Based on prior knowledge, we can analyze the relationship between the "poutcome" feature and the target variable.

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

We can assume that the outcome of the previous marketing campaign can have an impact on the current subscription decision. If the previous campaign was successful, it might increase the likelihood of the client subscribing to a term deposit. On the other hand, if the previous campaign was a failure, it might decrease the likelihood.

To analyze this relationship, we can group the clients based on their subscription decision and then examine the distribution of the "poutcome" values within each group. This will give us insight into how the different outcomes of the previous campaign are associated with the current subscription decision.

Now, let's create the dictionary:

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

In this case, we can include all the categories of the "poutcome" feature in the dictionary for both "no" and "yes" target classes.