Data Scientist Shares his Growth Hacking Secrets

In this article, we discuss various strategies used to generate exponential traffic growth, while preserving traffic quality, and user loyalty. Our growth hacking engine is a combination of
Raw data science: getting the right data sets, leveraging them,
Playing with various tools and API’s: designing an automated machine-to-machine communication service between Hootsuite and Twitter / LinkedIn based on insights automatically distilled from the following data sources: (1) data obtained via the Google Analytics API (traffic statistics about 50,000 live DSC articles), and (2) data collected via a web crawler written in Python
A blend of high-level (strategic) data science and low-level (tactical or operational) data science. In the end, relatively little coding is involved in the process. Domain expertise and smart innovation play a critical role.
Optimizing parameters of the statistical process used to select articles, create tweets, and schedule them, using experimental design and A/B testing
Artificial intelligence: detection and removal of articles that are time-sensitive, automated creation of relevant hash-tags for selected tweets, and creation of a taxonomy of all our articles using simple indexing classification scheme
Smart analytic-driven advertising on Twitter, using a good list of data science thought leaders worth following, as our core data set for advertising purposes. The creation of this list is an interesting data science project in itself.
Smart analytical and ROI-driven advertising on Google, as well as LinkedIn hacks, to get new members



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