Power BI: Correlation Analysis of Sales and Marketing Data to Identify Most Effective Method

The video discusses how to use Power BI to analyze sales and marketing data using correlation analysis. The purpose is to identify the most effective marketing method to enhance sales. The video begins by explaining that the company is currently using electronic media, print media, and social media for marketing. To get started, the data is imported into Power BI using the "get data" feature, and sheet one is selected in the navigator window.
Next, a title is added to the dashboard using a text box, and a scatter chart is added to analyze the relationship between electronic media marketing and sales. The sales field is added to the Y axis, the electronic media field is added to the X axis, and the month field is added to the values area. A copy of the scatter chart is created, and the print media field is replaced with the electronic media field in the X axis. Another copy of the scatter chart is created, and the social media field is used in place of the print media field in the X axis.
After creating the scatter charts, the video explains how to use the quick measure button in the home tab to calculate the correlation coefficient between electronic media marketing and sales. The month field is added to the category, sales field is added to Measure Y, and electronic media field is added to Measure X. This provides the correlation between electronic media marketing and sales data, which is added to the dashboard and changed to a gauge visual.
The video explains how to create a new measure to correct the maximum value for the gauge visual, which should be one instead of 1.4. The same process is used to calculate the correlation between print media and sales and social media marketing and sales. Each correlation measure is added to the dashboard and changed to a gauge visual, with the max field added to the maximum value area.
Finally, the video concludes by discussing the results of the analysis. The scatter charts show that social media marketing is the most effective method of marketing, with the highest correlation between social media marketing and sales. A trend line can be added to the scatter chart to forecast sales from social media marketing, which shows that sales can be increased by spending more on social media marketing.
Overall, the video provides a step-by-step guide for using Power BI to analyze sales and marketing data, with a focus on correlation analysis to identify the most effective marketing method.
virtual-school...
Source Data: docs.google.co...
#PowerBI
#DataAnalysis
#MarketingAnalytics
#SalesEnhancement
#CorrelationAnalysis
#SocialMediaMarketing
#PrintMediaMarketing
#ElectronicMediaMarketing
#TrendAnalysis
#DataVisualization
#BusinessIntelligence
#DashboardDesign
#DataInsights
#MarketingStrategy
#MarketingTrends
#DigitalMarketing.

[@viewview6687]

I love how simply you explain to each example, whoever you are, thank you for your contents ❤

[@virtual_school]

Thanks for appreciation sir.

[@duniacollymore1151]

this just solve a huge problem i had, thank you !

[@virtual_school]

Good to know that. Thanks

[@danakresova9880]

Amazing, thank you!

[@Mohamed.GadAllah]

Thanks a lot.

[@ivanoinserra104]

Very nice! But what is the value for each media? Clicks, page views, calls...just curious

[@virtual_school]

Very low. Educational videos dont get as much attention as entertainment stuff.

[@lebogangtau1365]

I am getting an extremely high correlation coefficient even though my scatter plot seems quite scattered. Is this method accurate?

[@virtual_school]

Well, I used a builtin function, so unless microsoft got it wrong, it should be correct.

[@pratiknandanwar4831]

WHY TO ADD CATEGORY

[@virtual_school]

If you dont add category, you are just comparing two values (i.e. sum of sales, and sum of Electronic Media) which is worthless. When you add category, you have two lines to compare which can tell the correlation. The correlation between 1,2,3,4,5 and 2,4,6,8,10 is 1 - a perfect positive correlation. But what is the correlation between 15 and 30 which is their SUM? You cannot even determine the correlation. Thats why we add category. 15 and 30 will be without category.

[@basem9187]

Can you advise if I have category , sales , huge number of loyalty membership how to visualize sales per cat. according to loyalty membership

[@virtual_school]

Sure. Please share a sample data and the visual/chart which you want to create using that data.