Based on prior knowledge, the feature "balance" can be a good indicator of whether a client will subscribe to a term deposit or not. Typically, clients with higher balances may be more likely to subscribe to a term deposit.

To analyze the relationship between the feature "balance" and the task of subscribing to a term deposit, I would first group the data by the target variable (subscription status) and then examine the distribution of different value ranges of "balance" for each target class ("no" and "yes").

Here's the dictionary with the analysis results:

```json
{
	"no": [0.0, 100.0, 200.0, 300.0, 400.0],
	"yes": [500.0, 1000.0, 1500.0, 2000.0, 2500.0]
}
```

In this example, I have provided 5 typical balance values for each target class. These values are just placeholders and can vary depending on the data and context. It's important to note that this analysis is based on prior knowledge and assumptions, and actual analysis should be performed on the specific dataset to validate these findings.