Creating a simple weighting and scoring system

Say I want to create/chart a single time series score for 3 unequal metrics.

Let's say those metrics are PF-related:

1. New registrations that turn into PF Advisors
2. New registrations
3. Page views

The first thing I'd think to do is try to ballpark a multiplier for how much more valuable one metric is over the one below it. New registrations that turn into PF Advisors are the most valuable but also the least likely to occur so maybe that needs a large multiplier. Then you have a common metric like page views, this may not even need a multiplier because it will be high.

So how do I approach creating a weighted score between them that will communicate progress accurately?

For a single metric the key is getting a sensible weighting. So you can ask questions like “if I gained 1 registration and lost 10 page views, would I consider it progress?”

Do you have some other (independent) variables that you think drive changes in these three (dependent) variables? If so then you could also do something like a multivariate anova or a principal component analysis

You could do multiplicative instead of additive. Or take functions of the numbers.

I am not sure that becoming a PF advisor is a good metric. I think it is too rare an event and will look like noise. How about still active after a certain period of time? Or reach a number of posts?

Frabjous said:

I am not sure that becoming a PF advisor is a good metric.

True, we could say members that reach 10 posts