Over the past year, LinkedIn has seen tremendous growth. The number of users has exceeded his 1 billion, content shares have increased by 42%, and special features such as newsletters have helped him collect more than 450 million subscribers. And LinkedIn's product teams have transformed the platform far beyond standard short-form content and simple brand extensions into a dynamic space for executives.
But corporate teams aren't taking full advantage of one of LinkedIn's most valuable tools: data. Executives and their teams are not taking full advantage of the extensive data available and are not properly leveraging it to improve their platform and social strategy recommendations.
Part of this oversight is due to the structure of LinkedIn's data ecosystem. Microsoft, which owns LinkedIn, maintains strict user privacy standards. There are no third-party tools that can connect to LinkedIn's API to get comprehensive metrics from across the platform. Instead, the data is generated manually (which is very time consuming) or collected through backend access to the executive's profile. But even with direct access to profiles, the data is fragmented across his three main sources and not seamlessly integrated into one consistent dashboard, making it difficult to gain meaningful insights. Masu.
Challenges to data integration
The most widely used data source is the “Creator Mode” dashboard, which provides an overview of executives’ LinkedIn page performance, including followers, impressions, engagement, top posts, and broad demographic data. But this one data source is just the tip of the iceberg when it comes to truly understanding how an executive's LinkedIn profile is performing.
Dashboards provide a broad overview, but require manual exploration to obtain detailed post-level data. Other potentially meaningful data layers include profile view statistics (providing executives with comprehensive analysis of who is viewing their profile) and who viewed these views. Contains demographic and geographic information about. LinkedIn also offers its own analytics dashboard that shows how individual content is performing. You can also analyze up and down fluctuations in your profile's follower count to determine which content attracts more followers and which content loses them.
With all this invaluable data available, the main challenge is to integrate it all. The lack of easy integration means that executives have to manually combine and analyze all their data to get a comprehensive view of what's working, what's not, and where there's room. means you need to.
Below are three recommendations to help you leverage LinkedIn data to increase the ROI of your executive programs and make faster, smarter decisions.
Develop a comprehensive LinkedIn data lake hub
Step one is to aggregate all available LinkedIn data sets (individual post-level data, creator mode insights, and profile audience statistics) into a single, centralized hub. You can do this by downloading and manually capturing key data points about an executive's profile, then cleaning and organizing the data for immediate analysis.
Establish strict data collection frequency
Whether using manual analysis or leveraging AI technology, it is essential to develop a systematic data collection process with checks and balances that reduce manual collection errors. Establishing a clear and consistent collection cadence is the way to maximize the potential of your LinkedIn data.
MikeWorldwide collects “Creator Mode” data and profile data monthly, but post-level data is added two weeks after each post is published to give you a complete picture of how long your content will last. The frequency of data entry depends on the specific goals of your campaign. For example, large campaigns may require weekly or daily updates to provide the quick insights needed for initiatives such as paid thought leadership campaigns.
Use advanced AI for better data analysis and content recommendations
AI is great at processing large data sets quickly and uncovering complex patterns that aren't immediately obvious, and teams should use these tools to improve data lake analytics. . A key part of a successful LinkedIn thought leadership campaign is being able to predict what type of content will resonate most strongly with different segments of your audience (including the most effective times of day to post). Machine learning is uniquely suited to this task.
AI can also estimate the potential effectiveness of different content strategies and copy recommendations, making data-driven decisions more accurate and reliable. Incorporating technologies like AI into your data lake greatly improves your chances of building meaningful executive visibility and company reputation on LinkedIn.
Clayton Durant is Director of Emerging Media and Platform Strategy at MikeWorldwide. Dorianne Ciccarelli is Vice President of Digital at MikeWorldwide.