In a previous post, I described a simple way on how to discover your blue measures for Quality Incentives.
The blue measures (just as the blue states) are those measures where your plan obtained the maximum possible points for Quality Incentives in the last 3 o more measurement periods.
The red measures, on the other hand, are those measures where you in several measurement periods definitively can not obtain higher than 0 or 2.5 points.
In the same way as the blue measures analyzing at first level the trends over the measurement periods, we can identify which are your "worst measures" (red states) for your plan.
The analysis here is slightly different because we need to identify why yours hasn't been able to score higher.
You have to consider different things here:
1) if you are always obtaining 0 points, it depends on your improvement over time? (a candidate who can not appeal to the electorate)
2) If your plan has been improving, has it done it enough compare to the statewide average? (it is about the candidate policies, personality, history?)
3) Who are those plans who performed better than your plan and improved its points? (which candidates are improving in that state and why?)
4) Is there anything your plan can do to begin to thrive, improve denominator, marketing? (changes a candidate must do to improve)
Clearly, the red measures (red states) represent the most difficult measures to improve. But it is highly important to identify them either to assign more resources to those measures or rearrange your plan's priorities.
We can help you to find those red measures and analyze them carefully to improve your results and points.
There are a lot of variables involved in improving Quality Incentives (as in primary and general elections). We know it might be difficult for you to keep track of all these variables, that is why we can do it for you with Lagrange AI.