Latest / Hiring & Firing with Fexingo: Recruitment, Performance, and Workforce Decisions / How a Rejection Letter Became a Second-Chance Hire Pipeline
Transcript
- Lucas: So let's start with a number: nearly sixty percent of candidates who get a rejection letter never hear from that company again. Ever. Not even a newsletter. Luna: And that feels like a missed opportunity, right? Because you spent all that time screening them. Lucas: Exactly. So today I want to talk about a mid-sized logistics firm, about twelve hundred employees, that decided to turn that graveyard into a garden. They built what they call a candidate pool, but it's not the kind of passive database that just sits there. It's actively managed, and it's already changed how they hire for hard to fill roles. Luna: I'm curious about the mechanics. How do you 'actively manage' a bunch of people you said no to? Lucas: Well, first, they changed their rejection letter. No more 'we've decided to move forward with other candidates, best of luck.' Instead, they send a personalized note that explains, in general terms, what the selection process revealed about the candidate's strengths and where they fell short relative to the role. Luna: That's pretty bold. Don't most companies worry about getting sued for saying too much? Lucas: They were careful. They stuck to job-related criteria, and they had their legal team sign off on the language. But the key was they didn't just stop there. They invited the candidate to opt into a talent community, and if they agreed, they'd be tagged in their applicant tracking system with specific skill clusters. Luna: So they're basically building a database of people they already evaluated. Smart. But how do they keep it from going stale? Lucas: That's the part I found most interesting. They don't send generic blasts. They use a customer relationship management tool, the same kind sales teams use, and they set up a cadence: a quarterly email that's tailored to the candidate's skill cluster. So if you were a warehouse supervisor candidate, you might get a note about a new distribution center opening, or a piece of industry research on automation. Luna: Okay, so it's like a nurture campaign for candidates. But what about the ones who might have been a bad fit for one manager but a great fit for another? Lucas: That's exactly what happened. They had a candidate who interviewed for a route planning role and didn't get it because the team needed someone with more experience in a specific transportation management software. But six months later, a different part of the company needed someone for a dispatch role, which required that same software. And because the candidate had opted in, they were right there in the system. Luna: So they didn't have to start from scratch. How much time did that save? Lucas: That's the killer stat. Their average time to fill for a specialized role dropped from about forty-five days to twenty-six. And the offer acceptance rate went up, because these candidates had already gone through part of an interview process and knew the company, so they weren't walking into the dark. Luna: I'm thinking about the candidate experience, though. Don't people feel like they're being kept on a leash? Lucas: The firm was careful about that. They made sure every email had a clear unsubscribe link, and they only sent two or three touchpoints a year. They also asked candidates at the end of the process, 'Would you like to stay in touch for future roles that match your skills?' It was an explicit opt-in. So the people in the pool want to be there. Luna: That makes sense. But how do you measure whether it's actually working? I mean, beyond time to fill and acceptance rates. Lucas: They started tracking a metric they call 'pipeline conversion rate.' That's the percentage of candidates from the pool who eventually get hired, and they compare it to the conversion rate from cold applications. It's about two and a half times higher for the pool. Luna: Two and a half times. That's a big deal, especially for roles that are hard to fill. Lucas: And there's a cost angle too. Recruiters spend a lot of time sourcing and screening. With the pool, they already have verified skills and a behavioral assessment on file, so they can skip some of those early stages. The CFO estimates they save about four thousand dollars per hire in sourcing costs. Luna: So, is this basically a boomerang hire program, but for people who never actually worked there? Lucas: In a way, yes. But there's a key difference: with boomerangs, you have performance data from someone who worked at your company. Here, you have interview data and assessment data from someone who was nearly hired. It's a different level of signal, but still valuable. Luna: What about the risk of 'false positives'? People who perform well in an interview but bomb on the job. Lucas: That's a real concern, and they addressed it by being more rigorous in the second round. They don't just fast-track people from the pool based on an old interview score. They re-interview, but they fold in the previous data to focus on areas of concern. So if someone had a weak spot in, say, conflict resolution, the second interview digs into that specifically. Luna: That's actually a smart way to use data. It's not a rubber stamp; it's a targeted re-evaluation. Lucas: Right. And they've also made it a point to be transparent with candidates about how their data is used. Their privacy policy clearly states that opting into the talent community means your assessment results and interview notes are stored and may be accessed by recruiters for future roles. That's a big deal from an ethical standpoint. Luna: I bet some candidates still feel weird about that. Like, do they really want a company holding onto that much data about them? Lucas: That's the tension. But in their surveys, they found that about seventy percent of candidates who opted in said they appreciated the transparency. And they had a zero-tolerance policy for using the data in a way that wasn't clearly communicated. It's a balance, and they seem to be doing it right. Luna: So, what's the one thing you want our listeners to take away from this story? Lucas: I think it's that the rejection letter is not the end of the relationship. It can be the beginning of a long-term talent pipeline. And with the right structure, respect for the candidate, and a bit of patience, you can turn a 'no' into a future 'yes' without spending a fortune on sourcing. Luna: That's a great note to end on. And hey, before we wrap up, if you've gotten value from these conversations, whether it's a new idea for your hiring process or just a fresh way to think about your workforce, you can support the show at buy me a coffee dot com slash fexingo. It's a small gesture that helps keep these episodes ad-free and accessible to everyone. Lucas: Yeah, we really appreciate that. Now let's get back to the story. The other thing I found fascinating was how they used workforce analytics to predict which pool candidates would actually accept an offer. They looked at factors like how quickly a candidate responded to emails, and whether they engaged with the quarterly updates, and that predicted acceptance rates pretty well. Luna: So they're using engagement data to prioritize who to approach first for a new role. Clever. Lucas: Exactly. And it's not just about responsiveness. They also track things like location preferences and salary expectations, so they don't waste time contacting someone who's likely to say no anyway. Luna: It almost sounds like a dating app algorithm, but for jobs. Lucas: Ha, that's one way to put it. But the bottom line is they're making smarter, data-informed decisions about who to engage with, and the results speak for themselves. Their internal mobility rate has also gone up, because some of these rejected candidates, once they got hired, ended up moving into other roles within the company, which is a whole other benefit. Luna: That's a nice side effect. So, would you recommend this approach to a small business with, say, fifty employees? Lucas: I'd say it's scalable, but you need the right tools. You don't need a fancy applicant tracking system; even a spreadsheet can work if you're disciplined. The key is to actually follow up and maintain those relationships. If you're not going to commit to that, it'll fall flat. Luna: Fair enough. So, what's the next evolution of this? Where do you see this going? Lucas: I think we'll see more companies integrating these candidate pools with predictive analytics, maybe even using machine learning to flag who's ready for a role based on their prior assessment data and market trends. But that's a topic for another day. Luna: Sounds like a good one. Thanks for breaking this down, Lucas.