Tuesday, December 27, 2011

Taking the Radar Chart off the Map

As part of my job I spend a lot of time reviewing analyst’s research on various industries like financial services, customer communications, hosted contact centers, and business intelligence and analytics.  Disappointingly, I frequently come across pin-wheel or radar graph-like circular visualizations similar to the one displayed below.

In this image each shape and color combination represents a different vendor in the industry and ranks their overall customer survey scores for each category. 
Most of us have heard all the arguments against pie charts and other circular visualizations.  This type of visualization is particularly troubling to me because it’s from an analysis of the BI industry where you might expect better.   When I see a visualization like this I intuitively want to line up the spokes of the wheel on top of each other like a bar chart so I can compare all the scores for a single vendor.  Consequently I end up wasting limited cognitive equity doing visually perceptive somersaults trying to create a virtual horizontal line graph in my head instead of analyzing the vendor capabilities.
To my delight a solution has been brought forward and it comes courtesy of the guys at Contemporary Analysis (CAN) right here in Omaha.  In a November blog posting on dashboard design they talked about using a bullet graph versus a bar graph to compare the survey scores of multiple vendors.  It’s the same problem that the example above is trying to solve but my buddies at CAN created a visualization that line up the spokes on the wheel for me.
Here’s the link to the original blog post: Dashboard Design: Bullet Graph vs. Bar Chart
This is the CAN graph and it shows the different shapes for each vendor on the same line for each survey question and the questions are stacked on top of each other for easy comparison of a vendor from survey question to survey question.
The CAN Graph

There is also a dark line that shows the distance from the low score to the high score to help the viewer understand the range of scores for each survey question. 
This is exactly what I try to do mentally whenever I come across one of those pinwheel radar charts.  This visualization worked so well for me that I decided to figure out how to build it in Tableau.
To recreate this chart using Tableau I started with a conventional horizontal bar chart.

Next I changed the mark type to “Shapes” and moved the “Company” pill to the Shape Shelf.  This created an individual shape to represent each vendor.  I thought about creating custom shapes that matched the shapes in the original graph to represent each company but I decided to leave the default shapes because I thought it was easier to see through them when two or more companies share the same score on a survey question. 
I’m only distinguishing between four vendors.  I don’t think these shapes would work well if there were any more than four vendors.
Finally, I added light row banding to help guide the viewer's eye across the chart and I right clicked on the x-axis and selected “Add Reference Line…” which brought up a dialog box where I selected “Band” which shades the range from minimum to maximum.  I think the range band works a little better visually than a dark line plus it was easier to make in Tableau. 
 Here’s the final version recreated in Tableau:
I think the Tableau version is true to the original CAN graph.  There are just a few differences.  I started the x-axis at zero instead of 2.5 which gives wider and possibly more accurate spacing between the shapes.  I used the built-in shapes which are different than the shapes in the original and I used shading instead of a dark line to highlight the range of values on a row. 
I can’t wait to use this in my work.  Special thanks to Grant Stanley from CAN for letting me link to his blog post and comment on it and hats off to Tadd Wood, also from CAN for designing the chart.

Wednesday, December 14, 2011

The Importance of Business Intelligence for Proactive Customer Care

Originally posted on my employers blog on December 8, 2011: West Interactive Blog

In 1989, Gartner analyst Howard Dresner introduced the term “business intelligence,” which he defined as “concepts and methods to improve business decision-making by using fact-based support systems.” The world has been trying to redefine it ever since.

Let’s say I’m looking to implement a business intelligence solution. I can find lots of definitions for the term business intelligence, or “BI,” depending on where I look. Gartner’s Magic Quadrant for Business Intelligence goes so far as to identify 13 specific capabilities that make up a BI platform, nine of which must be must be delivered for a software vendor to be included in the Magic Quadrant.

Successful notifications campaigns hinge on the ability to deliver relevant, personalized messages to the right customer, at the right time on the right channel. However I try to define it, when it comes to executing on a multichannel notifications strategy, a BI system needs to tell me what has happened and to who, what is happening right now, and what is likely to happen next.

In order to decide the best treatment for a specific customer, I need a historical perspective on what has happened before. I need to understand the key characteristics that apply to each customer, and I need to map that back to what happened on previous interactions. The BI system also needs to be able to tell me which specific customer characteristics are predictors of desired outcomes. This information helps define customer segments and gives me the insight I need to test and apply personalized treatment strategies to specific customer segments.

Next, I need to be able to test the various treatment strategies against subsets of a customer segment to maximize the effectiveness of the strategy. For example, I need to be able to determine which contact channels are most effective to maximize contact rates within a customer segment, and I need to be able test different outbound criteria profiles and message personas to see which ones are most successful at calling customers to action. And, most importantly, I need to be able to see the results and make changes on the fly. If I have to wait too long for the data, then I’ve missed an opportunity both in terms of financial impact and customer satisfaction. Real time is a must.

Knowing what’s going to happen next is the secret sauce that makes a proactive notifications treatment strategy truly sustainable. If I can predict how a customer segment will perform against a specific treatment strategy, I can apply that strategy consistently to achieve optimal results.

A precise definition of BI is not that important to me. Communicating what BI does and how my performance benefits from BI is important. I know I have a winner if I can achieve the following:

  • Use individual customer characteristics to define customer segments and match to prior outcomes
  • Test various treatment strategies against a customer segment and implement the best one on the fly
  • Define a sustainable treatment strategy by predicting how customer segments will perform