Making a sustainable business in the Web 3.0 era means incorporating feedback loops into the basic design of every product and service.
“Instrumenting” your products allows you to collect data on what your customers are doing, and enables you to quickly evolve your products to adapt to the market.
No one knows this better than one of the leading social networks, LinkedIn.
So we asked Jim Baer, the head of data science at LinkedIn, to join us at our upcoming DataBeat/Data Science Summit conference next month.
Like other social networks, LinkedIn carries a data-driven approach in its DNA. They have always been tuned in to all possible signals to ensure they continue to grow.
Metrics-hungry investors often clamor for extra visibility to a company, especially in its early days and after going public. Now an established powerhouse, LinkedIn is more committed than ever to looking at every feature through the lens of their users’ collective intelligence.
The fundamental question: Are customers using this feature or not? This question remains pertinent at full scale and applies not just to experimental features released only to beta testers, but to every single feature on the site. Because of LinkedIn’s huge user base, Baer and his teams have plenty to work with — helping executives make statistically defensible decisions.
The data gathered can produce many crucial insights, such as shifts in consumer demand, bottlenecks in processes, or unsuspected usage patterns. Analyzing this data can help uncover security threats, identify new market opportunities, and take customer segmentation to an almost personal level.
Over at LinkedIn, Jim Baer [left] describes using flexibly organized teams depending on projects, dedicated to identifying drivers of the basic aspects of customer analysis such as growth, engagement or revenue. (However, he reminded us that LinkedIn prefers to call its users “members.”) His motto is to always ensure their satisfaction and monitor their engagement to design products that are relevant so they use them.
With LinkedIn features like “People you may know” and “Groups you may be interested in,” Baer’s team is always fine-tuning the various recommendation engines and making sure that the site pushes you to use it more — while always only suggesting the best and most relevant content for you. Even aspects of LinkedIn’s user interface can be personalized to show custom shortcuts based on how different members tend to use the mobile app.
When asked about the role of humans in the strategy, he acknowledges that data doesn’t rule everything: All tests leave room for intuition and even serendipity. But we have to wonder if they instrument that too — just in case.
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