Based on prior knowledge, we can analyze the relationship between the feature "poutcome" and the target variable "Does this client subscribe to a term deposit?".

The "poutcome" feature represents the outcome of the previous marketing campaign. It contains categorical variables with the following categories: ['unknown', 'failure', 'other', 'success'].

To analyze the relationship, we can look at the distribution of each category in relation to the target variable. We can calculate the percentage of clients who subscribed to a term deposit for each category of "poutcome".

Let's assume the dataset contains 1000 sample points, out of which 500 clients subscribed to a term deposit (target value: "yes") and 500 did not (target value: "no"). The distribution of "poutcome" for each target class could be as follows:

Target Class "no":
- Unknown: 400 clients
- Failure: 200 clients
- Other: 100 clients
- Success: 150 clients

Target Class "yes":
- Unknown: 100 clients
- Failure: 150 clients
- Other: 50 clients
- Success: 200 clients

Based on this analysis, we can now construct the dictionary:

```json
{
	"no": ["unknown", "failure", "other", "success"],
	"yes": ["unknown", "failure", "other", "success"]
}
```

Please note that the values "unknown", "failure", "other", and "success" are assumptions made for the purpose of this analysis. In an actual analysis, you would need to compute the actual frequencies or percentages from your dataset.