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10-21-2015 06:44 AM
Hi,
There is a requirement to configure custom assessments for calculating the T-Shirt size of a Demand.
However, we were not able to figure out the logic/formula used to determine the weighted and the Normalized value of a Metric Result
All our Metrics are of Assessment Type so that the Actual and the Scaled values are same.
The wiki mentions the below about Normalized value (Assessment Results - ServiceNow Wiki😞
Adjusted value that accounts for weights, scale definition, minimum and maximum values, and other factors that impact the metric.
Can anybody help with the logic/formula used to calculated the Weighted and the Normalized Value of a metric.
Regards,
Kuldeep
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12-01-2015 01:10 PM
We had a ticket opened with SN and received the following calculation for the normalized value in Metric Results. I hope somebody finds it useful.
(Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor defined in metric type
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10-22-2015 06:43 PM
Hi Kuldeep. To answer the question, we have a Category score and Metric scores. The sum of weighed metrics for each category gives us the category score. Then the category score is multiplied by a category weight. This weight is different depending on the total number of assessment metrics for a demand. In addition, it doesn't always add up to 100, its rather a relative weight. For instance, if we have a small Size (meaning the scaled result for the Size category is 2), we then multiply this by say 10/80 (where 10 is Size weight and 80 is the sum of all weights), so you get .25 as a normalized value for Size.
Hope this helps.
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12-01-2015 01:10 PM
We had a ticket opened with SN and received the following calculation for the normalized value in Metric Results. I hope somebody finds it useful.
(Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor defined in metric type
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02-20-2018 01:00 AM
This was the closest explanation for the calculation. Hence, marking it correct
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06-12-2019 11:40 PM
Hi We too faced similar situation:
Example is below regarding the calculation:
1. Survey Definition -> 1 Category -> 3 metrics (service, staff, timeliness)
For each of them we use the smiley faces very dissatisfied (1) -> very satisfied (5). in the middle we have other values such as dis-sat, neutral, satisfied.
Now if you do a survey where you choose very satisfied (5) for all responses then calculation for normalized value is as follows:
Say for Service Metric (please note scale factor is always 10 unless you changed it)
Input value = 5 (very satisfied)
Min value = 1
Max value = 10
Metric Weight = 10
Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor.
So:
(Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) = (5 – 1) / (10 – 1) = 4/9 = 0.4444
Scale factor = 10.
current metric weight / (sum of valid metric weight) = 10/(10 + 10 + 10) = 10 / 30 = 0.333
Normalised Value = 0.4444 * 0.333 * 10 = 1.481 ~ 1.48.
This means normalised value in such scenario is 1.48 (high values good)
Category Normalised value (if every thing was very satisfied) = 1.48 * 3 = 4.44
This much I could verify Hope this helps !!