2022: Week 7 - Call Center Agent Metrics

Challenge by: Kate Brown

This week we have another incredible challenge created by Tableau Social Ambassador Kate Brown:

For this week’s challenge we need to create a data set for our call center agent metrics. We have 2 Excel files that contain the monthly metrics for the agent and another file that contains the agent, leader, location, and goals. 

If you work with databases you may encounter situations were you have one table that has ids and another table(s) that have the descriptors for those ids. We aren’t connecting to a database in this example, however, think about the people, location, leader, and date inputs in that way.

Inputs

  • People Data:
    • People 

    • Leaders 

    • Location 

    • Goals 

    • Date Dim 

  • Metric Data 2021:
    • Jan - Dec

Requirements

  • Input the data
  • People, Location, Leader, and Dates:
    • Join the People, Location, and Leader data sets together
    • Remove the location id fields, the secondary leader id field
    • Create last name, first name fields for the agent and the leader
    • Limit the dates to just 2021 and join those to the People, Location, Leader step
    • Keep the id, agent name, leader 1, leader name, month start date, join, and location field
  • Monthly Data
    • union the worksheets in the input step
    • merge the mismatched fields
    • create a month start date
    • remove the table names and file paths field
    • join the data with the people - remember we need to show every agent for every month
  • Goals
    • add the goals input to the flow
    • clean the goal data to have the goal name & numeric value
    • add the goals to the combined people & data step
    • be sure that you aren't increasing the row count - the goals should be additional columns
  • Metrics & Met Goal Flags
    • create a calculation for the percent of offered that weren't answered (for each agent, each month)
    • create a calculation for the average duration by agent (for each agent, each month)
    • create a calculation that determines if the sentiment score met the goal
    • create a calculation that determines if the not answered percent met the goal
  • Output the data

Output

  • 18 fields
    • id
    • Agent Name
    • Leader 1
    • Leader Name
    • Month Start Date
    • Location
    • Calls Answered
    • Calls Not Answered
    • Not Answered Rate
    • Met Not Answered Rate
    • Not Answered Percent < 5
    • Calls Offered
    • Total Duration
    • Agent Avg Duration
    • Transfers
    • Sentiment
    • Sentiment Score >= 0
    • Met Sentiment Goal
  • 1620 rows (1621 including headers)

You can download the full outputs here. 

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 



Popular posts from this blog

2024: Week 1 - Prep Air's Flow Card

Image
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works.  Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis.  You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year&
Read more

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will use dif
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in
Read more