Mastering QlikView Data Visualization

Mastering QlikView Data Visualization
Mastering QlikView Data Visualization

The book, Mastering QlikView Data Visualization, happens to be my lost adventures as a QlikView consultant. I haven’t promoted it much because after two years of writing, I was too anxious to run off to learn other skills. A year and a half after its publication, QlikView is still popular and customers continue to look for ways to squeeze everything they can out of QlikView like a tube of toothpaste. If you need help in doing so, I recommend that you read my book.

You’re Not Alone

A current Google trends analysis of Qlik-related search terms shows that “qlikview” keyword is still more popular than the tool-neutral “qlik” or the keyword for “qliksense” or “qlik sense.” Its relative popularity is in decline from its peak in June 2015 and will likely be surpassed by “qlik” in 2018 and by “qlik sense” in 2019/2020.  However, these trends, along with my own personal experience working with companies that have invested much capital into QlikView, tell me that it will continue to be an important tool for many companies over the next several years.

A Bucket Full of Ideas

Mastering QlikView Data Visualization is my bucket full of ideas for these companies that have QlikView and are looking for new ways to analyze and visualize their data. Here are some highlights from the book.

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Exponential Distributions in Qlik

Last week, I gave the business case for using an exponential distribution to predict a customer’s purchase frequency and detect at-risk and lost customers, Sales Analytics in Qlik: From the Basics to Statistical Modeling. In this week’s post, we go over the details of calculating and visualizing the exponential distribution in QlikView using the following chart.  I also add some final thoughts on how to use the information it tells us in a real-world sales dashboard.

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Sales Analytics in Qlik: From the Basics to Statistical Modeling

The basics

The most common Qlik application involves sales data analysis. Period.

Well, I don’t have enough information to back that up, and since data analysis is my life, I can’t make unsupported claims without some major nervous facial twitching (or so my wife says). However, I would bet based on personal experience that it is one of the most common applications, and moreover, I’d go as far to say that it is often the first analytical application that businesses develop after buying Qlik.

One the most obvious reasons that this could be true is that sales data is huge, low-hanging fruit. Sales is what drives most businesses and the data trail it leaves is usually the most readily available data to analyze. When a company purchases Qlik, it is often after continuous investments in an ERP (Enterprise Resource Planning) system, a CRM (Customer Relationship Management) systems, a customer portal and/or numerous Excel reports – all of which make sales data ripe for harvest.

Many of you are probably familiar with the following sales metrics.

MetricDefinition
Gross Sales RevenueSales before discounts measured in monetary units
Net Sales RevenueSales after discounts measured in monetary units
Sales VolumeSales measured in non-monetary units such as an individual item, boxes, pallets, kilograms, tons, etc.
Unit SalesAlso referred to as Average Price, it can be defined as Net Sales Revenue divided by Sales Volume
HitsNumber of sales transactions or invoices
Gross ProfitNet Sales Revenue minus the Cost of Goods Sold (COGS)
Gross Profit MarginGross Profit divided by Net Sales Revenue

If you are familiar with these metrics, you’ll also be well acquainted with the series of dimensions that often slice and dice them.  Catalogs that describe customers, sales representatives, products, dates, branches, stores, promotion codes, and channels answer the questions of who, what, when, where, why, and how that surround the sales numbers.  You’ll also recognize the importance of using some type of reference data that comes in the form of a budget, a forecast, or at the very least, historical data.

Almost immediately after businesses start to take advantage of this basic, yet powerful, sales data analysis, they start to adjust their business questions. We can answer some new questions by adding a new metric, a new dimension, or a new visualization, but some new questions involve more advanced analysis techniques.  One of the most popular questions that I encounter and that requires a more sophisticated approach is the evaluation of customer retention and the detection of customers that the business is endanger of losing.  For here on, we’ll refer to this type of analysis as customer churn.

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