The article further explores two types of predictive AI solutions: vertical and horizontal, and introduces Graph Neural Networks (GNNs) as a potential advancement in predictive AI. GNNs can boost machine learning models' pattern recognition abilities and eliminate the need for creating training sets and feature engineering. Despite the complex transition to GNNs, they offer significant advantages, as demonstrated by Amazon's use of a GNN model to detect malicious accounts. The article concludes that B2C companies implementing GNNs can predict the future with greater precision, validating more business decisions and setting the stage for long-term success.
Key takeaways:
- Generative AI is not sufficient for B2C use cases as it is not predictive in nature and cannot provide actionable data analysis and predictions for critical business decisions.
- Predictive AI helps B2C companies analyze large amounts of data and predict metrics such as churn, customer lifetime spend, and customer lifetime value, helping them identify the correct next steps.
- There are two types of predictive AI solutions available today: vertical solutions, which focus on niche problems, and horizontal solutions, which are applicable to broader use cases. Both have their own challenges and require significant oversight from data scientists.
- Graph Neural Networks (GNNs) may be a scalable technological advancement to predictive AI, offering advantages over older approaches by eliminating the need for feature engineering and iteration, and improving the precision of future predictions.