Five more Alexes
What makes your best performer work here is local, findable, and teachable in weeks
"We love Alex. I wish I had five more of him."
Every talent leader says a version of this sentence eventually. The name changes. The role changes. Sometimes Alex is a producer, sometimes a claims adjuster, sometimes the branch manager holding a region together. It is the most honest product requirement in the talent industry: figure out what makes our best person work here, then find more people like that.
The first half of that requirement has never been met, which is why the second half stays a wish.
Ask Alex's manager what makes him work and you get adjectives. Hungry. Coachable. Great on the phone. Adjectives cannot be screened for, so the team translates them into things that can: the right license, a competitor's name on the resume. The wish for five more Alexes becomes a Boolean filter, and the filter goes out and rejects the next five.
Then the disappointment compounds. The reqs fill, the new class arrives, and six months later the same leader is saying the new people are nothing like Alex. So the filter gets tightened, and the next class gets worse. Nobody in this loop is being careless. They are working from the only description of Alex they have.
That the filter rejects the next five is a measurable claim, and we measured it.
The resume is a bad photograph of Alex
What makes Alex work here is recorded. His call transcripts in the CRM capture what he says when a prospect stalls and how he reopens a conversation that went quiet. His performance history in the HRIS shows how his production built quarter over quarter. His original candidate record sits in the ATS, a snapshot of what he looked like on paper before anyone knew what he would become. The pattern exists, in data the company already owns. The resume version of Alex is a lossy compression of that pattern, and what got compressed away is the part that mattered.
At a Fortune 500 insurance carrier where Nodes runs in production, we parsed 8,181 unique skills from four years of applicant data and tested the 3,597 measurable ones against post-hire production. After Bonferroni correction, zero predicted sustained performance. Thirty correlated with lower output. The industry-experience filter, the one hiring managers defend hardest, had been eliminating 80% of the carrier's eventual top performers before a human ever saw them. The full audit is published at keywords vs performance.
Hold that 80% against the sentence at the top of this piece. A company can wish for five more of its best people while running a filter that rejects four of every five of them on arrival. The wish and the screening stack point in opposite directions, and nobody can see it, because the only view of Alex anyone has is the photograph.
What is the Performance Genome
The Performance Genome is the computed pattern of what actually predicts performance in this role, in this place. Computed is the load-bearing word. It is built by connecting systems the company runs today, CRM transcripts, HRIS performance data, ATS candidate records, and finding what separates the people who performed from the people who looked identical at the application stage and did not. It updates as outcome data arrives. And it can score any record the company holds against it: a new applicant, or a current employee two departments away.
A definition that short hides the hard part: "in this place."
Alex in New York is a different Alex in LA
The lookalike-modeling industry treats the ideal-candidate profile as portable. Build the template once, from your top performers or from an industry benchmark, then apply it everywhere. Most vendors in this market sell some version of that template.
The carrier we work with operates 215+ locations. The producer job carries the same title in all of them and is a different job in most of them. Lead density and product mix vary by territory, and so does the age of the book a new producer inherits. What a cold call must accomplish in its first ten seconds in midtown Manhattan has little in common with what it must accomplish in a small town where the prospect personally knows two other agents. The manager varies too, which changes which behaviors get coached and which get worked out of you in the first months.
So the behaviors that compound in one territory stall in another. A national template averages across all of it, and the average is where the signal dies. Cross-company templates are worse: your competitor's top-performer profile describes their market and their comp plan. An industry benchmark for what a great producer looks like is a description of someone else's Alex.
The uncomfortable implication is that a match score is a property of a pairing, the person and the place together. The same applicant can be a strong match for the Phoenix book and a middling one for the Manhattan one. Any system that hands a candidate one score for the whole company is averaging again, one level up.
Persona locality is the least discussed property of performance prediction, and I suspect that is because of what it implies. Everyone accepts that culture is local. Accepting that the predictive pattern of performance is local too, down to the territory, means a portable ideal-candidate template cannot work even in principle. The wish was never for five more great salespeople in general. It was for five more people who work here, and "here" carries more weight than any other word in the sentence.
Finding the next five
Once the pattern is computed and local, finding more Alexes stops being a sourcing problem and becomes a scoring problem. Every applicant in the funnel gets scored against the pattern for the role and the place they would enter. Nothing about the candidate has to change. What changes is what the company can see.
A score on its own moves nothing. Each one surfaces as a proposal, and the answer arrives with its evidence attached: a trace of what was read and what was weighed, which a reviewer can pull apart and challenge. A recruiter approves, edits, or declines.
At the carrier, the evidence behind this covers four years of production data, 10,765 agents in the study cohort, and 850,000+ applicants scored. Hires made against the genome reached the production milestone in 62 days against a 109-day baseline. First-year retention on the producer cohort moved from 64% to 91%. The methodology, including the adversarial review protocol behind every score, is published in Decision Traces.
The retention move is the one I point skeptics to, because retention is where bad matching surfaces. A mis-scored hire can survive an interview and even a strong first quarter. Holding 91% of a producer cohort through year one means the pattern matched people to the territory they entered, in the place where mismatch is most expensive to hide.
The genome also answers the half of the wish nobody says out loud: once you find the five, how long until they produce? The same pattern that scores a candidate teaches the new hire, because it encodes what the best producers in her territory do in the situations she is about to face. Ramp that ran 8 to 12 months on the producer cohort now runs six weeks.
The almost-Alexes already on payroll
The sentence assumes the five must be hired from outside. Some of them badge in every morning. A service rep with three years of recorded work whose record scores high against the producer pattern for her region is invisible to every process her company runs, because internal mobility runs on self-nomination and manager memory, and her manager has every incentive to keep her where she is. The genome scores internal records the same way it scores applicants, and the almost-Alexes surface on the same list.
When her record surfaces, it surfaces as a workflow with numbers on it: what the move costs, and what leaving her where she is costs in an unfilled producer seat. That turns a turf conversation into a business case, which is the only form in which her manager can say yes gracefully.
Regular readers will recognize the lineage here. The persona-based hiring pieces on this blog described the destination. The Performance Genome is how the destination gets built: computed from production outcomes instead of profile descriptions, and local instead of portable. One object, one vocabulary, from here on.
The sentence, answered
The sentence arrives as a sigh, because it is a wish about a person, and wishes about people do not scale. Stated against a computed local pattern, it becomes a query: what predicts performance in this role in this place, who across the applicant pool and the current workforce scores against it, and how fast they can be made productive. How the pattern gets computed, what the context graph underneath it looks like, and why the whole thing has to run inside the customer's VPC is covered in Workday Is the Friend Graph. This piece is about the sentence. The next time you hear yourself say it, the second half has an answer.
Saad Bin Shafiq is the founder of Nodes. Anchor pilot: Fortune 500 insurance carrier, four years of production data, 10,765 agents. Methodology: Decision Traces.