During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.

Preventing customer churn, i.e., termination of business commitments, is essential for companies operating in saturated markets, especially for subscription-based models such as telecommunication. Knowing when customers decide to terminate services is instrumental to effective churn prevention. In this study, we investigate how churn prediction performs in practice when training models on different time intervals of historic data (1-4 weeks back) and predicting churn at different numbers of weeks ahead (1-4 weeks). We use a real-world, time-series dataset of mobile subscription usage to examine churn prediction for business-to-business (B2B) customers. We utilize the timeseries data at a higher temporal resolution than prior studies and investigate different forecasting horizons. Leveraging popular machine learning algorithms such as Random Forests, Gradient Boosting, Neural Networks, and Gated Recurrent Unit, we show that the best model achieves an average F1-score of 79.3% for one-week ahead predictions. However, the average F1-score decreases to 63.3% and 61.8% for two and four weeks ahead, respectively. A model interpretation framework (SHAP) evaluates the feature impact on the models' internal decision logic. We also discuss the challenges in applying churn prediction for the B2B segment. 

Maintaining high degree of customer satisfaction is important for any corporation, which involves the customer support process. One important factor in this work is to keep customers' wait time for a reply at levels that are acceptable to them. In this study we investigate to what extent models trained by the Random Forest learning algorithm can be used to predict e-mail time-to-respond time for both customer support agents as well as customers. The data set includes 51,682 customer support e-mails of various topics from a large telecom operator. The results indicate that it is possible to predict the time-to-respond for both customer support agents (AUC of 0.90) as well as for customers (AUC of 0.85). These results indicate that the approach can be used to improve communication efficiency, e.g. by anticipating the staff needs in customer support, but also indicating when a response is expected to take a longer time than usual. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Customer support can affect customer churn both positively and negatively. By identify non-routine e-mails to be handled by senior customer support agents, the customer support experience can potentially be improved. Complex e-mails, i.e. non-routine, might require longer time to handle, being more suitable for senior staff. Non-routine e-mails can be considered anomalous. This paper investigates an approach for context-based unsupervised anomaly detection that can assign each e-mail an anomaly score. This is investigated in customer support setting with 43523 e-mails. Context-based anomalies are investigated over different time resolutions, by multiple algorithms. The likelihood of anomalous e-mails can be considered increased when identified by several algorithms or over multiple time resolutions. The approach is suitable to implement as a decision support system for customer support agents in detecting e-mails that should be handled by senior staff.