To implement explainable AI in a retention strategy, the article suggests unifying customer data from all sources, uncovering hidden patterns to diagnose the factors driving customer dissatisfaction, segmenting customers based on common risk drivers, and empowering the frontline to take action using intelligence. The author emphasizes that explainable AI is not a one-time project but a continuous journey that helps refine models, gain a deeper understanding of customers, and fine-tune interventions for better retention results.
Key takeaways:
- Explainable AI can help businesses understand and predict customer churn by identifying risk indicators, making customer-level predictions, and providing increased transparency into the decision-making process.
- Explainable AI enables actionable decisions by providing insights into customer sentiments and recommending targeted offers based on individual customer risk profiles.
- Implementing explainable AI doesn't require a complete data science overhaul. It involves unifying customer data from all sources, uncovering hidden patterns, segmenting customers based on risk drivers, and empowering frontline staff with intelligence.
- Explainable AI is a continuous journey that helps refine models, gain deeper understanding of customers, and fine-tune interventions for better retention results.