Power BI Builds 2 - Customer Insight

@JarrettM, kindly find, attached an update to my earlier visualization. Your criticism is welcomed.

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Wow Walter, impressive work once again!

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Sorry, yes it should be available now in the webinar series module at Enterprise DNA Online.

Video should be ready in a couple of hours

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Well done Diana. Looks great.

Hi Chad, nice work.

One small piece of feedback on your one is around the colours.

Check out this new tool we’ve created in our AnalystHub application

Consistent colours, that work well together is an important aspect of Power BI reports I believe.

It will make things stand out more

Sam

Hi all,

I really encourage those who’ve participated to discuss and talk through your solutions in more depth in new threads.

I believe we can be sharing more about how we went about the project. Share ideas and also get feedback on how we can get better at our work in Power BI.

I’ve kicked things off here.

Let’s all place these into the Project Updates category.

Look forward to learning more about what you’ve done.

Sam

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Hi Sam, thank you for the compliment.

It was a thought work with several combination and fancy thoughts before landing the final version. I tried the gauge and funnels but space usage was poor. I had to reinvent to meet my objective of having a single report page that provides both customer and engagement insights on all dimentions to provide our customer engagement manager (Janet) with a 365 view on one spot.

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Hello @Walter - is this reported to the power bi apps so we can navigate to it? Looks really fantastic.

Hi Brian,

I reflected several times over the report tried some combinations that didn’t work. My objective was to provide a single report page with all dimensions for both engagement and customer sales.

I tried the funnel and gauge but space usage was inefficient as you mentioned. I couldn’t tradeoff my objective.

Try and error landed me to the Donut Chart. Did some trials- it was fun!

1)The trick is to adjust the send emails for each of the engagement metrics.

An example for delivered Donut (the grey Donut): first adjust the Send to exclude deliveries given the inclusive nature of the metric.

– “SentAdjustedforDelivery = [EmailSent]-[Delievered]” (If this adjustment is not done the percentage depicted on the Donut would be inconsistent)

Then place “SentAdjustedforDelivery” and “Delievered” measures within the same Donut.
Do this for all the metrics.

The next step is to delicately place all the Donuts and make them compelling to your taste.

Use “Format” ===> Shapes ===>Inner radius to adjust the size.

  1. For the Pop-out Slicer, I used bookmarks.

  2. The tool used to create the color theme are my “eyes balls” :dark_sunglasses: and UX feelings I want to communicate.

Hope this helps.

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@Walter,

Bravo! I was certain that you had used a custom visual for that graphic. I had no idea that the standard donut visual was customizable in that way – I’ve never seen that done before. Thanks very much for the detailed explanation. I learn something new every day here…

  • Brian

Hi,

Here’s my submission for the Challenge. though is coming late (Sorry about that)
am open to your thought and contribution on area of improvement.

Clink link to view

DAVID.

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Hello Sam! Can we, by any chance, be able to download the final report you made for this challenge?

Yep will release it in the coming days.

We just released three other showcases on the showcase page.

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Here’s link to download my report for Power BI Challenge 2

Late submission as I work through the challenges I missed :)! See the report in action.

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@datazoe,

Super cool that you are going back through the previous challenges that you missed. There’s a whole lot I like about your report here, but there is one very big flashing red light. Scatterplots with the dynamically calculated trendline are great for showing the relationships between two variables – unfortunately they are also great at showing relationships that don’t actually exist, which is the case in this challenge.

This point was one of the primary focuses of my entry on this challenge:

Unfortunately, this challenge was before we commonly started sharing write ups about our entries, so I don’t have a ready-made supplement to accompany my entry. However, what I typically try to do when depicting these types of relationships is to report the correlation coefficient between the two variables ( or the R squared from a regression), as well as some indication of the significance of the correlation coefficient (either a p value, or what I did in challenge #4 which was to report the correlation matrix and have it place an “x” over each coefficient that was not significant at the 0.05 level.). My posting on challenge #4 includes the R script for how to do this.

image

R is a great way to run this analysis to make it dynamic within Power BI, but you can also do it using a custom visual (what I did in challenge #2) or using a Power BI measure for computing the Pearson correlation coefficient, posted on the Microsoft Community forum:

Ruth from Curbal also did a nice video a while back on the same topics:

There are a lot of different options, but reporting the magnitude and significance of the correlation coefficient will help prevent the consumers of your report from drawing the wrong conclusions

I hope this is helpful.

  • Brian

@BrianJ I did see your report, and it’s really well done! I actually did the scatter plot on purpose, knowing your point. This data is so flat, I wanted something ha. To be fair, I don’t think I’d call any of these plots a correlation looking at them as they are, even if the trend lines go up.

I left it because I wanted to see some change, but I suspect the increase is simply because more time had past after email than before. I’m going to add in an average per day and average per month into the report to test that theory. See it here: Now with avg per day and avg per month

For the top plots, it’s also fake, because all I’m doing is upping my chances on the fake data. Out of 45 days with emails sent, I get 1 sale on the same day. 34 days with emails sent I get 4 on the same day. As the number of emails sent increases, so does my chances that the day of sale is going to be the same. These are terrible outcomes for clicking a link especially. I was trying to prove that spamming isn’t actually driving the customer away. (it’s having no affect at all)

Edit: Also, there is not enough data here to really say the email affected sales. For one, there is too much email. If I email everyday and people make sales, I can’t say that the email was why that sale happened. Second, there are a lot of things that would impact sales – was it payday? was it black Friday? was there a massive sale? did they post something on twitter? was there no pandemic (ha)? I run into this with work, people want to see if business initiatives had an impact, but there is rarely a clear indicator. I’ve found that simply asking in a survey yields better results. Asking directly, did our email affect your purchase today (yes, no, what email?). It is also why I didn’t try to interpret the data.

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@BrianJ I’ve been thinking about your comments all week. What you say has merit so I have already removed the trend lines and changed out the titles, as I think they were misleading.

I am still thinking of how to best show what I want effectively. I like the scatter plots because honestly I never get to use them in a report and I’ve seen so many entries with them and I wanted to try it!

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@datazoe,

I like the scatterplots too. I would suggest just including a card with the correlation coefficient and p value. In cases where the null hypothesis is rejected (it never is in this challenge due to the randomized data, but generally speaking), you may also want to include the trendline.

  • Brian
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Hello Everyone,

Here is my work on the Customer Insights challenge.

Hope to get feedback and suggestion!

Thank you!

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