Predictive Analytics involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The Application
Organizations use predictive analytics to:
- Forecast customer churn
- Predict purchase likelihood
- Identify cross-sell opportunities
- Optimize pricing strategies
- Anticipate market trends
The Limitation
Predictive models are only as good as the data they are trained on. They often:
- Reinforce past patterns, making it hard to see new possibilities
- Struggle with “black swan” events or unprecedented changes
- Can create self-fulfilling prophecies (e.g., ignoring “low score” leads who might have been great customers)
- Miss the human context and emotional drivers behind behaviors
The Value-First Perspective
Predictive analytics should inform human judgment, not replace it. Use predictions to identify opportunities for value creation, not just for extraction. Be wary of reducing complex human potential to a probability score.