Based on prior knowledge, the marital status of a client could potentially be a useful feature in predicting whether the client will subscribe to a term deposit or not. It is possible that individuals who are married might have different behavior or priorities compared to those who are single or divorced, which could influence their decision to subscribe to a term deposit.

To analyze the relationship between the feature "marital" and the target variable, we can examine the distribution of the marital status categories for both the "yes" and "no" classes of the target variable.

Here is the dictionary analysis for the given feature:

```json
{
    "no": ["married", "single", "divorced"], 
    "yes": ["divorced", "single", "married"]
}
```

Please note that the values within each list represent the possible marital status categories for the respective target classes. It is important to understand that this analysis is based on general assumptions, and it may vary depending on the specific dataset and context.