Customer Satisfaction Score Analysis - Coding Interview

Question

## Problem

A customer support system stores satisfaction surveys as `(ticket_id, customer_id, agent_id, score, timestamp)` where score is 1-5. Implement a `SatisfactionAnalyzer`:

```python
class SatisfactionAnalyzer:
    def add_response(self, ticket_id: str, customer_id: str, agent_id: str,
                     score: int, ts: int) -> None: ...
    def agent_avg(self, agent_id: str) -> float: ...             # overall average score
    def agent_trend(self, agent_id: str, window: int) -> float: ...  # avg over last `window` responses
    def top_agents(self, n: int) -> list[tuple[str, float]]: ...     # top n by overall avg, desc
    def customer_history(self, customer_id: str) -> list[tuple[str,int]]: ...
    # returns (ticket_id, score) sorted by timestamp asc
```

**Example:**
```
sa = SatisfactionAnalyzer()
sa.add_response("T1","C1","A1",5,1000)
sa.add_response("T2","C2","A1",3,2000)
sa.add_response("T3","C1","A2",4,3000)
sa.agent_avg("A1")        -> 4.0
sa.agent_trend("A1", 1)   -> 3.0
sa.top_agents(1)          -> [("A2", 4.0)]
```

## Follow-ups
- How would you implement `agent_trend` in O(1) per query using a sliding window?
- Extend to flag agents whose 7-day trend is declining by more than 1 point vs their overall average.
- What database schema and indexes would you use to support these queries at scale?

Customer Satisfaction Score Analysis - Coding Interview

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