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Grinch Noisy

Reports Without Direction Can be “Noisy”Launch Quantity Dashboard

Welcome back readers, to another chapter in the DAX Reanimator Series! The post I’ll be re-envisioning today is one I’m very excited about. It’s an article that does some really cool magic using DAX, business logic, and a clever model design. It showcases a problem and solution that I think a LOT of companies experience. Frankly, if you sell a product or service, good chance you want to know how successful it is after launch.

Let’s Reinvent The Idea of “Time”

Sounds simple enough! We’d just write a DAX Measure to look at that product on a graph over time right? Well that’s where some complexities come in…because the launch of each product started at a different point in time. So placing sales data onto a graph with a date field would result in us comparing apples to oranges, since most product didn’t launch at the same time.

So the clever solution would be to create an artificial time perspective starting from the product launch date. With the launch date being the point-in-time for the release for a given product. Think of sales from product launch as a race, well now we’ve just put everyone on the same starting line. Still with me? Good, because we basically just became time ninjas!

P3 Time Ninja

What I Imagine A Time Ninja Would Look Like: Cumulative Quantity by Model & Normalized Time

“I Made This!” ~ A Time Ninja – AKA Cumulative Sales by Launch Period:

Highlights From The Original Post

The original post was written by us back in 2014, and can be found here. It’s original intent was to showcase the power of (the now retired) Power View. For those unfamiliar, Power BI Desktop is basically the spiritual successor and evolved form of Power View. The article does a bang up job of explaining all the concepts to of this model, and then walks you through all the steps needed to create your very own version. All of those steps can be transferred into a Power BI Desktop workbook, one of the beauties of the Power BI Desktop!

The post also references the coined terms for success rate buckets, originally conceived by some clever folks over at Tableau. They grouped success rates into three categories: the Rocket Ship, the Hot Burner, and the Slow Burner. These categorizations were originally from Tableau’s Tale of 100, and it is an article I would say is worth reading for the intellectually curious. It provides some cool analysis and charting when looking at how fast successful tech companies grow.

Power BI Desktop Chart Rocket Ship, Hot Burner, Slow Burner from Tableau

Example of the success buckets from the original post:

I want to also give one shout out to our colleagues over at SQLBI. They provided the Cumulative Total DAX formula we modified in the original post. It’s a link I’ve had bookmarked for YEARS, and has some wonderful DAX formulas and explanations. You can find more info about it either in the original post, or at the SQLBI link. Now with that said, here’s the Power BI embedded report! Enjoy, and until next time P3 Nation. Smile

Download the Power BI Desktop (.pbix) Report Here


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Reid Havens

Reid Havens

Principal Consultant at PowerPivotPro.

This Post Has 9 Comments
  1. Reid,
    Very nice. May I pose a question to you. Often this data is qualified by other comparative data such as depth of distribution, number of repeat purchases or repurchase frequency. How would you present this qualifying data within the same view?

    The reason I ask is that initial presentation of data can be misleading without the underlying drivers being shown alongside the main presentation. I often find the display of the underlying drivers complicates what initially was a simple story.

  2. Great post. The idea of time shifting is quite cool.
    I was wondering if the stacked area chart is the best option to show the cumulative totals for each product. It definitely has more visual impact than a standard line chart, but it makes it harder to compare the performance of different products. If all products were to be launched at the same time, a stacked chart would give us valuable insight about the performance of all the new products as a whole, but since that is not the case, I would rather go with the line chart.

  3. Have you found some negative values displaying in the DaysPastLaunch column of formulas table? I took those as pre-order before product launch, Make sense? By the way, as it was talking about the sales performance after product launch, why not rename the column name from ship date to launch date in the Cars table and the purchase date to sales date in the Formulas table intuitively?

    1. Hi Julian, those negative values were essentially pre-sales. Good suggestion as well to rename to launch date, I think that would make the column a more intuitive name.

  4. This technique can be used with debt to count months between the time a PO is issued and payments are made to a supplier. Or, from a collections standpoint to categorize clients by speed of payment (or days past terms).

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