Matthew Brandt didn’t know analytics was a potential career option until he started managing websites with Google Analytics. Matthew is Canadian, spent most of his life in Switzerland, and went to high school in Japan. He attended the EHL hospitality school at Lausanne, and was part of the 70% who left the hospitality field after graduation to start his career in analytics. From data engineering to data architecture to reverse ETL, Matthew has done and seen it all. To merge his love for hospitality and technology, Matthew founded a food tech startup based in Zurich. In this episode, you’ll hear about Matthew’s wide-ranging experience including livestreaming about analytics on Twitch, working at a fintech SaaS company, and co-founding a food-sharing startup.
Predicting customer churn at a fintech SaaS company
Before Matthew entered the world of livestreaming, he had a “normal” job working for a SaaS company. The company provides account software for small businesses in Switzerland. Swiss tax law is very complicated so this company helps businesses with getting all this back office tax stuff figured out. Matthew was originally hired as a marketing analyst. Out of 40 people in marketing, Matthew was the only one actively working with data. Originally, he was cleaning up data, auditing data, figuring out business workflows, and understanding relationships between entities. A jack of all trades.
The company wanted to reduce churn but didn’t have the technical infrastructure to get there. Matthew introduced Salesforce and the ETL model to the company to create a clean set of tables and schemas. He ended up using machine learning to produce different outcomes so that the company knew which parts of their operation to fine tune and optimize. The model would help them identify which customers are most likely to churn. For instance, customer who hadn’t logged in in a long time were most likely going to churn.
This was an interesting behavioral exercise since the customer already made the psychological decision to cancel. But we were able to identify these customers before they made that decision.
Keeping a foot in the analytics world with conferences and livestreaming
While Matthew’s full-time job is working on a food tech startup (more on this later), he still keeps a foot in the analytics world. He runs AnalyticsCamp, for instance. AnalyticsCamp is an “unconference” focused on data analytics, data visualization, business intelligence, and UX research. If you are based in Switzerland, this might be the perfect conference for you to attend if you’re in the data analytics field.
What’s more interesting are Matthew’s forays into the livestreaming space. I’ve never met someone who is providing education and creating a community around data analytics through Twitch. During the pandemic, Twitch grew by 82% in terms of hours watched.
Like many people stuck at home, Matthew went onto Twitch and started watching music, science, and technology streams. He discovered that many people were developers and many people were doing live coding streams. Something about teaching analytics on Twitch interested Matthew, so he thought he’d give it a shot. He discovered it had to be edu-tainment. I jumped on one of Matthew’s livestreams and it’s just as Matthew said: it’s a mix of learning and entertainment. I think this might be the first time I’ve mentioned a Twitch livestream as a way to learn about analytics and tech:
I asked Matthew how he figured out what type of content to stream on Twitch and what the feedback has been. He spends a few hours researching and planning out his content for every stream. One of his first streams was investigating how Uber fares have changed in NYC. You can get the Uber fare dataset straight from Kaggle. The stream consists of Matthew going through the steps to do the analysis. He shows how you can put the data into a Postgres database, creating a Docker container, and ultimately analyzing and visualizing the data.
So far Matthew has found a niche for himself. Initially there wasn’t a lot of chat in his livestreams. Over time, the stream would grow to 30-40 viewers in real-time and he would get derailed by comments in the chat. He says he is one of 50 or so livestreamers who focus on streaming for the data community.
Building a real-time analytics platform for Twitch livestreamers
Matthew started meeting more livestreamers teaching different technical subjects in Twitch communities. He was invited to join a team of developers who livestream on Twitch called The Claw. This team consists of 22 livestreamers and their tag-line is awesome:
Matthew started talking to his fellow livestreamers about an idea he had to utilize his analytics background. Most livestreamers he talked to didn’t really know what goes on during their livestream besides the normal gifting, comments, and follows on Twitch. What if streamers could get real-time analytics about their streams?
Matthew approached this project much like a startup. He got feedback from livestreamers, incrementally worked on features, and built a waitlist of 70-80 people who were eager to try it out. Harkening back to Twitch’s original name (Justin.tv), Matthew called his project Justin Numbers.
Real-time analytics is quite a popular subject these days because of the big data coming from all our devices and all the apps we use. All the big cloud providers like Google Cloud and AWS have data streamling and analytics solutions like Google Cloud’s Data Dataflow. I wouldn’t be surprised if Twitch comes out with their own real-time analytics given how prevalent these streaming tools are.
Making home-cooked meals accessible to everyone in Zurich
When Matthew started talking about his current startup, I was expecting it to be something related to analytics. Instead, the startup he’s working on is a bit out of left field. The idea behind Cook Eat‘s mission is to bring home-cooked meals to the masses and make it easier for people to eat lunch together. The meals are not prepared by chefs at restaurants but rather by individuals. Think Airbnb for meals.
This idea has been tried before and there are a lot of operational and quality issues associated with this model. Matthew and his co-founder think they can succeed by starting with just the market in Zurich first. They manually vet each cook to verify their skills and quality of food the cook creates.
Matthew met Ela, his co-founder, 9 years ago. They were both working at the same company and batted around this idea for a few years. 4 years ago, they decided to invest some money in the idea and built an MVP. Unfortunately, there were no significant developments since they didn’t do any marketing. Last year, they quit their jobs and raised some money from 4 angel investors to work on Cook Eat full-time. At the time of the recording, they were doing an impact study by working with the Zurich housing authority to see what impact food sharing has on the environment.
We chatted about the life of a startup founder and Matthew’s experience will probably resonate with you if you’re a startup founder. You work on 50 different things at any point in time and are being pushed outside your comfort zone. Sometimes Matthew doesn’t feel like he gets to work on analytics-related projects so that’s why he continues to do livestreaming and hosting the AnalyticsCamp unconference.
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