Create an Online Car Dealership arm for Amazon

Expert Rating


Feature prioritization
Metrics definition
User research




While the majority of car companies and dealers use the web primarily as a tool to drive consumers to showrooms, however, unprecedented spikes in online sales as a result of consumers being forced indoors by the global coronavirus pandemic has presented the car sales and dealership industry an opportunity for a strategic shift to meet the existing market. Having an already established presence in the e-commerce space, Amazon, the world’s biggest online retailer has developed an ambition to start selling cars online directly to consumers including providing finance and payment option. This service will deliver a world-class customer experience by removing all the friction for consumers by replacing the hassle of haggling, with transparent and upfront pricing; creating a fast and easy online buying experience; and saving consumers hours of waiting at the dealership with home delivery. This task was my portfolio delivery for a Product Management Virtual Experience Program I had only just recently concluded.

I was tasked with developing this arm of the global retail company within 12 months (4 weeks programme) to meet the hypothetical demands of the existing sizeable market of online shoppers that require a trusted and convenient means of purchasing their favourite cars online.



Resources (if any)

Figma, Miro, Ms Word

I began the task by broadly defining the target market for the product to ascertain our likely customers/users. This involved activities like drawing up assumptions such as “customers want to buy cars conveniently”; “customers want a wide range of car financing and payment options when buying cars”. The broad definition of the target market was closely followed by interviews of potential users from the target market intended to clarify assumptions and further narrow potential users and ensure the product is developed for the right users. Potential users’ insights were collected using interview questions such as “Do you own a car? How did you go about the purchase?”: “How did you buy your last car?”. With the findings and learned insights including the user’s common experiences, difficulties and car buying process, I started up on crafting a minimum viable product (MVP) to collect more validated learnings from the customers, and this included deciding on what features to have and user stories to incorporate. Features with the most value were derived and prioritized using the MSCW framework while the level of effort for their development was decided on using the T-shirt framework. In the same vein, I structured how success will be defined as well as how to measure it. Furthermore, I defined the objectives and key results for each of the prioritized MVP features and drafted their success metrics (Primary, Secondary and Guardrail Metrics).

With the outcome of the above activities, I sketched out a wireframe, a low fidelity representation of the product that will help guide the prototyping effort. These outcomes have also helped inform the structure of the shared portfolio document below.

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