RevTech Fellows Spring 2021 Case Study 

Crave Retail               

Brandon Toushan

Queen’s University, 2021

“The opportunity to network, learn and gain hands-on experience building ML-models and putting them into production at a fast-growing startup was simply fantastic. The further exposure to VC was just the cherry on top. I’ve learned a tremendous amount from this fellowship and can wholeheartedly say that I’m all the richer for it.”

Key Objectives: 

  • Crave Retail is a fitting room experience and analytics platform that easily equips retailers to optimize shopper joy and conversion. 
  • Project: Research, develop and implement a potential improvement to Crave’s existing product recommendation engine

Results & End Impact 

  • 3 distinct recommendation engine types (association-based, content- based & user-based) were researched and evaluated.
  • A working AI-powered (Apiori-based) recommendation engine was built, tested, and is set to be deployed in-app immediately
  • The pilot engine has the potential to improve Crave Retail’s impact on fitting room conversion rates and will serve as a baseline for all future AI/ML enhancements to Crave’s app.
  • The new product recommendation engine provides a great way for Crave to capitalize on its extensive cache of POS data and has the potential to improve the platform and its value proposition.
  • The research into various recommendation engines and other potential applications of analytics/ai should also be quite helpful for any future AI feature developments/platform enhancements.
  • In terms of ballpark value, assuming a mid-level DS contractor rate, my work with Crave ended up saving the company roughly ~$7200 in total (12 weeks, 10 hours per week, $75/hour).

Key Learnings 

  • That the start-up environment is where I really fit in best and want to be long-term, whether it’s as an employee, founder or VC
  • That daily standups (and weekly kickoff standups) can be quick, to-the-point and fantastic (and can be kept under 20 minutes!)
  • That I have still have a ton to learn about retail, SAAS and building ML models at scale (easier said than done!)

Paying it Forward & Next Steps 

  • I plan to take what I’ve learned about retail, ML engineering and startup analytics to my next role as a Consultant in Deloitte’s AI group.
  • I’m extremely grateful for the opportunity to learn from and work with such an amazingly talented group of people. I intend to pay this forward by continuing to provide mentoring, interview/resume prep and connections to all those in my network who can benefit.