# Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships
## 5 Agenda for Future Research
### Applying Emotion Attitude Intelligence to Further Improve Customer Relationship  
_\"There is no separation of mind and emotions; emotions, thinking, and learning are all linked.\"_ --[PERSON]  
Emotional intelligence enables machines to recognize and quantify human emotion and attitude, which is important in handling customer relationship. Different approaches are needed when different media types are used. Examples are to recognize and quantify: (a) the level of dissatisfaction based on a text complaint written in an online review; (b) the level of dissatisfaction in an audio call from an angry customer; and (c) the happy facial expressions seen in an image and a video [68]. Naturally, a machine does not possess emotional intelligence. Hence, a machine that categorizes an emotion accurately does not feel the emotion itself. Similarly, a machine that prevents an accident does not understand the importance of life. This opens another area of learning that AI can explore and exploit, whereby the right emotion is related to the right sets of actions in order to improve the common sense of machines. Ultimately, emotional intelligence helps machines to understand customer emotion in order to enhance customer relationship in an unobtrusive manner, whereby customers and users are unaware to have interacted with machines [69]. Emotion intelligence is important to: (a) encourage customers to interact with machines and humans in the same way [70;71]; (b) optimize media content in an advertisement in order to identify the right leads and improve their satisfaction; and (c) determine when to stop displaying an advertisement without annoying customers [38]. Further investigation can be pursued to introduce emotion intelligence into the AIM framework. Defining the Right Objective Functions to Prevent Bias and Discrimination while Improving Customer Relationship  
_\"Much has been written about AI's potential to reflect both the best and the worst of humanity. For example, we have seen AI providing conversation and comfort to the lonely, we have also seen AI engaging in racial discrimination.\"_ --[PERSON], Google Brain  
Using AI, the learning mechanism maximizes or minimizes an objective function, which captures the rewards (or penalties) for the appropriate (or inappropriate) marketing actions selected under certain environments. Examples of rewards are maximizing customer satisfaction, customer retention, sales and profits, market share, etc. Penalties capture the opposite of rewards, and an example is customer churning. While crafting the objective function based on general goals may lead to an improved overall performance, unfavourable consequences related to bias and discrimination may occur occasionally due to the lack of awareness on inclusive and sensitivity. As an example, in customer engagement, the machine may make gender-biased decisions that promote some products to a certain group of people, causing the rest to miss a promotion opportunity. As another example, in customer engagement, the machine may not choose to promote a product to a certain racial group of people, most of whom have a history of preferring another product. Imagine a machine that shifts its focus to promote products and promotion to profitable customers of a certain group of people with the same gender or race, such action can cause the other groups to become disgruntled and expedite their churning. Further investigation may be pursued to minimize social bias and discrimination to improve customer trust. Improving the Explainability and Interpretability of AI to Improve Sensibility while Enhancing Customer Relationship  
_\"Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model.