Analytics
Data Prepping
1. Mid([Pivot1 Names],11,4)
2. REGEXP_EXTRACT([Pivot1 Names],'(\d{2}\.\d{4})’)
3. RIGHT ([Pivot1 Names],2)
4. IF RIGHT ([Pivot1 Names],2)='3)' THEN 'Associates' ELSEIF RIGHT ([Pivot1 Names],2)='5)’ THEN 'Bachelors’ ELSEIF RIGHT ([Pivot1 Names],2)='7)’ THEN 'Masters' ELSE 'Other’ END
2. REGEXP_EXTRACT([Pivot1 Names],'(\d{2}\.\d{4})’)
3. RIGHT ([Pivot1 Names],2)
4. IF RIGHT ([Pivot1 Names],2)='3)' THEN 'Associates' ELSEIF RIGHT ([Pivot1 Names],2)='5)’ THEN 'Bachelors’ ELSEIF RIGHT ([Pivot1 Names],2)='7)’ THEN 'Masters' ELSE 'Other’ END
Ultimately, resulting in 5 clean output files for visualization.
Tableau Data Visualization
The purpose of these visualizations is for exploration of data associated with graphic communication higher-education programs. Using US Department of Education data from 2010-2019, users can explore all US higher-ed (under Title IX) by CIP code, GCEA Region, and degree granted. Additional stories focused on funding and number of graduates by program. A powerpoint slide-deck is shown as the final slide.
Click on the images to open in Tableau Public.
Tableau Data Visualization
The second image offers an interactive treemap showing average prices by neighborhood (parameter) around Muinich. This dashboard includes a heat map, calculated as the distance (in km) from the Oktoberfest fairgrounds for different Airbnbs. This multi-dimensional map is also interactive by bedrooms, bathrooms, price, etc. The hue shift to a brighter red means the property is closer to the fairgrounds. The larger the circle, the more expensive the property is per night.
This third image includes average pricing by bedrooms and square footage (using custom bins). The bottom line graph shows pricing trends broken out by square footage. A closer examination shows that larger properties increased in average price over the pandemic while smaller (and more abundant properties) decreased in average price over the pandemic. To the right, a forecast was developed, based on the square foot bins.
Evaluatiing Analytics
Please note that these graphs and tables are generated within Digital Media Pro.
In the simulation, students spend their marketing budget by selecting how much, which media, and when they spend money on various types of digital media, all while staying within budget. They purchase market intelligence (market research) and then decide which part of the purchasing funnel they want to emphasize to maximize their OPI (overall performance index). Media timing is also a critical consideration to leverage different regional events. After submitting their purchase decisions, the simulation provides a "score".
Teams compete against other teams over a "five year" period to analyze these data and convert insights into action, with the objective being to maximize marketing return on investment.
Teams compete against other teams over a "five year" period to analyze these data and convert insights into action, with the objective being to maximize marketing return on investment.
