Hebblerock
New Member
- Joined
- Oct 15, 2019
- Messages
- 1
Hello everyone,
I'm trying to calculate the impact three ever changing variables are having on our month on month performance. We have 10 buckets that are filled with applications and the scenario is:
So if for example volume shifts from one bucket to another where the conversion is lower that results in a worse performance, but this could be offset by an increase in conversion on another bucket.
How is it best to take into consideration all these moving pieces and pinpoint where performance went wrong (Or right). Example data below
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I'm trying to calculate the impact three ever changing variables are having on our month on month performance. We have 10 buckets that are filled with applications and the scenario is:
- The total number of applications changes month on month
- The % of total applications that sit in a bucket may change month on month
- The conversion rate of any buckets can also change.
So if for example volume shifts from one bucket to another where the conversion is lower that results in a worse performance, but this could be offset by an increase in conversion on another bucket.
How is it best to take into consideration all these moving pieces and pinpoint where performance went wrong (Or right). Example data below
No. Apps | 65000 | No. Apps | 70000 | ||||
Month 1 | P/O No. | Month 2 | P/O No. | ||||
1 | 10% | 2% | 130 | 1 | 10% | 2% | 140 |
2 | 10% | 12% | 780 | 2 | 10% | 10% | 700 |
3 | 10% | 2% | 130 | 3 | 10% | 2% | 140 |
4 | 10% | 5% | 325 | 4 | 5% | 5% | 175 |
5 | 10% | 7% | 455 | 5 | 20% | 5% | 700 |
6 | 10% | 2% | 130 | 6 | 5% | 2% | 70 |
7 | 10% | 3% | 195 | 7 | 10% | 3% | 210 |
8 | 10% | 5% | 325 | 8 | 10% | 5% | 350 |
9 | 10% | 10% | 650 | 9 | 10% | 9% | 630 |
10 | 10% | 1% | 65 | 10 | 10% | 3% | 210 |
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