<p>Nowadays, the incorporation of XAI—explainable artificial intelligence—into customer analytics has gained major attraction driven by the need for trust, transparency and actionable understanding in predictive modelling. This systematic literature review analyses the application of XAI techniques like SHAP, LIME and also other AI models like machine learning, deep learning, ensemble models and further provides counterfactual explanations in churn prediction, customer segmentation and retention strategies. Using a structured search across important databases like Scopus, IEEE and other digital libraries, 80 studies published between 2015 and 2025 were selected and analysed. This review categorises XAI approaches by industry application, interpretability findings and methodology and emphasises their role in improving model decision-making and transparency. The findings explore that XAI not only enhances stakeholder trust but also enables more ethical and targeted customer interventions. A comparative study has also been performed for XAI techniques for churn prediction, customer segmentation and retention. The limitations in real-time deployment, standardisation and evaluation metrics are discussed in gaps and discussions along with future research directions for XAI models, hybrid models and domain-specific explainability contexts.</p>