🧑🏻💻 Comparison Matrix
In progress - Not sure - to discuss
But this is to reduce bias. We are all very subjective, but let's put some numbers and see what the result is.
Here's a sample comparison matrix
| Proposal | Innovation (20%) | Market Potential (30%) | Technical Feasibility (20%) | Team Experience (15%) | Financial Projections (15%) | Total |
|---|---|---|---|---|---|---|
| Proposal A | 8 | 9 | 7 | 6 | 8 | ? |
| Proposal B | 9 | 8 | 8 | 7 | 9 | ? |
| Proposal C | 7 | 7 | 6 | 8 | 7 | ? |
| Proposal D | 6 | 9 | 9 | 5 | 6 | ? |
Just an example
Maybe we don't care as much about Innovation, maybe we would rather focus on more market potential, or rather: Ability to raise. The more important it is to us the more weight.
To calculate the total, you can multiply each criterion score by its corresponding weight and sum them up. For example:
- Proposal A: (8 x 0.2) + (9 x 0.3) + (7 x 0.2) + (6 x 0.15) + (8 x 0.15) = ?
- 1.6 + 2.7 + 1.4 + 0.9 + 1.2 = 7.8
Just an example
We then can fill in the total scores for each proposal and then rank them accordingly.
Here's some concrete for the current proposal(s):
| Proposal | Innovation (20%) | Market Potential (30%) | Technical Feasibility (20%) | Team Experience (15%) | Financial Projections (15%) | Total |
|---|---|---|---|---|---|---|
| ResponsAI | 8 | 9 | 7 | 6 | 8 | 7.8 |
| Proposal B | 9 | 8 | 8 | 7 | 9 | 8.3 |
| Proposal C | 7 | 7 | 6 | 8 | 7 | 7.2 |
| Proposal D | 6 | 9 | 9 | 5 | 6 | 7.5 |
Now we can rank the proposals based on their total scores:
- Proposal B (8.3)
- Proposal A (7.8)
- Proposal D (7.5)
- Proposal C (7.2)
We should adjust the weights and criteria