Based on prior knowledge, we can analyze the relationship between the feature "job" and the target variable "Does this client subscribe to a term deposit?".

Here is the analysis:

1. Management: It is likely that clients in management positions may have a higher likelihood of subscribing to a term deposit, as they may have higher income levels and more financial stability. 

2. Technician: The subscription to a term deposit may not be strongly correlated with the occupation as a technician. Hence, it is difficult to predict the likelihood based solely on this job category.

3. Entrepreneur: Entrepreneurs may have varied financial situations, and hence, it is difficult to predict their likelihood of subscribing to a term deposit based solely on their occupation.

4. Blue-collar: Blue-collar workers may have lower income levels and financial stability. Hence, they might have a lower likelihood of subscribing to a term deposit.

5. Unknown: The "unknown" category does not provide any information about the occupation, so it is difficult to analyze its relationship with the target variable.

6. Retired: Retired individuals may have saved money throughout their careers and might have a higher likelihood of subscribing to a term deposit.

7. Admin.: The occupation of being an admin may not have a strong relationship with the likelihood of subscribing to a term deposit.

8. Services: The services industry is broad and includes various occupations. Without further information, it is difficult to predict the likelihood based solely on this job category.

9. Self-employed: The occupation of being self-employed may not have a strong relationship with the likelihood of subscribing to a term deposit.

10. Unemployed: Unemployed individuals may have lower income levels and financial stability, which might result in a lower likelihood of subscribing to a term deposit.

11. Housemaid: Housemaids may have lower income levels and financial stability, which might result in a lower likelihood of subscribing to a term deposit.

12. Student: Students may have limited income and financial stability, resulting in a lower likelihood of subscribing to a term deposit.

Based on this analysis, we can create the following dictionary:

```json
{
	"no": ["technician", "entrepreneur", "unknown", "blue-collar", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"],
	"yes": ["management", "retired"]
}
```

Note: The values listed in the "no" group are the job categories that are difficult to predict or have a lower likelihood of subscribing to a term deposit.