Breaking out of your comfort zone is hard. Especially when it comes to the clothes in your wardrobe. That’s how Armoire Style, a women’s rental clothing subscription service, helps women expand their safe zones by trying styles they’d never pick on their own. Don’t think yellow is your color? How about olive green?
Well, the team at Armoire recently overcame a pivotal hurdle with their business: bringing the same data to all tables within the business. With data-rich products, they understood the value of collecting data to feed machine learning models to learn about their customers. The problem they were facing was their Periscope business intelligence platform wasn’t flexible enough to empower forward-facing analytics in a governed way. As you can imagine, trying to understand customer tenure across subscriptions, upgrades, and plans can make for some gnarly SQL queries.
Enter Data Clymer & Looker. A perfect combination of expertise and strategy. Like slipping on the exact shoe that goes with your dress and makes you say to yourself: “Dang. I look gooooood!”
As a boutique consultancy firm focused on data culture transformation, they understand how governed data democratization can enable self-service analytics which ultimately changes the way an organization thinks and talks about data. That’s why Looker was a great choice for the Armoire team. They had data, dashboards, and a lean analytics team, they just needed to wrangle it all into one system that then could be rolled out throughout the entirety of their business. By migrating away from inflexible and complex SQL queries to clearly defined Explores in Looker, the Armoire team has a business intelligence platform utilized in all departments. As summed up by their CTO, Tristan Rees, “we wanted to invest in a tool which would allow us to extract tables and enable business users to explore data even without dashboards or SQL knowledge.”
Thanks to the expertise of Data Clymer, the Armoire team now has optimal data models where they now have visibility into real-time item performance. Gone with the days of a rear-facing analytics mindset and enter a forward-thinking data culture. “We now have people in our warehouse, merchandising, and operations teams, some who have never touched data, where our conversations are less of ‘hey can you build me this report’ to ‘hey, I’m running this report and it’s not performing. Can you help me?’”