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davidjimenez18's avatar
davidjimenez18
Frequent Visitor
4 years ago

Creating a Quality Dashboard from Raw data

Good morning Team, 

 

          I'm trying to create a Dashboard for Quality for my Call center team; however, I've never have worked with Power Bi always used Excel. I wanted to know if you guys could help me or give me direction on how can I calculate and analyze be below data set into a Dashboard. We get the quality result, not with percentages but with the questions and the answers (results). 

 

Example: 

 Q: Was the agent kind and polite on the call?             A: Yes or No

 Q: Were all needs and requests met on the call?         A: Yes or No

 Q: Any Escalation was needed?                                    A: Yes or No

 

We get the report with the questions and the answers and from there we take the amount of "yes" and divide it by the number of questions which would give the % for the call quality. See below the report we get. Any help will be much appreciated!

Note: the (0.00) beside the answers have no meaning (result/percentage-wise)

 



 

5 Replies

      • lbendlin's avatar
        lbendlin
        Icon for Super User rankSuper User

        Thank you for providing the sample data - that is very helpful. 

         

        It looks like your "Question" column may need some refinement, especially for the BEHAVIORS section. From what I understand each main question (like "Take Ownership") is then divided into a positive and negative path.

         

         

        There is a (0.00) note behind each question.  Is that where the weight would be?  Or should all these items be treated equally? Is "quality" the ratio of "Effectively or Exceptionally Demonstrated" to the overall count?

         

        Ultimately - what behavior are you trying to drive with the report?  Identify and reduce the negative answers? Or only the top issues?

         

        Side note: There is a data quality issue for "Build a Personal Connection" - the main question has tagged 20 calls but the sub-questions have tagged 21.  The label for MIE is inconsistent too. Not a big issue but data quality assurance should be part of your process. 

         

        See attached for a first stab at a quality chart.