Data Science applications: Improve Domain Flipping

Domains are widely considered digital assets, which increases their value over time when they are well picked up. However, in the coming of the digital economy era, there are so many market inefficiencies (asymmetry information, etc.) in how the market participants value this kind of asset. In that sense, an interesting way to exploit some inefficiencies is domain flipping.


Domain flipping is the practice of buying a good potential domain name as cheaply as possible. Then, after a few days (months or even years), you sell the domain to an interested party for more than you originally paid.

With Data Science, we can make the process of buy and sell (for a profit) domains an ultimately profitable business. Here, I will show you how data science solutions could improve flipping domains:

  1. Domain’s name: Understand which can be a good domain name for a given business is the most important domain flipping task. Here, a data scientist could use NLP techniques and historical valuation vs. performance metrics to find patterns on which domain names could hide a huge potential before the market participants price in the domain’s value.
  2. Historical domain sales: Analyzing the patterns within the historical domain sales and identifying the causes of the spike of valuation in the domains (Social media trends, Google Trends, etc.) could provide flags on potentially good deals on the table.
  3. Asymmetry information: Having access to information in Google Trends, Google Adwords, Social Media Trends, GoDaddy, Sedo, Flippa using techniques such as web scraping could help you to shape a better understanding of what has driven the market in valuing domains.

In each of the above areas, there are many Data Science problems that could be solved using Supervised, and Unsupervised learning, Recommendation systems, Mathematical Optimization, and more.

Please find below the flowchart of the Domain Flipping process.

Domain flipping process

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