Based on prior knowledge, we can analyze how the feature "campaign" relates to the task of whether the client subscribes to a term deposit.

An analysis of the relationship between the feature "campaign" and the target variable would involve examining the distribution of "campaign" values for each target class ("no" and "yes") and identifying typical values for each class.

Here is a dictionary with the format requested, including typical campaign values for each target class based on the analysis:

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

The above dictionary suggests that typical campaign values for both target classes ("no" and "yes") range from 1.0 to 5.0. This indicates that clients who subscribe to a term deposit and those who don't tend to have similar distributions of campaign values within this range.