1/ #smarketplace i.e. the intelligent next generation of exchange platforms
New post I wrote in @TechCrunch: techcrunch.com/2018/10/23/sma…
New post I wrote in @TechCrunch: techcrunch.com/2018/10/23/sma…
2/ Marketplace businesses are intrinsically linked to the technologies that enable them. e.g. there would be no Uber without location services and ubiquitous smartphones
3/ As enabling technologies have evolved, marketplaces have grown to facilitate increasingly complex exchanges in new environments and industries. e.g. from Craigslist text listings to social network proofed + photo documented crafts Etsy.
4/ Until this point, marketplaces relied on small numbers of variables to inform matches and transactions. e.g. I tell Airbnb I would like to rent an apartment in Paris for a week in July and let it know my budget, and it shows me the options.
5/ SO what does machine learning and dense edge computing enable for marketplaces this go around?
6/ To understand significantly more complex demands, and thereby facilitate transactions currently impossible using the present, human expressed models. i.e. machine understanding of richer data, and by extension multivariate needs.
7/ The next generation is intelligent: i.e. #smarketplace
8/ SO, a #smarketplace directly links complex demand to supply by understanding multi-dimensional needs.
9/ e.g. Take a common manufacturing process such as laser cutting.
10/ Historically, this has been a non-trivial quoting process which can require expert CAD engineers, and a lot of back and forth between customer and machinist. This is understandable for an offline over-the-counter transaction.
11/ This is where machine learning could enter the conversation. With a rich history of orders and their resulting price, you could train a neural net that would ingest the 3D file, spit back out an accurate cost and facilitate exchange digitally.
12/ (a process that would have taken a human CAD technician hours + numerous back & forths!)
13/ HOWEVER, building a marketplace is not without challenges, notably achieving liquidity. Starting a (non-smart) marketplace business is difficult if you need to begin the supply and/or demand from a “cold start.”
14/ A #smarketplace is even more complex to set up. In addition to needing demand and supply-side engagement; the matching algorithm needs to be trained.
15/ e.g. Uber Pool could not exist without Uber providing the training data: It needed the pickup and routes data from the solo rides to optimize the communal rides.
16/ Therefore, a #smarketplace faces a “double cold start.” Not only do they have to populate supply and demand, but they also have to train their matching algos before they can be effective.
17/ OR, as platforms scale, concomitantly increasing the volume of training data, does the platform choose to eat the prediction errors (and consequently their profit margin), the better to train the model? If so, when does “break even” happen? It is worth it?
18/ In the short term, this all prompts the question: Is a horizontal #smarketplace feasible, given the quantity and quality of training data required? If so, which categories could currently support it?
19/ ALSO, builders of a #smarketplace need to ask themselves: Is AI a core competency “baked into” the platform, or is AI a “partner” technology that is brought in? If the latter, what does this imply for defensibility?
20/ #smarketplace may first emerge in analogue industries producing complex, high-dimensional data that humans are not good at describing: industry (customer parts), content (stock footage), healthcare (MRI/CT) and finance (commodities trading).
21/ The current crop of marketplaces are likely to layer AI on top of their current offerings to reduce friction and increase efficiencies, but the truly smart marketplaces will be those reliant on AI to broker previously impossible transactions. END
ps: if building a #smarketplace in EU, please reach out :-)
