Web commponent currently living at https://breadcrumbs.ninja. Or check out the repository.
Breadcrumbs is a shopping companion that logs every product you see when shopping online so that you can later find them in a single place. Breadcrumbs works in the backgroung of your browser to seamlessly collect product information so that you need to worry about it, and offers a web-based product viewing dashboard with all the features you need to easily find all the alternatives you need and click to finalize your purchase. Breadcrumbs is online shopping, without the open tabs.
Starting from the total addressable market of all online shoppers (200 million strong in the US alone according to eMarketer), our target segment is 21-to-45 female professionals that window-shop online for an array of non-standardized goods like clothing, accessories or home items.
- Our product delivers more value the less standardized product options are, the more alternatives shoppers check before making a decision, and the less price-based such decision is. BREADCRUMBS IS NOT A PRICE COMPARISON TOOL. Neither is it a product discovery or recommendation tool, although it could well evolve into any of the above.
- Our target segment has been found to very actively discussing their online shopping activities on social media, which makes them easier to understand and reach with our value proposition in a cost-effective manner than the average online shopper Our target customers will use Breadcrumbs to build their product shortlists for different categories as they desktop-shop around different sites, possibly for different products. By definition, these customers lead busy lives and multitask a lot.
Our value proposition, and the features that would enable it, is actually threefold:
-
To online shoppers (end users), we offer a one-stop repository with all their online shopping research history. Key features are:
- Seamlessly logging all products viewed on our supported sites
- Compiling all those products into the user's personal dashboard, sorted by automatic categories
- Offering support features like filtering and search so that users can zoom in and out to their desired breadth of choice
-
To our supported online stores, we send them recurring traffic to their deepest-level product pages, in the form of potential buyers that have already shown interest in the product and are now likely further down the purchase decision funnel. This value prop is essentially what those stores are seeking when they engage in retargeted advertising tactics, only here it's customers themselves that are opting in to be retargeted. Key features are:
- All those that allow us to provide our core service to end users
- Implementation of our supported sites' affiliate program APIs
-
To 3rd-party brands or non-supported online stores, we offer them a platform t advertise their products to an audience as finely targeted as could ever be: people that have browsed similar or related products to those they offer. Again, this value prop is what those players are seeking when they engage in retargeted advertising tactics. Key features are:
- All those that allow us to provide our core service to end users
- Smart ad-serving that enables relevant ads on our users' dashboard pages
- Server: Meteor
- Database: MongoDB
- Scripting: Coffeescript
- Styles: SCSS
- Testing: Velocity + Mocha + Chai
- Kevin Chen - Developer
- Edward Hu - Developer
- Ignacio (Nacho) Naveiras - Product Manager
- Penny Wang - Design and Content Manager
- README: this
- A folder with your mid-course and final presentation deck
- A folder with files from LPC (can easily export files - see image below)
- Final BMC and any other BMC that demonstrates how your business developed over the course of the class
- Interview notes (click “Export your team Discovery Narratives”)
- A folder with videos - final demo video and the mid-course pitch video
- GitHub organized with all code and components necessary to run project: this
##Troubleshooting
- Contact Kevin Chen or Edward Hu at [email protected] or [email protected] for technical assistance