I'm seeking advise on how to calculate ppm levels of customer complaints for PMS in a B2C market. EU medical and non-medical.
Theoretically (normal) the calculation would be #complaints(from_sales(mthX))/#sales(mthX)*1.000.000, monthly figure
What is preventing us from getting this figure is:
- There is a big delay between complaint date and the sales date. This delay is a distribution therefore we can't use one figure for correction.
- Only from a little fraction of the complaints we get the device serial nr to track back to the sales date.
Solutions that have been proposed are:
- PPM calculation by cumulative numbers -> #complaints(CUM)/#sales(CUM)*1.000.000, monthly figure
- PPM calculation by moving annual total -> #complaints(MAT)/#sales(MAT)*1.000.000, monthly figure
- The three calculations above but with the individual complaints corrected for av (sales->complaint) delay to het the amount of complaints approximately at the right month that correlates to the sales
The problem with the solutions is that they all create their own artifacts in graphs either at the start of sales or at the end of sales.
Needles to say the straight forward PPM calculation of complaints per month against sales per month does not give a realistic number due to the delays between sales and complaints.
The delays can be in the range of a few days up to several months.
Does anyone have encountered such a situation and have implemented a solution to come up with a number that will show if complaints are rising or lowering independent of sales?
I would love to hear about it
Theoretically (normal) the calculation would be #complaints(from_sales(mthX))/#sales(mthX)*1.000.000, monthly figure
What is preventing us from getting this figure is:
- There is a big delay between complaint date and the sales date. This delay is a distribution therefore we can't use one figure for correction.
- Only from a little fraction of the complaints we get the device serial nr to track back to the sales date.
Solutions that have been proposed are:
- PPM calculation by cumulative numbers -> #complaints(CUM)/#sales(CUM)*1.000.000, monthly figure
- PPM calculation by moving annual total -> #complaints(MAT)/#sales(MAT)*1.000.000, monthly figure
- The three calculations above but with the individual complaints corrected for av (sales->complaint) delay to het the amount of complaints approximately at the right month that correlates to the sales
The problem with the solutions is that they all create their own artifacts in graphs either at the start of sales or at the end of sales.
Needles to say the straight forward PPM calculation of complaints per month against sales per month does not give a realistic number due to the delays between sales and complaints.
The delays can be in the range of a few days up to several months.
Does anyone have encountered such a situation and have implemented a solution to come up with a number that will show if complaints are rising or lowering independent of sales?
I would love to hear about it