LinkedIn is the world’s largest professional network with over 740 million members worldwide. With over 39 million companies represented on LinkedIn, there is an enormous amount of data generated on the platform every day. This includes profile information, skills, interests, connections, group discussions, job postings, and more.
To make sense of all this data and derive value from it, LinkedIn relies heavily on big data analytics. Big data refers to large, complex datasets that traditional data processing applications cannot adequately deal with. Big data analytics examines these massive volumes of data to uncover patterns, correlations, trends, preferences, and other insights.
For LinkedIn, big data analytics delivers several key business advantages:
- It enables better understanding of members to improve their experience on the platform.
- It provides recruitment and talent insights to employers.
- It allows delivering more relevant, timely and engaging content to members.
- It supports developing premium products and features that members will find valuable.
- It improves LinkedIn’s marketing efforts by identifying audience segments and targeting advertising.
Let’s look at some specific ways LinkedIn taps into big data analytics across its platform.
Understanding Members
With over 740 million members, LinkedIn has a vast repository of data on professionals from all over the world. Big data analytics helps LinkedIn deeply understand its members – their interests, skills, aspirations, challenges, career trajectories and more.
These member insights allow LinkedIn to improve various aspects of the user experience, such as:
- Personalization: By analyzing member activity, interests and profile data, LinkedIn can provide customized feed recommendations, relevant job suggestions, and profile improvement tips.
- Search: LinkedIn search leverages member data to return more relevant people, jobs, content, companies, groups etc.
- Social graph: Mapping the professional interconnections between members allows LinkedIn to recommend useful new connections and highlight shared connections.
- Skill endorsements: Big data helps identify top skills for members which can then be endorsed by their connections.
- Group suggestions: LinkedIn can recommend highly relevant groups for members to join based on their profile data.
Overall, a data-driven understanding of members helps LinkedIn be more useful and engaging for its community of professionals.
Talent Solutions
LinkedIn Talent Solutions offers recruitment, talent branding and skill insights products to enterprises. Here again, big data analytics powers key capabilities:
- Targeted recruitment: By analyzing member skill sets, employer history, qualifications, interests etc. recruiters can precisely identify and reach suitable candidates.
- Skill mapping: LinkedIn skills graphs give talent analytics into skill gaps, emerging skills, talent migration patterns etc. to improve workforce planning.
- Competitor analysis: Recruiters can research talent moves to/from competitors to benchmark recruitment and retention practices.
- Premium talent search: Big data powers LinkedIn Recruiter which lets you search LinkedIn’s entire member base and identify the most relevant candidates.
- Salary insights: Tools like Salary provide data-driven compensation analysis specific to industries, locations and skill sets.
For HR managers and recruiters, these insights from big data analytics make LinkedIn an invaluable tool for sourcing and engaging talent.
Content Recommendations
To keep members engaged, LinkedIn strives to serve content that is timely, relevant and appealing to each individual. Some ways big data drives more personalized and engaging content experiences:
- Feed algorithms: Sophisticated machine learning algorithms analyze member activity and interests to populate the feed with engaging content.
- Sponsored content: Advertisers can target content to members based on their profile, network and interests for better visibility.
- Interest tags: Millions of member actions are used to identify thousands of unique interest tags that can be matched to content.
- InMail suggestions: Data on member attributes and interactions informs recommendations on which members to reach out to via InMail.
- Creator mode insights: Analytics into post performance help creators shape content strategy and growth on the platform.
Big data is vital for LinkedIn to leverage its unmatched professional audience and help brands meaningfully engage this audience.
Premium Products
LinkedIn’s premium offerings would not be possible without big data analytics. These include:
- Premium Career: Users get personalized guidance for resume improvement, interview prep etc. based on their profile data.
- Premium Business: Enables targeted advertising to quality leads based on member professional and company data.
- Recruiter Lite/Pro: Recruitment analytics helps find the best candidates faster.
- Sales Navigator: Users can identify key accounts and build relationships aided by company/contact insights.
- Learning subscriptions: Analytics informs development of skill-based learning content aligned to member needs.
The relevance, customization and actionable insights delivered by these premium services depend heavily on big data analytics.
Big Data in Action
To make the use of big data more concrete, here are some statistics from LinkedIn that highlight the scale and impact of their data analytics initiatives:
- 50+ petabytes of data processed across member activity, jobs data and more
- 100% member base scanned daily to connect talent with relevant opportunities
- 1 million inferred skills added to member profiles daily through machine learning
- 2.5 million company pages created using firmographic data to showcase brands to talent
- 75% of feed engagement driven by relevant content recommended through analytics
These numbers give a sense of how deeply analytics pervades everything LinkedIn does – from understanding its members to creating an engaging, personalized experience across its platform and offerings.
Conclusion
LinkedIn has leveraged big data analytics to greatly enhance value for members as well as enterprise customers. The depth of insights uncovered from LinkedIn’s data assets would not be possible without big data technologies like Hadoop, Spark, machine learning algorithms and more.
Analytics has evolved from a business support function to becoming LinkedIn’s linchpin for deepening member engagement, improving products, targeting customers, informing content strategy and more. With data generation growing exponentially, expect LinkedIn to continue finding new ways to tap analytics to create economic value.
