How to Use Data in Talent Pipeline Management

With the increased volume of inbound applications, we’ve been working with teams on how to manage this through designing processes to hold a positive candidate experience, leveraging technology for efficiency, and digging into network analysis. Most teams are looking at the first two, but very few are even thinking about the last one. So what is network analysis, why does it matter for recruiting, and how do you do it? What is network analysis? Network analysis is a method of studying the relationships and connections between ‘nodes’ or entities in a network. LinkedIn is the obvious example where people have relationships and connections to other people and organizations, and when you view profiles you can see mutual connections. The relationships we were seeking to better understand were between jobs and candidates applying for those jobs. Why does it matter for recruiting? For talent strategy, network analysis can surface incredibly valuable insights that can be used to identify and connect with potential candidates. Here’s how it works:  When candidates apply to jobs they create a connection, or what we call an application. When candidates apply to multiple jobs they create shared connections across jobs. When many candidates apply to many jobs you now have a very interesting network that can be used to better understand things like:  ➡ What jobs have shared candidate pools that could indicate if it would be more efficient to have some of those jobs consolidated to review a more streamlined pipeline? ➡ What jobs do candidates find most interesting for certain talent pools?  ➡ What jobs that have been filled historically could be used as sourcing pools for future roles? ➡ Are there job attributes that attract similar candidates - level, location, pay, skillset, etc? How do you do it? 1. Get the data Create a report from your ATS that has applications over the period of time that you want to analyze. The report should include a unique candidate and job ID, and upload your report into Google Sheets 2. Do the analysis Use Claude or ChatGPT to generate the Apps Script to run for generating the job pairs with shared candidates with the number of shared candidates for each pair. You could also expand this prompt to include other job attributes for your analysis. This will return the basics of shared candidates. I'll include a prompt you can use in the comments. If you want the Apps Script code and instructions I used, let me know in the comments. 3. Visualize the relationships between candidates across jobs You can take it a step further and visualize these relationships. Use a tool like Gephi where you can apply filters like number of shared connections >100 or using attributes like engineering or sales, etc. It will allow you to do all sorts of analyses through weighting variables and visualizations through color coding, sizing, etc.

A bad hire costs a business $15,000, and it happens all the time — nearly a third of new employees leave in the first 90 days.     If you’re recruiting 2,000+ people a year like we do… that failure rate just wouldn’t work.     There’s no recruiting crystal ball to tell you whether someone’s right for a role, but there is data.    Our Talent Acquisition team took all our data on past hires and focused on all those who stayed over a year.     Next, they looked at what type of experience those people had, their personality assessments, education — lots of different factors.     The end result was a profile for each of our most common roles that tells us who is most likely to succeed in that role.    It’s not a checklist and it’s only one part of a really thorough screening and talent acquisition process, but it’s an important first step that helps us make smarter decisions quicker.     How did it change those odds?   Our 90-day retention rate is at 97.38%.     #Recruitment #BusinessStrategy #BusinessGrowth 

Most recruiting teams don’t suffer from a lack of effort. They suffer from a lack of insight. In our new TA strategy, Pillar 3 is Talent Intelligence and it’s the one that helps everything else work better. It’s not just about collecting data. It’s about making it useful in the moment for recruiters, hiring managers, and execs. Its not perfect but we’re making faster, smarter hiring decisions with: → Google Gemini Gems: Summarizes scorecards, predicts offer declines, flags pipeline risks, and analyzes Zoom transcripts for deeper signal capture across interviews. → GoodTime: Delivers interviewer intelligence, cancellation and reschedule patterns, and a full-funnel view of candidate experience. → Greenhouse Software Reports + Auto Dashboards: Real-time pass-through, source of hire, and funnel health. → Tableau Dashboards: Tracks hiring velocity by team, role, region, and whole lot more! → LinkedIn Talent Insights: Competitor talent pools, market maps, and conversion trends. → LinkedIn Recruiter: Shows us who’s warm, who’s stalled, and who’s ready to re-engage. → Workday: Captures post-hire data, which will enable us to connect early performance and onboarding outcomes to sourcing, speed, and process quality. On the wishlist: → A true AI-powered Talent Intelligence platform like BrightHire. One that layers intelligence across every conversation and surfaces insights without the manual lift in our process. 🫶 This pillar helps us see around corners. It’s how we know what’s working, and most importantly what isn’t. Next up: Strategic Enablement. How we bring TA to the table with tools, not just reqs. How are you using talent intelligence to level up hiring decisions? #talentintelligence #recruitingops #recruiting #futureofwork #taops #ai #peopleanalytics