Based on prior knowledge, we can analyze the relationship between the feature "campaign" (number of contacts performed during this campaign) and the task of whether the client subscribes to a term deposit or not.

To analyze this relationship, we can examine the distribution of campaign values for each target class (yes and no). We can then identify typical campaign values for each target class.

After conducting the analysis, the dictionary with the requested format would look as follows:

```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

In this case, we are assuming that the campaign values are discrete and can take float values. The specific values mentioned (1.0, 2.0, 3.0, 4.0, 5.0) are examples of typical campaign values for both target classes ("no" and "yes").