How to build an effective professional network on LinkedIn: Some data-driven insights


Our analysis is based on the career progressions of a random sample of 3 million members from across the world who held at least one full-time job between July 1, 2017, and December 31, 2019. The dates were chosen to avoid confounding data from changes in job seeker behavior during the global pandemic.

Step 1: Accounting for the effect of demographics and experience
Although professional networks are important, they are by no means the only factor that affects career mobility. For example, demographics such as age, gender, education, and country of residence, as well as professional features such as industry, job function, seniority, and level of experience, all factor into your career mobility. In order to make more of an apples-to-apples comparison later on, we control for a total of 18 features that are not directly related to members’ professional networks but may affect their career mobility in the first step of our analysis. In particular, we use a survival model to estimate the expected amount of time it takes for members to transition to their next job based on their demographics, professional experience, and other non-network related features.

Step 2: Identifying the most important network features for career mobility
In the second step of our analysis, we sought to identify a shortlist of the most important network features for career mobility. To this end, we constructed a list of 24 features that capture different facets of one’s professional network. For example, in addition to the raw count of members’ LinkedIn connections, we constructed features that represent their connections’ absolute/relative seniority, employment status, company, industry, and job function; we also considered looser definitions of “professional network,” such as whether or not the member is in a group, how many companies the member is following, and how many professional endorsements the member has received from their network.

To understand the relationship between members’ networks and their career mobility, we look at the residual variation in career mobility that can’t be explained by non-network features in Step 1 and try to identify the network features that are the most explanatory of this residual difference. The following are the top three network features that were selected by LASSO as the most predictive of the residual difference in career mobility after controlling for a myriad of non-network demographic, education, and career-related features.

  1. Number of connections from companies other than the member’s current employer

  2. Whether the member is in at least one group on LinkedIn

  3. Number of companies that the member follows on LinkedIn

Step 3: Quantifying the relationship between top network features and career mobility
In the final step of our analysis, we quantify the relationship between the residual variation in members’ career mobility (not explained by their demographics, education, and professional experiences in Step 1) and the top three network features identified in Step 2 using an OLS regression. After controlling for members’ non-network features, we observe a statistically significant relationship between all three of the network features from Step 2 and members’ career mobility. We further discretized these network features in order to identify the threshold to cross that corresponds with the greatest gain in career mobility. From this, we arrive at a checklist of items that we believe can give members some guidelines as to how to build a professional network on LinkedIn that can accelerate their career progression.

The Professional Network Checklist

By comparing members with similar demographics but varying professional network structures, we identified three network features that bear the strongest relationships with career mobility. Relative to comparable members who do not satisfy these network criteria, those who do, on average, realize between 7.1% and 22.9% higher career mobility speeds (defined as the inverse of the time it takes members to transition to their next job from the start date of the current job).



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