Based on prior knowledge, the feature "poutcome" represents the outcome of the previous marketing campaign. It is a categorical variable with four possible values: 'unknown', 'failure', 'other', and 'success'. 

To analyze the relationship between the feature and the task of whether the client subscribes to a term deposit, we can examine the distribution of the feature values for each target class (yes and no).

Here is the analysis:

- For clients who did not subscribe to a term deposit (target class 'no'), the 'poutcome' values could include 'unknown', 'failure', and 'other'. This implies that previous marketing campaign outcomes of 'unknown', 'failure', and 'other' are associated with the client not subscribing to a term deposit.

- For clients who subscribed to a term deposit (target class 'yes'), the 'poutcome' value of 'success' is likely to be more prevalent. This suggests that the previous marketing campaign outcome of 'success' is associated with a higher likelihood of the client subscribing to a term deposit.

Based on this analysis, we can create the dictionary as follows:

```json
{
	"no": ["unknown", "failure", "other"],
	"yes": ["success"]
}
```
Please note that this is a general analysis based on prior knowledge. The actual relationship between the feature and target may vary depending on the dataset and specific context.