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Prediction Techniques for Power BI - How to Set Up This Type of Analysis

The future may be unknown, but we have the capability to predict it when it comes to business. The importance of profit prediction in business cannot be overemphasized. While there are several approaches and analytical tools out there to predict profitability, Power BI is one of the best tools to use for this type of analysis.  


There are unlimited profit prediction possibilities when using Power BI. The key is to learn how to implement the correct development techniques, and that’s the aim of this post. In this tutorial, I have brought together three of my Power BI tutorials on profit prediction. 


You can calculate what the future might look like for your results by using a combination of DAX calculations and modeling techniques in Power BI.  


The first tutorial that I’ll share with you demonstrates how you can “shock” some variables using some DAX logic and see what might happen in the future. 


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In this tutorial, I adjust variables, such as the number of customers, the cost of goods sold, etc. I work out an analysis to predict the ultimate outcomes if any of these scenarios will happen.


Moreover, I have enabled some sensitivity analysis, which allows me to see the end results and the variations of results based on the sensitivity inputs that I’ll place inside my reports.


Check out the video below to see how I did this. 



The second tutorial that I have for you showcases the combination of time intelligence functions to create a prediction. I work through an example of how you can predict profitability for future time periods based on nuances.


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I dive into many DAX functions, including CALCULATE and some time intelligence functions, specifically DATEADD. With DAX combination and data modeling in Power BI, you can extract predictive results in an effective and dynamic way.

Here’s the video link for this tutorial.



Combining advanced DAX and data modeling techniques can bring you some seriously amazing insights. Power BI predictive analytics is not limited to profit prediction. You can also predict the purchasing behavior of your customers.


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In this next tutorial, I walk through how you can predict when your customers are likely to make their next purchase. Click the link below to watch my demonstration.



Although there’s a bit of work here, this is something that’s quite attainable in Power BI from a DAX perspective. In this example, I discuss how you can use your data to predict customer behavior. Ultimately this can impact your sales and finances positively.


Key Takeaway

This blog post showcases some of the analytical work that I enjoy doing the most, which is working on predictions in Power BI using DAX functions. I’m always captivated by understanding what could happen in the future from a business perspective.

With DAX combination and data modeling, you can predict future profitability as well as customer behavior. What’s great about these techniques is that you can use them in many ways. You can try to predict future demand, transactions, revenues, cost, etc.


Most, if not all, reports are based on historical data. So, if you can build your models correctly and run analysis out of your financial information to predict future results, you can generate significant value for your company and stakeholders.

The real quality insights are achieved by combining all these techniques together. That’s what I want you to achieve in your analysis using the power of DAX formulas and data model inside Power BI. I hope you can implement this one successfully in your own work environment.







***** Related Links *****
Effective Techniques for Profit Prediction Using Power BI
Forecasting Technique: Exploring Forecasting Logic In Power BI Models
Sensitivity Analysis Examples In Power BI

***** Related Course Modules*****
Scenario Analysis Deep Dive
Time Intelligence Calculations
Scenario Analysis Series

***** Related Support Forum Posts *****
Predicting Future Years
DAX Total Issue Within Predicting Results Showcase - How I Fixed It
Cumulative Forecasting
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