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Kenja Needfinding
We believe a strong tailwind exists in better AI-enabled search for discovering and recommending new products. However, we're still debating between two GTM motions.
In sum, our market is all of online shopping, which has an $850 billion TAM (as of 2022) and is growing at a CAGR of 15%. There are over 265 million digital buyers worldwide, with over 26.5 million e-commerce websites.
In terms of our approach to tackling the market, weβre a big fan of βdoing things that donβt scaleβ and tackling specific niches of products first before expanding into a more general shopping experience. Regarding the types of products, we believe that high-margin products that people buy online β not just search up and buy in-store β are the right products to focus on and perfect discovery for. We also plan to approach the market without ignoring other broader tailwinds, such as the rise of influence marketing and social shopping and the ability to integrate such media into our recommendations and overall user experience. We believe that through this focused approach, we can build a product that a certain community of folks love and easily use that traction to expand into other product areas.
From a B2C angle, we would try to build out the "Perplexity for shopping." This would require a more generalist algorithm capable of scraping the web and would mean a business model similar to sites like Wirecutter, where we become affiliates. The idea here would be economies of scaleβonce you have enough traffic and enough conversions for people to buy your products, then the amount of money you make selling a product will be more than the cost of both customer acquisition and a single search query.
The other angle is going directly to businesses, namely e-commerce businesses, who could hook up their database to our algorithm and get, in return, an API endpoint they could call when users search. This would largely keep our "find and filter" algorithm intact, while our business model would be on a per-API-call basis rather than based on users loving the product. The sale would help increase conversion and create a shopping experience that is more personalized.
The next steps are to take some time during finals week and clearly define our initial customer profile. Then we can all take time to reach out to at least 10-15 candidates, getting as many interviews as possible and then trying to sign on design partners to try out our platform.
For other information, check out our team's Google Drive. For a daily stream of thoughts, check this document.
- OKRs and KPIs
- Team Coding Standards
- Real Customer Profile
- Launch Week Recap
- Unusual Academy Pitch Event
- Final Reflection
- 1/23 - Jay Borenstein
- 1/26 - Chris Oh
- 1/30 - Glenn Reid
- 1/30 - Adam Heher
- 1/31 - Samantha Phillips
- 1/31 - Chris Tsakalakis
- 2/2 - MZ Zaveri and Kasey Zhang
- 1/25 - Introduction
- 2/1 - Proposing a Product
- 2/13 - OKRs and KPIs
- 2/27 - Catching up for Last Weeks of Winter
- 3/5 - Demo + Discussion
- 4/2 - New Quarter
- Strategy for Search for Shopping
- Ideal Customer Profile Brainstorming
- Outreach Messaging
- Tracking Outreach
- Discovery Call Outline
- Feedback from Unusual on Slides
- Meeting with Seena from Nike
- Meeting with Mike from Launch
- Meeting with Heather from Walmart
- Meeting with Sandy from Walmart
- Demo Call Outline
- Prototype v0: A New Shopping Experience
- Prototype v1: Updated Bookworm for Demo Day
- Prototype v3: SUPost Battle for Software Fair