Howdy my bunch of rowdy recruitniks! today I want us to focus our data minded awesomeness on influencing hiring managers. I spoke to a recruiter this week who said that they have really good dashboards and data in TA but they struggle to make it land with hiring managers. Really common problem so below is a breakdown of the kinds of analysis and data that is beneficial for influencing hiring manager's
The TLDR is :
1. Your data should speak to their business goals, they don't care about conversion rates, but if you can link that conversion rate going up, to the engineering time spent on interviews going down... they will defiantly care about that.
2. Time and money. Translate your TA data into time and money.
let's get into it....
Data-driven recruitment has transformed how recruiters work with hiring managers. Instead of relying on subjective opinions or anecdotal evidence, recruiters can now use concrete data to inform decisions.
But so many of us are stuck in the outmoded communication style where we use arguments from authority eg "trust me I'm a good recruiter" but in a relationship where were are expected to influence without authority. It's maddening.
So here is a bit of a practical set of methods for using data in communication with hiring managers.
1. Setting Realistic Expectations Using Data
One of the first challenges recruiters face when working with hiring managers is aligning expectations. Hiring managers can have idealised views of the perfect candidate or an unrealistic timeline for filling a position. its the equivalent of going into a Ferrari showroom with the budget for a Volkswagen Lupo.... Data can help shape these conversations, ensuring that expectations are grounded in reality.
How to Do It:
Market Compensation Benchmarks
Use industry data to benchmark salaries for the position. For example, if the hiring manager is offering 20% less than the market average, you can use compensation data to show the salary mismatch and its potential impact on attracting top candidates . This is especially effective when managers are unwilling to adjust salary bands or expect to hire highly experienced candidates at below-market rates. you can find some free resources but always remember to get salary expectations in your screen calls, you can aggregate this field over say, a years worth of hiring to get really specific historical data on what candidates expect vs how willing they are to accept an offer.
Time-to-Hire Data
Present historical data from previous hires to manage expectations around timelines. If a hiring manager expects a complex role to be filled in two weeks, but the average time to fill similar roles is closer to 45 days, use your data to show the average recruitment timeline. This prevents setting impossible targets and helps both parties plan accordingly .
I track four dates, contact, offer, sign and start to build a stacked bar chart for this.
Its great for senior roles too as if historical data shows that, say and engineering director, including notice period will likely have them starting in 5 months time, you can push back and see if theres someone in the business can be promoted up into that role in 5 months and you backfill the more junior role with a lower time and cost per hire.
Talent Availability Metrics
Show the availability of talent for specific roles in your region or industry. Use data from talent pools or platforms to illustrate how many candidates fit the required profile and how many competitors are seeking the same talent. This helps hiring managers understand whether they need to be more flexible with qualifications or timelines.
You can combine this with your conversion data to show the number of candidates likely to reach offer stage.
"You want a senior rust developer.... with 15 years of experience in DevOps?... who invented functional programming? And can double as a UX designer?....and based in Cyprus? Theres four of them, so you will have 0.0067% of a candidate at the offer stage"
2. Creating Data-Driven Regular Updates
Ongoing communication is essential, but it’s even more impactful when backed by data. Weekly or bi-weekly reports that outline recruitment progress help hiring managers stay informed and engaged, while holding you accountable for the progress of the search.
How to Do It:
Pipeline Health Reporting
Send weekly updates on how many candidates are in each stage of the hiring pipeline. For example, share how many candidates were sourced, how many have been screened, and how many are in the interview process. You could also highlight the response rates or candidate engagement levels. This gives hiring managers insight into the volume of work being done and identifies any bottlenecks early .
Feedback Loops from Candidates
Include feedback from candidates on how they perceive the hiring company or role. For instance, if candidates express hesitation due to a lack of brand recognition or concerns about the hiring process, sharing this data can prompt a change in strategy. This real-time feedback helps hiring managers see market sentiment and adjust their approach if needed.
Adjusting Expectations Based on Data
After a few weeks, use the data you’ve gathered to reassess the search. If you’re not attracting the right candidates, show the hiring manager what the data says about sourcing channels, qualifications, or the job description itself. Then, collaboratively decide whether adjustments need to be made, such as widening the talent pool or refining the job requirements .
3. Presenting Your Insights to Influence Decision-Making
Once you’ve gathered data over the course of a search, use it strategically to drive decision-making. Data is a neutral, objective tool that can be used to remove emotions and subjectivity from the process.
How to Do It:
Candidate Source Effectiveness
Track and share which sourcing channels are providing the highest quality candidates. This allows the hiring manager to see which channels are worth investing more time and resources in. For example, you might find that candidates sourced from LinkedIn have higher success rates compared to those from generic job boards. or candidates sourced from GitHub are fewer, but have much higher conversion at the tech stages.
Quality of Hire Metrics
Measure the success of hires by tracking performance, retention rates, and how quickly new hires reach productivity. Presenting these metrics to hiring managers not only highlights the effectiveness of the recruiting process but also informs them about the impact of their hiring decisions . For example, if new hires from a particular source have higher retention and performance scores, that source should become a priority for future searches.
Candidate Experience Data
Gather and report on candidate feedback during the recruitment process. Candidate satisfaction, time taken during interviews, and communication responsiveness. If the data shows that a slow or confusing interview process is causing top candidates to drop out, you can present this insight to the hiring manager to streamline and improve the process.
Offer Acceptance Rates
If a high percentage of candidates decline offers, track this data and use it to adjust the offer packages. For example, if candidates are declining due to low compensation, use salary comparison data to recommend more competitive offer. its worth noting that the more you increase offer accept, the less volume you need in every previous stage to make a hire, to the time saving form offer accept allows you to make more hires elsewhere in the business.
4.Predictive Analytics
As recruitment evolves, so do the tools at a recruiter’s disposal. Predictive analytics is increasingly being used to anticipate hiring needs, identify potential skill gaps, and optimise recruiting strategies based on past data trends.
How to Do It:
Forecasting Hiring Trends
Use historical data and predictive models to forecast when hiring spikes might occur, helping hiring managers plan ahead. For instance, if data shows that demand for certain skills is increasing, you can recommend proactive recruiting efforts .
Forecast Level of Effort
You can use your basic recruitment conversion date backwards, to predict the volume needed, based on historical conversions to complete a certain number of hires, so if your hiring manager is building two new squads and need to make 8 full stack engineering hires, then you can run up the funnel and say
"that will mean 16 hours of your time in final interviews, 45 hours of engineer time on tech interviews and 125 recruiter screens. have you factored that into your points for this quarter?"
makes you looks awesome and you can start influencing your hiring manager to focus on more resource efficient processes
Book Update
Progress is coming along at a good clip. I've been hammering through the theoretical sections at light speed as I write extensively on this anyway. I'm also adding any formulas or technical bits in a resources section and writing up common scenarios you can use your new found data.... drivenliness to work through.
Im trying to stay away from decisions related to style as (A) I'm terrible at design and (B) it's just procrastinating from writing the actual content.
That being said, if anyone knows of a good tool to layout a non fiction E-book other than Canva, please let me know!
Well... that's issue 17. If you have any questions about it, or any feedback on this issue of The Data Driven Recruiter, grab me on LinkedIn for a chat.
I'll see you next week!