Based on prior knowledge, we can analyze the relationship between the feature "month" and the task of whether the client subscribes to a term deposit or not.

One possible approach is to check if the month of the last contact has any impact on the subscription outcome. For this, we can calculate the distribution of the "month" feature for each target class: "yes" and "no".

Here is the analysis and the resulting dictionary:

```json
{
    "no": ["may", "jun", "jul", "aug", "oct", "nov", "dec"],
    "yes": ["mar", "apr", "sep"]
}
```

Explanation:

- For the target class "no", the months with significant presence are "may", "jun", "jul", "aug", "oct", "nov", "dec".
- For the target class "yes" which represents clients who subscribed to a term deposit, the significant months are "mar", "apr", "sep".

Please note that the analysis indicates the months that have a discernible influence on the subscription outcome. Other months not mentioned in the dictionary may not contribute significantly to the prediction.