2
$\begingroup$

I have monthly sales data of 500 restaurants for one year.

For the same restaurants, I also have customer defection or dissatisfaction rates.

I want to create a composite score that can rank restaurants based on their performance on sales and customer satisfaction. Something similar to customer life-time value, but as my unit is customer and the time series is only 12 months, I am wondering if there is any suitable technique to derive a composite score to rank restaurants.

Improve this question
$\endgroup$
4
  • 3
    $\begingroup$ What is the aim of this compsite score that two different ranks of the data you have do not solve? $\endgroup$
    – kurast
    Commented Jun 18, 2014 at 14:21
  • $\begingroup$ Because of two reasons. 1.) A restaurant just on strong on financial may not be sustainable if it customers are unhappier. For example, a restaurant could be in food court of high traffic but actually has low customer satisfaction. 2). Both variables together create a single view of the restaurant for ranking purpose. $\endgroup$
    – user3658835
    Commented Jun 19, 2014 at 0:25
  • $\begingroup$ See the answer here. $\endgroup$
    – Scortchi ♦
    Commented Jun 20, 2014 at 13:56
  • $\begingroup$ Also here. $\endgroup$
    – Scortchi ♦
    Commented Jun 20, 2014 at 14:02

1 Answer 1

Reset to default
1
$\begingroup$

To create a composite rank I suggest two steps. Normalization and combination.

The aim of normalization (also called standardization) is to each of the metrics to the same order of magnitude. I will list some, but there are many more:

  1. Linear, or min-max (set min value at 0, max value at 1)
  2. Gaussian normalization (subtract the mean, divide by stddev)
  3. Inverse tangent normalization

To combine, there are also many methods, I will list some:

  1. Linear combination (a.rank1+b.rank2)
  2. General polynomial (a.rank1n+b.rank2n)
  3. Multiplication (rank1.rank2)

Since you provided no utility function, there is nothing to fit, so I suggest you start with linear normalization and linear combination with equal weights, to visualize the results and see if they serve your purpose.

You see this is all independent of the length of the series, however the combination step is better suited if both lengths are equal. However, if you use Gaussian normalization, you can use zero for all the observations you don't have, and it will not induce any bias to the measure.

Improve this answer
$\endgroup$
1
  • $\begingroup$ This is very helpful. Unfortunately I am not very familiar with linear combination or general polynomial approach. Can you through some light on these? Which statistical technique would allow me to accomplish this? $\endgroup$
    – user3658835
    Commented Jun 23, 2014 at 1:54

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.