Creating Data-Driven Scoring-Models

Oct 30, 2018

Klaus Schulte

Scoring-Models are something like a Swiss Army Knife in decision-making and you can use them on almost unlimited occasions. They are models in which various criteria are weighted and result in a score. Based on this score a conclusion or a decision can be made or an advice can be given.

They follow a very structured setup:

- Define criteria
- Weight criteria
- Assess alternatives based on the defined criteria (e. g. on a scale from 1-10)
- Calculate part-scores ( 2. x 3. )
- Calculate overall score ( ∑ 4. )

Now imagine a use case where your defined criteria from step 1 can be assessed in step 3 **based on data**:

- You want to calculate a customer score based on criteria like recency, frequency or monetary ratio (like in the RFM-Model)
- You want to build an investment portfolio on criteria like rating, fund volume, performance, etc.
- You search a location for a new plant or new markets to invest in based on economic data

The only thing left to do in this case is to weight the criteria (step 2) and to calculate the scores (step 4+5). And this is something that can be easily done in **Tableau**! Such a data-driven scoring-model is something I have already created in several real-world projects.

Let me explain you how to set it up based on my #makeovermonday visualization from week 09/2018 (*click the image to play with the interactive version on Tableau Public; I used the same approach in my winning #IronViz on the Big Mac Index*):

My analysis is searching for new markets in Africa to invest in based on the criteria *freedom to trade*, *legal system*, *regulation*, *size of government* and *sound money* (**step 1**). These criteria are taken from Fraser Institute’s World Economic Freedom Index (https://www.fraserinstitute.org/economic-freedom).

While Fraser Institute weights all criteria equally to calculate its world economic freedom index I wanted the user of my dashboard to play with the weight of the criteria to see how this affects their (in this case fictive) investment decision.

I therefore created five parameters (one for each criterion) like this one for the criterion *freedom to trade*:

This parameter can be used to evaluate the importance of a certain criterion from 0-10. (*There are also more advanced methods to weight criteria but I will not dwell on that in this blog post.*)

Based on these five parameters I was able to calculate a relative weight of a criterion; all relative weights sum up to 100%.

Then I created 5 BANs to show the relative weight of each criterion and showed the parameter control.

Users of the dashboard are now able to change the parameter resulting in different weights for the criteria and changing investment advices based on the weighted index from this formula:

So, just some basic math that leads to a nice **actionable** dashboard that will for sure put a smile on your audience’s faces 🙂

I hope you enjoyed reading and that you will find own use cases for this!

## COMMENTS