Exercise 3: Customer Feedback

Note on this Data
The Customer Feedback data set is a more full dataset specific to feedback scenarios based on [Moro et al., 2014] S. Moro, P. Cortez, and P. Rita. “A Data-Driven Approach to Predict the Success of Bank Telemarketing.” Decision Support Systems, Elsevier, 62:22-31, June 2014. For the purposes of our lesson we will consider a scenario where we are collating public feedback for crocodile captures however this customer feedback example may have a lot of applicability across your organisation in employee and customer experience.
Task 1: Analyse a feedback metric that is categorical
For this exercise open the customer feedback.pbix file in your student starter materials. Your Captures Manager
wants you to figure out which factors lead customers to leave negative reviews about your capture initatives.

1. Open the customerfeedback.pbix file – to reinforce Key Influencers we will now start using some alternative data for customer feedback that is not specific to captures but keep an open mind.
2. Under Build Visual on the Visualisations pane, select the Key influencers icon

3.Move the metric you want to investigate into the Analyze field. To see what drives a customer rating of the service to be low, select Customer Table > Rating.

4.Move fields that you think might influence Rating into the Explain by field. You can move as many fields as you want. In this case, start with:

• Country-Region
• Role in Org
• Subscription Type
• Company Size
• Theme

5.Leave the Expand by field empty. This field is only used when analysing a measure or summarised field.

6.To focus on the negative ratings, select Low in the What influences Rating to be drop-down box.

The analysis runs on the table level of the field that’s being analysed. In this case, it’s the Rating metric. This metric is defined at a customer level. Each customer has given either a high score or a low score. All the explanatory factors must be defined at the customer level for the visual to make use of them. In the previous example, all of the explanatory factors have either a one-to-one or a many-to-one relationship with the metric. In this case, each customer assigned a
single theme to their rating. Similarly, customers come from one country or region, have one membership type, and hold one role in their organisation. The explanatory factors are already attributes of a customer, and no transformations are needed. The visual can make immediate use of them. Later in the tutorial, you look at more complex examples that have one-to-many relationships. In those cases, the columns have to first be aggregated down to the customer level before you can run the analysis. Measures and aggregates used as explanatory factors are also evaluated at the table level of the Analyze metric. Some examples are shown later in this article.