How to Identify Candidates Open to Change Before They Signal It Publicly

Two hundred million professionals have indicated on their professional network profiles that they are open to new opportunities. Only 40 million made it visible. The other 160 million are invisible to any recruiter relying on badges, banners, or self-reported availability.

That gap is the core problem in passive candidate identification. How can recruiters tell which candidates are becoming ready to move before they update a profile or turn on a badge. Career trajectory data, specifically the pattern of job changes, title progression, tenure length, and employer stability over a candidate's full work history, makes that prediction possible. One recruiting team built an AI model around these signals and reported 70% accuracy in predicting which candidates would respond positively to outreach. This article covers the specific signals they used, how to read them from enrichment data, and how to build a scoring workflow around them.

Why "Open to Work" Badges Miss the Best Candidates

The major professional network's "Open to Work" feature has two modes. The public green banner is visible to everyone, including the candidate's current employer. The private mode is visible only to recruiters with paid Recruiter licenses.

The stigma around the public badge is well documented. Victoria McLean, CEO of City CV and Hanover Talent Solutions, described it as looking "desperate and amateurish." Career coaches warn it signals that a candidate is struggling to find work, which can lead to lowball salary offers.

Recruiters who source top-tier talent see this play out daily. As one executive recruiter told us: "Sometimes the people that are most excited about representing themselves and being open to work are not the people that I want to talk to because they're not the highest quality candidate. The people that don't need a network, it's because they already have one and people know them and know they're good." A technical recruiting agency that specializes in placing engineers at high-growth startups was more direct: candidates with open-to-work badges are "typically not in the top 0.1% of engineers, which is what we go after." Another recruiter we spoke with actively filters open-to-work candidates out of search results entirely, treating the badge as a negative signal.

For recruiting teams, this creates a structural problem. If your identification method depends on candidates telling you they are open, you are working with the weakest slice of the market. The candidates most worth reaching are employed, performing well, and selectively open. Another constraint with this tag is that if somebody wants to change their job, they will not display the tag as its publicy visible to their managers.

The Passive Candidate Spectrum

The term "passive candidate" is usually thought of in a simplistic manner - either someone is looking for a job or they are not. Research from the largest professional network breaks this into four segments: 25% are actively applying, 15% are "tiptoers" who are not applying but quietly exploring, and 60% are not looking but would consider the right opportunity.

That 60% segment is the identification challenge. They will not apply to a job posting. They will not turn on a badge. But given the right role at the right time, they would engage. Rally Recruitment Marketing's data, drawn from BLS workforce surveys, puts the total reachable passive market at 74.4% of working professionals.

The question is how to distinguish the subset of that 74.4% who are approaching a transition point from those who are genuinely settled. Career trajectory analysis provides the answer.

Career Trajectory Signals That Predict Openness to Change

Academic researchers at Utah State and Arizona State have validated that behavioral patterns can predict turnover with 2x accuracy over baseline rates. Dr. John Sullivan's retention trajectory framework identifies 20 predictive factors organized around tenure history, career stage, and organizational fit. These frameworks were designed for employers trying to retain employees, but the same signals work in reverse for recruiters trying to identify candidates who are becoming open to a move.

The difference today is that you do not need to be the candidate's employer to read these signals. With people enrichment APIs that return full work history, title progression, and education for any professional, you can run trajectory analysis across thousands of candidates programmatically.

One recruiting team we work with built exactly this. Their AI analyzes each candidate's career history from enrichment data and outputs a percentage likelihood that the person is open to change. The core logic: if someone has been at the same company for 30 years, they are almost certainly not leaving. But if someone has changed companies three times in six years, has been in their current role for over a year, and their title has not moved up with each change, the model flags them as likely open. Job change frequency combined with title stagnation is the foundation of their scoring, and they report 70% accuracy on positive outreach responses.

Here are the five signals that matter most, drawn from both that team's approach and the academic research.

Title stagnation relative to tenure

A candidate who has been in the same title for three or more years while their peers in similar roles have progressed is experiencing title stagnation. This is one of the strongest predictors of openness to change because it reflects a gap between where the candidate is and where they expected to be.

To detect this from enrichment data, compare the candidate's current title against their title history. If they held progressively senior titles at previous employers (for example, Analyst to Senior Analyst to Lead over five years) but have been in the same title at their current employer for three years, the trajectory has flattened. The longer the flat period relative to their historical pace, the stronger the signal.

The recruiting team's algorithm captures this as "the name of the function is not higher than the other one," meaning the candidate has changed roles multiple times but their title level has not increased. When that pattern coincides with a job change frequency of every two to three years, the candidate is flagged as likely open.

Job change velocity approaching historical pattern

Most professionals develop a pattern in how frequently they change roles. BLS data shows average tenure is 3.2 years for workers aged 25 to 34 and 10.1 years for those 55 to 64. But individual patterns vary widely.

The signal is not just about raw tenure length. It is whether a candidate is approaching or exceeding their own historical average tenure. If someone's last three roles averaged 2.5 years and they are currently at 2.4 years, they are in the window where a move becomes statistically likely. If they are at 3.5 years, they are past their pattern and the probability of a near-term move increases.

Jacob Kaplan-Moss's tenure evaluation framework makes a useful distinction: "tenure in and of itself means nothing. It's the causes that we need to understand." Raw tenure is a blunt instrument. The signal comes from comparing a candidate's current tenure against their own trajectory.

Lateral moves after an upward trajectory

When a candidate's career shows consistent upward movement (Associate to Manager to Director) and then shifts to a lateral move at the same level, it often indicates one of two things: they took a role for a specific reason that may have run its course, or they are in a holding pattern at an employer that cannot promote them further.

In enrichment data, this appears as a title change without a seniority increase. If the candidate moved from "Director of Engineering" at one company to "Director of Engineering" at another, the move was lateral. If they have been at the second company for over two years without a title change, the holding pattern signal strengthens.

Skills additions that diverge from the current role

When a candidate adds skills, certifications, or courses to their profile that do not align with their current position, it often signals preparation for a different type of role. A backend engineer adding product management certifications, or a marketing manager completing data analytics coursework, is investing in a future that may not exist at their current employer.

This signal is detectable through enrichment data that includes skills and certifications with dates. Recent additions (within the last 6 to 12 months) that diverge from the candidate's current function are worth flagging.

Decreasing tenure at successive employers

If a candidate's tenure has shortened at each successive employer (4 years, then 2.5 years, then 1.5 years), the pattern suggests decreasing satisfaction with each new role or an accelerating search for the right fit. When this pattern is present, the candidate is more likely to be receptive to outreach, particularly if you can articulate why your opportunity breaks the cycle.

This is the easiest signal to calculate from work history data: extract start and end dates for each role, calculate tenure, and check whether the trend line is decreasing.

Behavioral Signals Before the Badge Goes On

Career trajectory signals are structural. They tell you about long-term patterns. But there is a layer of shorter-term behavioral signals that can indicate a candidate is actively beginning to explore, even before any public announcement.

The most telling of these is profile edit activity. When a candidate who has not updated their profile in months suddenly starts making changes, updating their headline, adding new skills, refreshing their summary, it often means they are preparing to be found. One recruiting platform we work with monitors candidates at scale for exactly this signal. As their team described the use case: "As soon as a candidate has edited their profile, you can get an alert. Why is this person all of a sudden editing their profile? Is she looking for a new job?"

The aggregate version of this signal is even more powerful. If 15 or 20 employees at the same company start editing their profiles within a short window, it can indicate impending layoffs or a restructuring that has not been announced yet. Those candidates are about to enter the market, and reaching them before the news becomes public gives you a window that closes quickly.

The key insight is timing. Profile edits happen before the open-to-work badge goes on, before the resume gets uploaded to job boards, and before the candidate starts responding to inbound messages from other recruiters. Monitoring for these changes through people watcher webhooks means you can get notified the moment something changes on a candidate's profile, before they even announce that they are open.

Company-Side Signals That Compound Individual Readiness

Individual trajectory signals tell you about the candidate's career pattern. Company-side signals tell you about the environment they are working in. When both point in the same direction, the probability of openness to change increases substantially.

The employer is posting backfills for adjacent roles

When a company posts job listings for roles adjacent to the candidate's position, it can indicate organizational restructuring, team instability, or a manager who is losing people. If a candidate's employer has posted three engineering manager roles in the last quarter while the candidate is a senior engineer on that team, the working environment may be shifting in ways that make external opportunities more attractive.

You can track this programmatically using job listing APIs that let you filter postings by company, title, and location. Monitoring job posting patterns at target companies gives you an early signal about internal instability before it becomes public knowledge.

Declining headcount without replacement

A company whose total headcount is declining, whether through layoffs, attrition, or hiring freezes, creates pressure on remaining employees. Workloads increase, promotion paths narrow, and morale often drops. Candidates at companies experiencing sustained headcount decline are statistically more likely to be open to outreach.

One investor we spoke with monitors these signals across their portfolio and target companies: "We want to understand if certain companies are adding people, reducing headcount, if they are signaling new product announcements, potential acquisitions, M&A, exits, all those financing events." The same data that informs investment decisions informs recruiting decisions, because the same company-level disruption that creates investment opportunity also creates candidate availability.

Company enrichment data that includes headcount time series (6-month and 12-month growth rates) makes this signal easy to track. A company with negative headcount growth over two consecutive quarters is worth flagging as a source of potentially receptive candidates.

Leadership turnover

When a company's C-suite or senior leadership changes, it creates uncertainty throughout the organization. New leaders bring new priorities, restructure teams, and shift strategy. Employees who were well-positioned under previous leadership may find their trajectory disrupted.

One headhunter platform built their entire prospecting model around this signal: monitoring executives in specific industries for departures, then alerting their recruiters that the company will need to fill the gap. The same watcher infrastructure works in reverse for candidate identification. A new CTO or VP of Engineering at a company where you have target candidates is a compounding signal worth acting on.

Track this through people watcher webhooks that notify you when executives at target companies change roles.

Building a Candidate Readiness Score from Enrichment Data

The signals above become actionable when you combine them into a weighted score. Here is how to structure the workflow.

Step 1: Define your candidate universe

Start with a people search filtered by the criteria that matter for your open roles: title, seniority, skills, geography, and years of experience. This gives you a list of candidates who match the role requirements, regardless of whether they are actively looking.

For example, to find senior backend engineers in the Brussels metro area:

curl -X POST 'https://api.crustdata.com/screener/person/search' \
  --header 'Authorization: Token $auth_token' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "op": "and",
      "conditions": [
        {
          "filter_type": "current_title",
          "type": "(.) ",
          "value": ["senior backend engineer", "senior software engineer"]
        },
        {
          "filter_type": "current_region",
          "type": "in",
          "value": ["Brussels, Brussels Region, Belgium"]
        },
        {
          "filter_type": "years_of_experience",
          "type": ">",
          "value": 5
        }
      ]
    },
    "limit": 100
  }'

curl -X POST 'https://api.crustdata.com/screener/person/search' \
  --header 'Authorization: Token $auth_token' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "op": "and",
      "conditions": [
        {
          "filter_type": "current_title",
          "type": "(.) ",
          "value": ["senior backend engineer", "senior software engineer"]
        },
        {
          "filter_type": "current_region",
          "type": "in",
          "value": ["Brussels, Brussels Region, Belgium"]
        },
        {
          "filter_type": "years_of_experience",
          "type": ">",
          "value": 5
        }
      ]
    },
    "limit": 100
  }'

curl -X POST 'https://api.crustdata.com/screener/person/search' \
  --header 'Authorization: Token $auth_token' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "op": "and",
      "conditions": [
        {
          "filter_type": "current_title",
          "type": "(.) ",
          "value": ["senior backend engineer", "senior software engineer"]
        },
        {
          "filter_type": "current_region",
          "type": "in",
          "value": ["Brussels, Brussels Region, Belgium"]
        },
        {
          "filter_type": "years_of_experience",
          "type": ">",
          "value": 5
        }
      ]
    },
    "limit": 100
  }'

Step 2: Enrich each candidate's full work history

For each matched candidate, pull their complete professional profile through the People Enrichment API. This returns 90+ data points including every employer, title, start date, end date, skills, education, and certifications.

The fields that matter for trajectory scoring:

• All employers with dates: calculate tenure at each role and total career length

• All titles: map title progression and identify stagnation or lateral moves

• Skills and certifications: identify recent additions that diverge from current role

• Current employer details: cross-reference with company enrichment data

Step 3: Calculate trajectory scores

For each candidate, compute:

1. Title stagnation score: years in current title divided by average title-change interval across career history. A ratio above 1.5 is a moderate signal, and above 2.0 is strong.

2. Tenure velocity score: current tenure at employer divided by average tenure across previous employers. Ratios approaching or exceeding 1.0 indicate the candidate is in their historical change window.

3. Trajectory direction: is the most recent move lateral, upward, or downward relative to the previous role? Lateral or downward after a history of upward moves is a signal.

4. Skill divergence score: count of skills or certifications added in the last 12 months that do not map to the candidate's current title or function.

Step 4: Layer company signals

Enrich the candidate's current employer with company data including headcount growth rates, open job postings, and leadership changes. Negative headcount growth, high job posting volume in the candidate's department, or recent C-suite turnover each add to the composite score.

Step 5: Set up ongoing monitoring

For high-scoring candidates, create watchers that notify you when their profile changes, when their employer posts new roles, or when executives at their company depart. These real-time signals tell you when a candidate who was already high-propensity takes an action that pushes them closer to active consideration.

The recruiting team that reported 70% accuracy used a similar methodology: career trajectory analysis from enrichment data, cross-referenced with employer stability signals, scored and ranked before any outreach was sent. Their lead described the process as building a system to "collect the data and understand the data and say, yes, these people, you can get."

Timing Outreach to the Readiness Window

Once you have identified high-propensity candidates, the outreach itself matters. Gem's analysis of nearly 8 million outreach sequences found that 21.3% of sequences receive replies overall, and personalized subject lines alone produce a 4.8% lift in open rates over generic ones.

The key is referencing the context that makes your timing relevant without revealing the scoring methodology. If a candidate recently added a certification, mention it. If their employer just went through a leadership change, acknowledge it. If they are approaching a tenure milestone, frame the opportunity around growth.

The window is narrow. The best passive candidates, once they decide to look, are typically off the market within 10 days. Reaching them before that 10-day window, while they are still in the "would consider the right opportunity" phase, is the entire point of trajectory-based identification.

For teams running this at large scale, the workflow is: score candidates weekly from enrichment data, layer real-time watcher signals on top, and route high-scoring candidates with recent compounding signals directly to recruiters with full context already attached. This eliminates the manual research step that typically adds days between identification and first outreach.

Conclusion

Passive candidate identification has been treated as a sourcing problem, the solution to which was find more candidates, send more outreach, hope someone responds. The actual bottleneck is not finding people. It is knowing which people are approaching a transition point.

Career trajectory data makes it possible to guesstimate this point. Title stagnation, tenure velocity, skill divergence, and employer instability are all readable from enrichment data that is available through APIs today. Recruiting teams that build scoring models around these signals reach candidates while they are still in the "would consider the right opportunity" phase, before the 10-day availability window opens and closes.

The methodology works whether you are building an internal recruiting tool, running an agency, or operating a recruiting platform. The data infrastructure is the same: search by criteria, enrich for full career context, score on trajectory signals, monitor for real-time changes, and route high-propensity candidates to outreach with full context.

If you are building candidate identification into your recruiting workflow, see how Crustdata's people data APIs work or book a demo to walk through the scoring model with our team.