Book Image

QlikView: Advanced Data Visualization

By : Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover
Book Image

QlikView: Advanced Data Visualization

By: Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover

Overview of this book

QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: • QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Table of Contents (25 chapters)
QlikView: Advanced Data Visualization

Customer stratification

We had the following user story:


As a sales representative, I want to see who my most important customers are so that I can focus my time and effort on them.

A customer's importance is determined by a mixture of measures. In the sales perspective, we started to determine a customer's importance using a Pareto analysis over sales. The following diagram shows the results of a customer stratification based on sales:

We can use Pareto analysis to stratify all measurements whose total is the sum of its parts, such as gross profit and quantity. However, there is another set of customer metrics whose total is an average of its parts. For example, the total company DSO is a weighted average of the DSO of each customer. In this case, we use quartiles to stratify customers.

Finally, once we have more than one measurement that stratifies customers, we look at how to combine them both numerically and visually. Even though we discuss customer stratification, the same principles...