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What I don't really understand is why you would want to do this The data is well summarised in the cross tabulation already

thank you

You explain tough concepts so clearly.

What, if some of your quantities are the same as volume breaks themselves ? Also, what do you mean by multi column approximate match ?

918 Kohler Stravenue

Hi At 4:30, I noticed for getting M value you are dividing sum of sales by sum of quantity. Why is that? Can't we just sum the sales amounts for each customer to get the highest contributing customer? PS: Can you provide a link to the dataset? Thanks

How can we download files used in this video?

Such an amazing job!

brilliant

как вы считаете churn rate если вы не знаете кто из тех кто купил в прошлом месяце купил в этом?? DiffCust - это просто разница между количеством покупателей, которая может сказать лишь о том, стало ли покупателей больше в текущем месяце или нет

I'm working on a huge data project and this technique is extremely helpful.

You lose the sound at the 16:14 mark.

You're a Lifesaver!

thank you very much, this really helped me a lot!

You should provide the dataset or the powerbi file so that it becomes easy for everyone to just experiment it easily

Really Very help full and clear communications. Subscribing your channel Now

Super ....Great job ...👍

how do we create the rfm chart if we have the value all sorted out

But how can we do daily. I tried but it show only 16 variable maximum, but we have 30 days

Okay so far so good? how do you use this data to create a parameter in the direct query?

Great Technique Sir, Kindly share practice file used in here to follow along. Thanks Again.

Could you share the data file for Practice please ?

@Data.Analytics.Central Can this method be used for data that isn't sales? So example, number of patients seen by a hospital over time?

Good examples, thanks. But what about HAVING ?

Hi, thanks for the video. Very helpful. The M Score formula should have [M Value} instead of [F Value]. I think this is causing a lot of the issues other people are having.

Very helpful. However, PBI only imported the first 25 movies. Not sure why. BTW, I have the latest (May 2024) Power BI version.

Useful. Thank you.

You have a DAX formula for Active customers. Does that mean I need to filter out Inactive customers?

You started the formula half way I did get where the value came from

Wonderful tips.. thank you very much..

Thank you for your great video! Do you think this work correctly with 12 months data historical and some month value = 0 as well ? I look forward to hearing your advices. Then how can we apply for multi sku products.

If I have to do the same thing but on a day level How can I achieved that?

Thanks for the video was very useful.

Thanks, was driving me nuts!

Amazing

This is amazing. Thank you for sharing this video.

Best video ever! Thank you very much!

Your M score formula is just the same as F score formula 6:16

Thank you, I was looking for his. It is so helpful.

Made more sense to me when I realized you had two different y axis. If you do this again, better to show them on the same y axis. Awesome info otherwise! Thanks

Very Nice Workaround thought using fill down

The best lecture, Thank you so much!

This was a good solution. I also found that you can modify this query to collect the 2nd value if say the values were in varying columns "Step Name" = Table.AddColumn(#"Add Name Column", "Value", each List.First((List.RemoveFirstN(List.RemoveNulls(Record.ToList(_)),2))))

This is exactly the solution I was looking for. I've liked and subscribed, looking forward to learning more from your valuable video tutorials.

Hey, great video. Nicely explained. Thank you!!!🤓

u are the best bro thank you for the help🙏

Hey man, I used the data and the given DAX code, I ended getting all the RFM = 555 why is that so in the RFM Table? is there something wrong with the DaX code?

good stuff

please provide sample file for practice

meaning of Granularity ??