## What the solution does
For a single brand and a defined  **NBA_cycle**  (planning period), the solution **recommends  the best channel (or no action)**  for each HCP - balancing  **likelihood of engagement** ,  **HCP commercial value, channel impact** , and real-world budget/capacity/compliance constraints.

## Key challenges
**Attribution:** Prescription, Rx outcomes that usually come from third-party systems, are not directly attributable to individual actions; multi-touch journeys + lagged/aggregated Rx data. (We cannot prove "this single email caused +2 prescriptions")
 -  _Solution: Use Expected Impact as a relative ranking score, not absolute Rx prediction. Focus on probability of successful engagement, prescription volume and channel uplift._ 
 
**Data Quality:** Engagement success is binary (0/1) and sparse. Many HCP × channel combinations have zero history. We have no CRM notes (content-type granularity). Rx data may have coverage gaps or reporting delays.
 -  _Solution: Feature engineering with rolling windows, cross-channel patterns, and trend detection to enrich sparse signals._
 
**Explainability & adoption:**  reps/ops need a clear “why” with defensible logic and auditability. Raw ML model  values are hard to interpret and compliance requires transparency. 
 -  _Solution: LLM-powered translation of SHAP outputs into natural language explanations._ 