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

To analyze the relationship between the feature and the target variable, which is whether the client subscribes to a term deposit or not, we can look at the distribution of the "poutcome" categories for each class of the target variable.

Here is the dictionary with the specific details:

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

For the "no" class, the feature values for "poutcome" include "unknown", "failure", and "other". These are the possible values observed in the dataset for clients who did not subscribe to a term deposit.

For the "yes" class, the feature values for "poutcome" include "unknown" and "success". These are the possible values observed in the dataset for clients who did subscribe to a term deposit.

Note that I did not include the category "other" in the "yes" class as it was not observed in the dataset.