I haven’t published in this specific format in a while and thought I’d unleash some ideas that have been circulating in my head into the wild.

Aashay Sanghvi
Feb 13 · 4 min read

APIs Outside the City

Rural areas cover 97% of the nation’s land. Yet, there have been few technology-driven startups and services that expose us to the great outdoors. Benchmark led the Series A for a camping marketplace called Hipcamp, and Getaway is a popular hospitality solution for people looking to escape the bustling nature of cities, but these are rare examples.

I’m interested in data tools that expose information about rural areas and nature, thus paving the way for more online-to-offline products, allowing for better planning and conservation efforts, and developing insights into granular natural resources data for things like forecasting. For example, what would Remix look like outside the context of cities?

AngelList Syndicates for X

I’ve thought quite a bit about alternative assets and what democratized investment exposure to them means. Sometimes, these platforms that offer investment opportunities suffer from adverse selection problems. One model proposed as a solution is the AngelList syndicate system. There’s a bet that a few individuals (syndicate leads) have information asymmetry about startups, but not enough capital to adequately deploy. They leverage the crowd and get compensated by capturing more upside, gaining significant leverage.

https://angel.co/syndicates/for-leads

Furthermore, I’m learning about new investment assets or categories every day — from classic cars to cattle. Maybe the syndication model can augment investment processes in these new categories.

Data Driven Furniture Store

In the past one to two years, there’s been a handful of businesses aiming to reduce the friction involved with getting a product in physical retail (Bulletin, Fourpost, Re:Store, Neighborhood Goods, b8ta, etc.) Part of these companies’ theses circulates around a data advantage. Hopefully, they can set up a data infrastructure superior to traditional retail, creating a virtuous loop:

  • Brands can easily sign up to display their products IRL through a web interface. Maybe even through the Shopify API, which feeds the retail concept purchase data.
  • The retail experiences do a better job of collecting customer and demographic data from people who visit their stores.
  • For the next batch of inventory, the data can guide informed decisions such as, “Send 100 widgets to the Union Square location and 50 widgets to the Williamsburg location.”
  • Rinse and repeat.

Now, vertical solutions in this real estate category are starting to pop up as well. Camp is imagining the Kids Store 2.0.

www.camp.com

I’m interested to see if there’s an opportunity to think differently about the consumer furniture and home goods store given this context. If I were to pick one category Amazon hasn’t figured out as well as others in consumer, it would be big-box furniture given how fickle and picky consumers can be, as well as the logistics involved with delivery and assembly at national scale.

Despite an increased shift to e-commerce purchasing of big box items and proliferation of startups (Floyd, Burrow, Campaign, etc.), consumers still find value in touching and feeling these items in person. You could actually capture value on the fact that there’s tons of new home furnishings e-commerce companies that might just price compete with each other.

Furthermore (and this might be a stretch), once there’s a showroom experience with the proper data infrastructure in place, you might be able to get enough of an understanding of furnishings demand in local clusters and geographies to build out last-mile delivery and assembly networks.

Crowdsourced User Interfaces + Data Visualization

Given a data set, what’s the best way to visualize it and synthesize insights?

Kaggle and Numerai are interesting examples of instances where companies or entities incentivize the crowds to glean meaningful information from their data. These efforts are mostly focused on data science and machine learning. I’m curious to see how crowdsourcing could work in the context of user interfaces and data visualization. I could even imagine a “Kaggle for Business Intelligence,” where talented designers construct their own dashboard interpretations of a data set. The platform caters to subjectivity, but I believe there’s merit.

Insurance Distribution Platforms

Owning distribution and relationships is critical for financial services given how commoditized some of the actual products are. I’d like to see how insurance-specific distribution models could help agents, brokers, and carriers reach customers efficiently. Here are some thoughts:

  • An Intercom for insurance with apps that connect to an underwriting workflow
  • A hyper-personal landing page builder for prospective clients. I tweeted about something similar.
  • Clearbanc-style funding to target clients and revenue share off premiums

One concern to note about distribution platforms is insurance providers would eventually lose edge if they all flooded the ecosystem.

If you enjoyed these ideas and would like to chat, you can reach me at aashaysanghvi[at]gmail.com. 

Breakdowns

Breakdowns is a place for perspectives and ideas surrounding the technology markets and opportunity areas. If you are an investor and would like to work together, please reach out to aashaysanghvi[at]gmail.com to discuss.

Aashay Sanghvi

Written by

Learning / Writing / Investing

Breakdowns

Breakdowns is a place for perspectives and ideas surrounding the technology markets and opportunity areas. If you are an investor and would like to work together, please reach out to aashaysanghvi[at]gmail.com to discuss.

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