William Chu

Based on user research, we knew customers were looking to incorporate more plant-based meals into their lives beyond our existing dinner plans. We aimed to fulfill this need by extending our service to upsell breakfast and lunch options, to increase lifetime value, average order value, and margin on each box shipped—all while not cannibalizing on our core product.

The challenge also came in the form of a more complicated data model, where users no longer had a weekly plan with three set meals but had the flexibility to add, remove, or swap any number of meals. This resulted in an exponentially larger number of permutations for each customer, with new business rules, complexity in forecasting demand for ingredients, and a new mental model for users as to how subscriptions worked.

As part of a lean team, I led product design, usability testing, and QA in addition to the core responsibilities of managing a roadmap and backlog, working with developers, and measuring KPIs. We were able to hit our deadline for shipping before New Year's Day. New Year's Day is when we typically saw the most significant spike in customer acquisition due to resolutions, and we achieved our goal of at least 10% adoption in the first week of launch.

After Avis's acquisition, I managed integrations and opportunities for synergy. In addition to back-office requirements for accounting and allocating expenses, we tested new products that leveraged the benefits of Avis and Zipcar.

This was a flexible service offered at Avis, where users didn't need to book specific cars and could scan and go on arrival. We removed Zipcar's limitations: users can now book the last minute, pick any car, with no time constraints from future bookings. The downside was these were not easily accessible in your neighborhood through self-service.

Key questions we had to answer:
• What were the use cases, if any, for a user to travel further to Avis?
• What price would they be willing to pay?

We created a Wizard of Oz experiment in two weeks and shipped immediately. This generated over 500 trips and $200,000 in two months, indicating the potential for product-market fit. Each week, I A/B tested different value propositions and pricing while learning where Avis needed help to operationalize and scale physically. This set us up for success as we worked in parallel to update our booking platform and telematics hardware for the official launch. Today this service has evolved even further (see link).

As a pioneer in the car-sharing industry, Zipcar has an easy-and-simple customer experience. Behind the scenes, this is supported by a sophisticated fleet and location management operations. As a consultant, my job was to create a strategy for revamping a suite of tools that had not been prioritized in years, even as business processes evolved.

Over five months, I used a solution-agnostic approach to understand processes, unmet needs, and measures of success for three key roles. Due to the robustness of the research, I could go beyond the original goal to produce value-added deliverables.

Original:
• Developed vision and roadmaps for three global operations roles.
• Produced UML diagrams and UX/UI designs for potential in-house solutions.

Value Added:
• Mapped out processes and nuances between markets, providing the ability to share best practices across markets and transparency into business operations.
• Identified opportunities for real-time dashboards and apps of data science for better business outcomes.
• Onboarded a new product manager that was hired to execute these roadmaps.
• Created tutorials and ran workshops to train the product team on applying jobs-to-be-done and the Outcome-Driven Innovation® methodology.

At the time, as direct-to-consumer brands burst into eCommerce, the experiences were cumbersome when it came to physical touchpoints. For footwear, customers had to take a chance without having the ability to try the product. When it came to exchanges, the policy was usually to have customers return and buy again or order multiple sizes at once.

In the case of a luxury brand like M.Gemi, not only did this mean extra shipping costs, but customers did not like paying more upfront or having two holds on their bank account at an average of $500. Our data showed that returns due to size or color issues had available exchanges of more than 50% of the time! We addressed this opportunity in two steps:

1) We devised a lean experiment to figure out if customers were indeed willing to exchange when given the opportunity. For two weeks, every customer looking to return was forced to speak to a representative. Over 75% were willing to make an exchange, saving the sale.

2) Based on the encouraging data, I led an 8-week initiative to build an automated workflow—mapping out the customer journey, defining new business rules for automated shipping and billing, and working with our devs to build the first component of our site in React.