Network Effects: When Value Comes from Others Joining in
Network Effects: When Value Comes from Others Joining in
... and Data Boutique Partnership Program launch
About Data Boutique
Data Boutique is a web-scraped data marketplace. If you’re looking for web data, there is a high chance someone is already collecting it. Data Boutique makes it easier to buy web data from them.
Network Effects: When Value Comes from Others Joining in
TL;DR: Network effects in Data Boutique favor buyers who refer buyers, and sellers who refer sellers, because of mutual self-interest. Plus: The launch of our Partnership Program: Long-term value sharing with our network.
Buying and selling web scraped data is a fragmented business, and this fragmentation causes hidden costs.
Whether you buy it or hire someone to collect it, it’s a one-on-one relationship between buyer and seller: Finding each other, selecting, testing, validating, and negotiating the transaction takes time.
You must go through the same process when you need it again. Every. Single. Time.
This is not the case for data marketplaces (heavily managed marketplaces1, to be precise): Once on the platform, every time you buy data, most of these costs disappear. From the second purchase onwards, transaction costs are lower: Data streams with the same format, structure, delivery method, quality controls, and payment methods.
But there is more: Every single person who joins the platform adds value to all other actors.
More buyers is good for buyers (cost-wise)
What is obvious is that more buyers is a good thing for sellers.
What is less obvious is that more buyers is also a good thing for buyers. Let’s see why.
Data selling (with pre-scraped datasets) is a fixed-cost activity. Once the collection costs are covered, every new sale simply adds margins.
The more sales units, the more margin a seller has to lower prices and attract more buyers.
In practice: If a data product is purchased by just one client, the seller is forced to keep higher prices to cover costs. But this is less than ideal, as the convenience vs. in-house web scraping would be smaller.
When more clients buy the same product, scale economies will give the seller room to lower unit prices and attract more sales. Competition, in fact, is not seller vs. seller, but seller vs. in-house scraping.
Although buyers may be skeptical about suggesting to others where to source data, in the end it’s a self-interest mechanism: Data Buyers have a direct incentive to refer users to the platform as it lowers their future costs.
Let’s say I know Peter. Peter is using web data for image recognition in fashion e-commerce. I am using web data for market intel in fashion. I want Peter to join the marketplace, as in the long run costs will go down for me.
More sellers is good for sellers (density)
Let’s look at the other side: Sellers.
Web scraping is highly competitive. Try posting a web-scraping job on Upwork and see how many apply, you’d be surprised. So, it’s natural for sellers to be reluctant when other sellers join a marketplace, as they are afraid of competition.
In a talent marketplace (Upwork, or Fiverr), this is true: The more people doing web scraping join in, the higher the competition. It’s a losing price-squeezing game.
But the economics of data marketplaces are different: If sellers offer different data products - and we as market makers need to make sure this is incentivized - more sellers means more products. It’s a win-win, here’s why:
Data products, unlike others, are not mutually exclusive: They are mutually reinforcing. There is a higher chance buyers searching for dataset A, will purchase dataset A and B, if B is available and adjacent to A, as it completes the information set.
An example: Let’s say I’m a data seller offering H&M data, and I know Tina. Tina is scraping ZARA website. It is my interest to have Tina with me on the platform offering ZARA, because this raises my sales of H&M if we’re together. The sum is greater than the parts.
Now, if Matthew joins, offering MANGO or BERSHKA, we’re getting closer to building a cluster of data for fast-fashion brands, that we alone would have trouble building, given the cost of collecting all of the brands individually.
Some may argue, I could have scraped ZARA directly, instead of calling in Tina, but the reality is I would harvest a bigger gain if I scraped UNIQLO, or FOREVER21, and still called Tina in for ZARA.
Sellers have the incentive to put their effort in adding websites when others don’t cover them, and calling in others, when they do. This allows them to grow the graph faster, and reach together a larger audience, than they’d be able to do alone.
Plus: Launching The Partnership Program
We have seen network effects. Let’s speak about the Partnership Program:
Soon after its launch, we saw Data Boutique’s impact reach beyond the core web-scraping community: Decoupling data collection from data usage created new applications. Without the burden of data sourcing, we enabled teams to access data and create PoCs and MVPs quickly, safely, and with low investments.
From Business Intelligence to AI, from price monitoring to market research, applications are countless.
This is why we decided to open our Partnership Program: To strenghten our outreach to those applications, BI teams within corporates, AI startups and geeks. We offer a multi-year value-sharing program, rewarding the growth of the network.
Enter the program and refer data-hungry companies. It’s a variable-sum game.
Joint, all win.
About the Project
That was it for this week!
Data Boutique is a community for sustainable, ethical, high-quality web data exchanges. You can browse the current catalog and add your request if a website is not listed. Saving datasets to your interest list will allow sellers to correctly size the demand for datasets and onboard the platform.
More on this project can be found on our Discord channels.
Thanks for reading and sharing this.
Covering search, bargaining, enforcement, and distribution. See Dan Hockenmayer’s post on different marketplace types.