only identifying the right images, template and timing to engage a given customer, but also understanding the context and judge whether the message is actually relevant to the recipient.
To be successful advertising optimisation requires taking into account lots of data coming from multiple digital sources such as Customer Relationship Management (CRM), web analytics, and advertising interactions. Although this process could be performed manually by marketing specialists, more recently data-driven methodologies have shown promising results.
In this direction, our study proposes an automated system addressing advertising optimisation via a supervised learning approach where decision-making is performed accounting for the latest customers interactions in a near-real-time fashion. Specifically, this work presents a solution for direct email marketing (DEM) composed of three modules: monitoring, decision-making and automation. Monitoring is provided through a web dashboard showing historical performance of relevant Key Performance Indicators (KPI). The decision-making module computes a relevance score predicting how a given email message or sequence of messages are suitable for a specific customer or cluster of customers. Subsequently, this score is used to support the decision process within the automation module in order to deliver fully personalised messages. Experimental results confirm that the proposed DEM management system promotes customer satisfaction minimising perceived spamming. Moreover, DEM activities contribute to boost the revenue without sacrificing the customer’s experience.