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  • Writer's pictureVictor Lam

Data Analysis at Urban Outfitters - My Experience So Far

Updated: Apr 18

Having worked at Urban Outfitters for almost 2 years now, I have been so surprised by the amount of information we have. Before my time at URBN, I did not have the most experience working with big data. At my last workplace, Publicis Health Media, I would help with providing technical SEO recommendations for healthcare brands which allowed me to be in the mindset of providing fact-based recommendations to stakeholders, but was not able to tie back to historical performance years back.


In this blog post I wanted to go over some of the amazing things I've been able to accomplish using data at my workplace so far, and what I'm looking forward to next.


So far, I have been able to develop a ton of automated reports and a few interactive dashboards thanks to MicroStrategy and Tableau. In MicroStrategy, I am able to send reports to different parts of the company such as marketing, finance, merchandising, and even to senior leadership. When I first started using MicroStrategy, I hated it so much because of how old and finicky it was. Now that I've been able to make a few reports, I've somehow become the lead on our team for all things MicroStrategy. For Tableau, I was able to create a dashboard that would display the top products by designated marketing areas (DMA). It was such a good experience since it taught me more about creating calculations and making filters interactive with another through the action feature within Tableau. I also was able to revise a few of our other dashboards to include additional metrics and slices. One of the things I'm looking forward to is creating a demographics dashboard so it can help with the many ad hoc requests we get relating to demographic info for a specific product or category.


Outside of data visualization and BI tools, I have gotten much more familiar with writing out extensive SQL queries for specific ad hoc asks. I also have been able to do some deep dives on our promotional performance and loyalty programs which have allowed for changes in strategy for the business.


I have heard that using Python or R would be an easier method for data analysis, but have gotten just enough analysis and insights from SQL alone! Despite having grown my knowledge in SQL dramatically, I can't wait to add Python and R to my workflow to see its capabilities and opportunities to overlap different resources. I am in discussions with colleagues at work on ways we can implement these other languages into our analysis work.


If you're ever interested in learning more about the fashion industry, data, or life at URBN, hit me up - always happy to chat!

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