After my previous post about variables, I got an interesting question from DV. He asked me about the reuse of chart expressions by referencing the label of another expression (“label referencing”), and what the performance implications of using variables and label referencing versus direct expressions are.
I use variables and label referencing extensively in my applications, but I never really tested what this means for performance. I have always assumed that using variables instead of direct expressions would have a slight impact on performance. I also suspected that using label referencing would result in significantly better performance (I will explain this later).
But was this really true? Triggered by DV’s question, I set up a small experiment to test my assumptions.
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It has been a while since my last post. To get back in the habit of regular updates, I am starting today with a short tip on a caveat of the use of the equals sign (=).
Starting an expression with or without an equals sign may almost seem like an arbitrary decision. Most developers quickly figure out that this is not true for text objects. However, there is another, less obvious area where the use of the equals sign can greatly impact how (and more importantly, when) your expression is calculated.
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December 27, 2010 – 16:00
In a previous post I described how to create a custom sort order in QlikView by assigning a sort order value in the load script or by using the match function. This post describes two other clever methods that I recently became aware of:
Read on to see how these methods work.
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December 17, 2010 – 15:22
A short tip today on how to ensure that dimensions in different charts get assigned the same color, regardless of how the data is sorted or if dimensions are missing.
The image on the right shows an example in which revenue per beverage is visualized in a number of different charts and tables. Each beverage is assigned its trademark color (Coca-Cola Red, Heineken Green, Pepsi Blue etc.) and this color is used consistently in each of the charts. Read on to see how you can accomplish this effect.
(Please note that using lots of very bright/saturated colors in your dashboard or report is generally not a good idea, I am only using these colors because they are recognizable to many. Do not try this at work!)
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November 18, 2010 – 12:30
With over 80% of data* having a spatial component,
geographical analysis can add a powerful new dimension to almost any reporting environment. In the coming time I intend to review the various methods of extending QlikView with geographical analysis capabilities, describing how to apply these methods and what their pro’s and cons are.
Read on to see the options I have identified so far. read more »
November 10, 2010 – 00:44
Do you know if your customers are profitable? All of them? Performing Customer Profitability Analysis can answer these questions and give you some amazing, and sometimes counter-intuitive, insights into your customers’ contribution to your bottom line.
This post describes one of the visualizations that you can create once you possess accurate data* on the profitability of your customers: the whale curve.
In a whale curve, customers are ranked by profitability, from highest to lowest, on the X-axis while their accumulated profit is plotted on the Y-axis. The curve that results can, with some imagination, be said to look like a whale coming out of the water. An example of a whale curve chart is shown below.
When you look at this chart, you may notice that the top 200 customers generate the bulk of the profit. You may also notice that the you are losing serious money on the bottom 100 customers and that the customers in the middle are more or less break-even.
Read on to learn how to create a whale curve in QlikView. Even if you’re not interested in creating a whale curve, you might still want to read on to learn more about the rank function and the continuous x-axis. read more »
QlikView offers quite a few ways to easily sort dimensions in your listboxes, tables and charts: by frequency, numeric, text or load order. But what if you want to use a custom sort order that does not follow one of these patterns?
Consider, for example, the following scenario; we have three Business Lines: A, B and C. These Business Lines always need to appear in the order B, A, C. This type of ordering cannot* be achieved by using any of the default sort orders, we will have to create a custom sort order. This post will describe two methods for doing this.
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Decile analysis is a popular segmentation tool. Where a pareto analysis splits the top 20% customers (or products, regions, etc.) from the bottom 80%, decile analysis divides them into equally sized groups of 10%.
The image below shows an example of a decile analysis.
The example shows how a group of 1.000 customers is divided into deciles of 100 customers. Lots of interesting things can be learned from this analysis, amongst other things:
- Your top 10% customers are generating profit that is significantly above average;
- Your top 30% customers are responsible for 80% of your profit;
- You are losing money on your bottom 20% customers (the so-called “bleeders”).
So, how do we create a decile analysis in QlikView? read more »
