Experimentation is a valuable activity in a variety of functions. A product team should be constantly experimenting with features to see which variant leads to the most engagement, sales, or some target metric. But what about on a data analytics team? Mel Restori is the former Director of Analytics & Analytics Engineering at Trove, a resale platform for some of the world’s top brands. Mel’s career has always been in tech, but she started as an industrial engineer. She discusses her path to leading the analytics team at Trove, how to build a culture of experimentation, and how to navigate a career in analytics.
Experimentation to drive decision making
While Mel was at Trove, her team led the charge on implementing an experimentation framework, including incorporating a third party A/B testing tool. People at Trove were hungry for data analysis and wanted to know how to make better product decisions using a new testing framework. The end result will be to give all PMs and analysts an easy way to run thoughtful experiments. In addition to developing this testing framework, Mel was also a big believer in how people internally would learn about testing. At the end of the day, PMs and analysts should be able to make better decisions.
Testing what works on a brand’s website when it comes to reselling is quite different from a traditional e-commerce site. “Recommerce” provides one customer with an experience for trading in their existing goods and another customer with buying a resale item. The product team may not have the luxury of seeing what competitors are doing since there might only be one color or one size of a product.
Pursuing an analytics degree at university and pivoting into data analytics
Mel talked at length about how people like herself have stumbled into data analytics. For Mel, she thought this was the only way to do it since there wasn’t a data analytics “major” when she was in college. I completely agree with her on this point since the only data analytics experience I received was on the job.
Now you can major in specific areas of data analytics like data science and machine learning. If you major in data analytics, your career path is a little more straightforward. One thing Mel pointed out is that datasets are never as clean as what you see in school. When you’re working with real world data, most of the time you’re just cleansing data to get the data in a usable format.
Mel gave some great advice for those who want to learn a specific skill or tool like Python or Excel. Look for a larger company that has an entire analytics department built out where you can specialize. You probably won’t have to do as much data cleaning compared to the data you might come across at a startup. A benefit of being at a startup, according to Mel, is that you can figure out where you would thrive but wearing multiple hats.
Other Podcasts & Blog Posts
No other podcasts mentioned in this episode!