Customer lifetime value (CLV or LTV) refers broadly to the revenue that a company can attribute to one or more customer over the length of their relationship with the company [55]. The process of predicting the lifetime value consists in producing one or more monetary values that correspond to the sum of all the different types of revenues that a specific customer, or a specific cohort, will generate in the future. The purposes of this prediction are manifold: for example, having an early estimation of a customer's potential value allows more accurate budgeting for future investment; moreover, monitoring the remaining potential revenue from an established customer could permit preemptive actions in case of decreased engagement. Predicting customer lifetime value is a complex challenge and, to date, there is no single established practice. Furthermore, due to its wide potential impact in different business aspects, the problem is being researched in different communities using a plethora of different techniques, varying from parametric statistical models to deep learning [28, 70].

As marketing strategies of enterprises evolve to become more data-driven, analytical, and focused on engagement, the worldwide use of robots armed with artificial intelligence seems naturally imminent in the future of interactive marketing. The field of robotics has experienced a rapid progress in recent years, giving emergence to robots that are getting ever-closer to perfectly emulate humans in interactive marketing. Although initially robots were conceptualized and developed almost exclusively for automate industrial operations, the integration of AI, machine learning, and deep learning have opened up a world of possibilities for the application of robotics in different business areas. One of the areas that can benefit from the use of robots is marketing. As traditional, unidirectional marketing is being replaced by interactive marketing, there is an increasing need for systems that collect and analyze massive volumes of customer data.