Scatterplot Analysis

The purpose of the article is simply and objectively presenting a solution for the analysis of a given content through a scatter plot, given the problem in which it is necessary to improve the service performance of certain customers.

For the study development and data analysis a base was used in which it has the information of the employees pertinent to the process in addition to the data from the last three months of its activities, let's take as an example the following indicators: 'Number of Calls Answered', 'Average of Call Time' and 'Productive Time', we can also add qualitative indicators to the study. However, we will only focus on quantitative indicators.

The image below illustrates this data:

After gathering all the requirements for investigation, I chose as a methodology for the study of the segregation of the information set entitled Quartile. However, these parts were separated into 4 equal parts to facilitate the analysis.

As a classification criterion, the call time average was used, classifying it from the shortest to the longest.

After data classification and categorization, we can build two scenarios based on the inputs generated.

First Scenario: They make up the Quarterly Average between the results of 'Average Answer Time x Answered Calls'.

Second Scenario: They make up the Quarterly Average among the indicators of 'Average Service Time x Productive Time'.

Let's take a look at the results of the first and second scenarios.

First Scenario: This is a view, where we can compare Answer Time Average x Answered Calls, which shows the distribution by quartile of each operator. 

We can also raise some concerns. 

• 1Q employees with Average Call Time low and Calls Answered below average.
• 1Q employees with Average Call Time high and a very few Calls Answered.

Second Scenario: The analysis was performed based on the Average Service Time x Productive Time and a table in which simulates the actual Service Capacity of employees per Quartile.

Point of attention to taking into consideration are: High Average Service Time and low Productive Time

• Low Average Attendance Time and low Productive Time
• Opportunity 1 - Reduction in Average Attendance Time.
• Opportunity 2 - Increased Productive Time.
• Excellent - keep low TMA and High Productive Time.

Conclusion:

Through the information obtained and with a few steps applied. We were able to generate the scatter plot, which helped us in developing the data analysis making it possible to extract data information for decision making.

Reference:

Siddharth Kalla (Jan 20, 2011). Quartile. Retrieved Nov 20, 2019 from Explorable.com: https://explorable.com/quartile

Note:

The graphics as well the content described are my own, being used as external sources only the concept of quartile calculation.

This is my first public article written in English, thank you for reading any questions or suggestions look for me.

Thank you very much,

Helber Teles, published Nov/2019.