Understanding Histograms and Their Uses

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Understanding histograms may seem daunting to many, because it is misconstrued that the mathematical steps involved are complicated. On the contrary, the process merely involves tabulating the data on a tally sheet where values are organized from smallest to largest.

This is a basic procedure, because a histogram is a graphical presentation of substantial information grouped into data sets or classes, for which frequencies or occurrences are counted.

(View the sample tally sheet and the related histogram on your left. Click any image for a larger view.)

Nevertheless, certain technological advancements are now in place to take care of the tallying processes up to the preparation of histograms, which make the work less tedious. Still, understanding histograms and their uses rests on the analyst, since it is quite important in today’s business trends and methodologies.

Image Credit: Ryan, T. A., Joiner, B. L. and Ryan, B. F.at Wikimedia Commons

To explain the significance of using a graph for a large amount of data, a case study is utilized to illustrate data presentation via a histogram.

Case Study of Call Center's Failure for Rapid Response

A call center agency wants to improve its system as an inbound call center and service agency. Although there’s no specific industry standard to which handling of calls should adhere, the agency's goal is to sustain its ability to respond rapidly to customers' calls. Currently, the hold-time it takes for their agents to respond has been falling short of the previous track record of less than a minute.

In line with this, a study was conducted in order to determine if the agency needs more staff to attend to the customers. Fifty (50) inbound calls for technical support took more than a minute before a help-desk technician could attend to the customers' requests for assistance. In fact, it was observed that half of the fifty calls took more than five minutes, which can be gleaned from the following data gathered during the course of the observations.

Number of Minutes the Customers Were On Hold

• 0-1 minute = This is the rapid-response time by which phone calls should be answered.
• 1-2 minutes = Two (2) customers had hung up their phones.
• 2-3 minutes = Five (5) more customers had hung up.
• 3-4 minutes = Eight (8) customers also gave up on holding.
• 4-5 minutes = Ten (10) customers stayed on the line and were attended to.
• 5-6 minutes = Another set of (10) customers who waited while on hold, had been given assistance.
• 6-7 minutes = Seven (7) more customers had been provided with technical assistance.
• 7-8 minutes = Another set of four (4) customers waited for their turn to be served.
• 8-9 minutes = Three (3) of the customers simply gave up and decided to hang-up.
• 9-10 minutes = One (1) customer’s patience paid-off and he was finally served.

Now if you are to study the data presented above, would you be able to perceive with relative ease, the implications of the data presented?

Not quite, because it’s a bit difficult to focus and delve into words and numbers at the same time to visualize a larger concept. This is now where the histogram comes in as a useful and practical tool.

Data Presentation With the Use of a Histogram

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At a glance, the data for the 50 customers who had waited beyond the rapid-response time, can be gleaned through the use of a histogram. Planners can readily perceive that the hold-time for the calls can even last up to 10 minutes before they could be attended to.

By this simple graphical presentation, it communicates information to a project team that there really is a need to improve the agency’s call-handling systems. Otherwise, the agency is bound to face potential problems if this trend is allowed to continue or get worse.

What other inferences can be derived from the histogram?

Proceed to the second page in order to find out how histograms can be used for analysis.

Image Credit: Histogram for case study on Call Center Hold-Time was created by the author for this article.