I have data of various sellers on ecommerce platform. I am trying to compute seller ranking score based on various features, such as
1] Order fulfillment rates [numeric]
2] Order cancel rate [numeric]
3] User rating [1-5] { 1-2 : Worst, 3: Average , 5: Good} [categorical]
4] Time taken to confirm the order. (shorter the time taken better is the seller) [numeric]
My first instinct was to normalize all the features, then multiply parameters/feature by some weight . Add them together for each seller score. Finally, find relative ranking of sellers based on this score.
My Seller score equation looks like
Seller score = w1* Order fulfillment rates - w2*Order cancel rate + w3 * User rating + w4 * Time taken to confirm order
where, w1,w2,w3,w4 are weights.
My question is three fold
Are there better algorithms/approaches to solve this problem? i.e I linearly added the various features, I want to know better approach to build the ranking system?
How to come with the values for the weights?
Apart from using above features, few more that I can think of are ratio of positive to negative reviews, rate of damaged goods etc. How will these fit into my Score equation?
How to incorporate numeric and categorical variables in finding seller ranking score? (I have few categorical variables)
Is there an accepted way to weight multivariate systems like this in a better way and build a ranking system in general? Any reading suggestions are welcome