How to Manage the Surge in Applicant Volume

Sep 30, 2024

To Our Talent Community,

“400 applicants applied to this job” – LinkedIn, 48hrs after posting! The reality of today's job market is overwhelming for both candidates (who wonder, what’s the point of applying when so many candidates have already?) and recruiting teams (how can I possibly review that many applicants AND deliver a positive experience? And where should I begin?).

According to Ashby, applications have 2x since 2021! Specifically, there’s been a 207% increase in business role applications and a 161% rise for technical roles since 2021. Year-over-year growth in inbound applicant volume has slowed but has not stopped, with the 2024 average now at 4.2x and 3.8x the 2021 average for business and tech roles, respectively. After many layoffs, fluctuation in job demand, and the rise of automation bots that help candidates submit applications in bulk – inbound applicant volume is HIGH!

We asked you, our Talent Community, about managing the high volume and if/how processes/practices have adapted and teams said their current processes (for reviewing resumes/managing the applicant pool) are far from optimized, an average of 4 out of 10 (10 - very optimized). Teams shared the following about reviewing applicants: “it’s very time consuming, and the yield is low”, “our current tools are ineffective, making it really difficult to quickly surface top talent,” and lastly, “we’re under-resourced, and are pulled in many directions - closing candidates, coordinating late stage interviews, sourcing candidates, optimizing hiring practices, and more, it’s hard to juggle.”

With inefficient processes, under-staffed recruiting teams, AND high volumes of applications, the candidate experience suffers. In our experience, candidates wait anywhere between 2-10 days for their application to be reviewed (Gem data says 4-6 days, and Ashby data from this quarter had a medium of 4.5 days; long story short it varies by role, location, team performance, and tool efficiency. Here’s a cautionary tale if yours is on the higher end – when the total process drags beyond 60 days, candidate satisfaction (measured by NPS) drops by 54.9% for rejected candidates and even drops by 16.5% for those that got the job, impacting your Talent Brand and close-rate (according to Ashby Data).

So…how can we optimize and effectively manage the applicant pool? You may be wondering: As a candidate, should applying first mean you’re reviewed first by the Recruiter? Should Recruiting Teams leverage AI to rank and/or sort candidates, or prioritize employee referrals? Before we discuss best practices, let’s re-enforce why it matters; why should Recruiting Teams invest in optimizing their applicant pool and resume review process?

The #1 Source of Hire

Bottom line: the applicant pool is the leading source of hires, accounting for 43% of total hires in Q4 2024 (Ashby Data).

Optimizing how we manage the pool can improve time to hire, candidate experience, talent brand and recruiter productivity. Jim Miller, who led the team that managed global inbound applications for Google (> 4MM annually) doubled their hires by restricting the number of roles applicants applied for, thereby increasing application accuracy. And this was before many of the AI tools we have today! Check out that article if you haven’t already.

Solutions and Best Practices from the Talent Community

To optimize the application review process TA Teams are using a combination of tools, resourcing, and tactics, and though there is no silver bullet, here’s what folks have found to be effective:

AI Tools: Teams are investing in AI to help streamline the resume review process; allowing recruiters to filter, search, and sort resumes. More on this below.
Knock-out Application Questions: Pre-screening applicants using mandatory "knock-out" questions (e.g., minimum qualifications, location, work authorization, years of experience, etc) is a widely adopted practice. This helps eliminate unqualified candidates early, saving time for recruiters.
SLAs for Recruiters: Some teams have implemented Service Level Agreements (SLAs) to ensure timely application reviews, and reports/dashboards that highlight candidates that require action. While this can drive accountability, it can’t stand alone as the solution.
Inbound Sourcers (or Resume Screeners): A few organizations have hired dedicated inbound sourcers (or partnered with offshore teams and/or firms) to review applications, enabling their core recruiting teams to focus on more strategic tasks like sourcing and closing talent. For some organizations, this team is a group of junior sourcers or senior coordinators building their experience/skills in Recruiting.
Application Closing Dates: Some teams have experimented with limiting the application window to reduce applicant volume (either by date or application volume). We see this often in markets like APAC. If you decide to do this, set a goal for applicant volume (by source) based on historical pass-through rates, so you understand what it takes to make a hike.
Better Job descriptions - Auditing and drafting clear job descriptions (and titles) that use standard terms, and specific qualifications that are optimized for discoverability and make it easy for a candidate to select in or out if qualified.

AI Tools for Application Review: What to Look For and What to Ask

There are many players in the space, some that focus only on app review only but many that have other offerings (full ATS functionality, sourcing, shared networks of candidates, etc).

Here are three things to consider when exploring AI-powered tools for application review:

Before any demos, work with your Recruiters (reverse shadow, etc) to understand how they review resumes today (current methods, best practices, limitations, etc), and what functionality/features would help expedite app review in an efficient and unbiased way (what filters would help narrow the pipeline, what experience is lacking for under-qualified talent, etc).
Understand the functionality (current and upcoming) of your current ATS. Leading ATS platforms like Ashby, Greenhouse, and Gem are rolling out new AI-powered resume review functionality this Fall. For instance, Ashby’s AI-Assisted Application Review feature, which went live this month, uses AI to automatically analyze resumes to determine if candidates meet the criteria you define for your jobs, which you can then filter on for review. Greenhouse also has a new App Review feature that uses AI to suggest keywords based on the job description to narrow down the applicant pool. Before you start exploring, be sure to understand what’s coming with your existing ATS. In some cases, the new functionality is free!
If you’re ready to consider new AI Tools, proceed thoughtfully & strategically! Adding new tools without creating redundancy or data silos is key. Before expanding, audit your existing tools and identify gaps that new technologies could fill. You’ll be surprised to discover that in this space you may find a 2 for 1 – for example sourcing & resume review functionality. And finally, it’s very important to partner with your Leadership Team and Legal Team to develop and discuss your approach to leveraging AI (for example: What is your philosophy? How will you guide hiring teams? What will you share with candidates? etc).

AI-Application Review Features/Functionality:

We reviewed 9+ different tools and here are some of the features we’re seeing:

Advanced filtering & Boolean: The ability to leverage filters and boolean to narrow down your applicant pool (years of experience, source, location, etc.). Our client’s have had great things to say about both Gem’s App Review and Ashby’s advanced filtering and search functionality.
Applicant Ranking or recommendations: Many tools offer functionality that ranks, sorts, or scores candidates based on how well they match predefined criteria. Some tools use a rating scale (1-4) like EightFold and Findem, others sort candidates into categories (those that are a strong match vs. not) (like Covey and Ashby), and some assign a % score that the candidate satisfies the role criteria (Tofu and EndorsedAI). Most (if not all) tools share some form of an audit log on every candidate showing which criteria they meet or don’t meet.
Sophisticated role-criteria building: In order to rank or analyze candidates, most of these tools include some role-defining/criteria gathering functionality; some even include AI nudges for problematic or vague qualifications (i.e “software engineering experience” - VAGUE! Include specific technology, years of experience, etc). Specifically, Ashby’s new AI-Assisted Application Review has a “validation process” when building job criteria, that will look for ways to improve the criteria AND surface any potential bias. We were impressed with this feature!
Blind resume review: Tools, like Hirefly, and Greenhouse, that can anonymize candidate information during review.
Talent Rediscovery or Sourcing capabilities: Some tools go beyond just the applicant pool and will suggest passive talent (in and out of your ATS) based on your job criteria.
Bulk actions: The ability to easily manage applications with batch processing options.

What functionality/features should you prioritize or optimize for? Advanced Boolean, filters and the ability to bulk action are huge time savers and, in our opinion, are table stakes for consideration. If you have a Senior role that you're hiring for, having the ability to within a few clicks take action on candidates who don’t meet the minimum requirements for years of experience (location requirements, or responses via their application), could save significant time. Ranking and Recommendations help determine the order in which you review applicants, driving more efficiency, BUT there are pros/cons here depending on how the tools let you audit and/or guide how the ranking is taking place.

During our in-depth evaluation of various tools, we found Ashby to be quite compelling. It provides responsible AI safeguards alongside advanced and impactful capabilities, all in a single platform for the recruiting process.

Important Call-outs:

Optimizing and driving efficiency is great but let’s be very clear – AI should never be used to make hiring decisions; your Recruiting Team/Hiring Team should make a determination based on the candidate’s resume and experience. Instead, leverage tools to sort and surface qualified talent based on defined criteria that your hiring teams set.

When using AI Ranking or Recommendations, remember that the tool is only as calibrated as the criteria you set. Clearly and explicitly defining those requirements with the Hiring Team is imperative to running an effective selection process. We love to see tools that match based on clearly defined criteria – and push teams to do a better job scoping them!

Lastly, pricing (and the pricing models) will vary by tool – by user, by applicant volume, data set, etc. Again, they may also be free if included in your current ATS package (Ashby and Gem are great options if you’re looking for an ATS that has this more advanced functionality built in). Greenhouse offers an AI search for application review (suggested keywords based on the job description) and several filters, but doesn’t currently rank, score, or recommend candidates.

Questions to ask potential Vendors:

Choose your vendors thoughtfully as regulations are changing fast and technology is evolving. We recommend working with your HR and Legal Teams to ask key questions about data privacy and bias mitigation to ensure your AI tools align with your company values and meet regulatory requirements.

Here are a few questions to ask vendors:

How do you avoid bias?
What inputs drive your AI recommendations? Understanding how recommendations or rankings are made is key - is it by user activity? (rejected candidates, hired candidates, etc) User defined role requirements? Ideal profile? We know humans are biased and if the learning models update based on who we reject we risk perpetuating certain biases - like screening out talent from certain schools or companies.
Is there an audit log for rankings or recommendations?
How can users override / flag the model's recommendations? Does the system use unsupervised learning techniques, and if so, how does it ensure the patterns it discovers are relevant and unbiased? “Unsupervised learning” refers to when AI finds patterns in data without explicit guidance. For example, when used during application review, it could discover new criteria based on users' rejection patterns. While this can uncover valuable insights, it also risks perpetuating hidden biases if not carefully monitored.
What LLM do you use? Ensure the model is regularly updated to prevent bias.
Who else do you share data with and how do you make sure that they take the right measures to protect it? How do you prevent our data from being shared with other customers (both by design or accidentally)?
How often is data synced with the ATS? Real-time syncing ensures accurate and up-to-date recommendations.
What are some things you DO NOT recommend doing with the AI functionality you're providing?
What functionality does your product provide to support candidate rights of notification and opt-out?

It’s exciting to see the technology in this space and the impact it could have on Recruiting!

Our parting thought - always remember that every resume, every “application review” is a human; one who’s making an important life decision, or perhaps dealing with one that was made for them. As you review resumes, seek to understand your own biases and how to mitigate them. Unlike interviewing (where feedback is documented/captured and reviewed by peers), application review is often an un-audited (unsupervised) practice, with little accountability. If you have this responsibility, take it with heart.

--Mike, Adam, Jill & special thanks to GBD Talent Advisor, Holly Brown, for her contributions to this month's newsletter.

🗓️ Upcoming Events:

Academy: Resource Capacity Planning (October 8, 9:30-11:30am PT)
- Take a deep dive into headcount planning and capacity modeling and you will be given tools and templates to jump start planning for your company.
Recruiting Operations Conference: (October 24+25)
- Join your talent industry peers in Chicago for inspiring talks, hands-on workshops, and actionable insights – all designed to propel our industry forward together.
  - Register here for a 15% discount!
Academy: Leading a High Performing Talent Team (November 12th-21st, 9:30-11am PT)
- Gain insights into your leadership style, develop a clear team mission and vision, and acquire effective strategies to lead your team through growth and challenges, all while learning from experienced recruiting leaders and experts from rapid-growth companies.

🚀 September Leadership Moves in the Market:

Leah Thomas, former Director of Recruiting at Extend, joined CALSTART as Talent Acquisition Manager.
Teresa Tan-Vial, former Partner at Spire, joined Lambda as Head of Talent.
Reginald J. Williams, former Head of Tech Talent at Bain Capital Ventures, joined Sequoia Capital as Director, Head of US Early Talent.
Kristie Bryan, former Senior Director, Talent at Coinbase, joined Blackbird Labs as VP of People.
Miranda Kalinowski, former VP, Global Head of Recruitment at Meta, joined Stripe as Head of Global Recruiting.
Robyn (O’Brien) Watson, former Manager, Recruiting Enablement at Upstart, joined BioRender as Manager, Talent Operations.
Olivia Bentley, former Senior Recruiter at Pave, joined Abnormal Security as Senior GTM Recruiter.
Tess Schneider, former VP, People at Found, joined Thrive Capital as VP of People.
Heather Kern, former Head of Talent at Happy Hire, joined ASM as Head of Talent Acquisition for North America.
Amanda Roberts, former Talent Parter at L Catterton, joined Advance as Chief Talent Officer.
Lindsey Siegel, former Principal Technical Recruiter, joined LineLeap as Director of Talent Acquisition.
Laura Li, former Solutions Engineer/Recruiting Operations Consultant at Ashby, joined OpenAI’s Recruiting Operations team.
Marcin Hordyniec, former Senior Talent Acquisition Manager, Technology, joined Bumble Inc. as Talent Acquisition Partner - Data, AI & ML
Jennifer (Carberry) Ayala, former Director of Talent and Interim VP of People at Bubble, joined Distributional as Head of People and Talent.
Monica Althoff, former Senior Manager, AI & Robotics Talent Acquisition at Cruise, joined Ramp as Senior Manager, Recruiting.
Yuichi Kurosawa, former Talent at Neara, joined DFJ Growth’s Talent team.

Did we miss your career change? Let us know: hello@gbdtalent.com

📖 What We’re Reading & Listening to:

How to Succeed in Mr Beast production - example of clear culture and values
This Framework Will Change How You Think about Your Career - Shishir Mehrotra
Founder Mode - Paul Graham
Founder Mode: A sign you have yet to, or failed to, hire the right people and build the right culture - Amir Shevat
Brian Checky’s new playbook - Lenny’s Podcast
Despite AI enthusiasm, Workforce Index reveals workers aren’t yet unlocking its benefits - Slack / Salesforce
The Evolution of Sourcing in Talent Acquisition: A Back to the Future Moment - Talent Intelligence Collective
The Future of Recruiting 2024 - LinkedIn
Global Workforce Report: Top Talent is hard to find, harder to keep - Workday
An Org Design benchmarking tool for rapidly scaling startups - Index Ventures
Binstack: Making a maximal multi-dimensional decision - Jason Cohen, Founder, WP Engine
This bot is trained on Lenny's Newsletter, Lenny's Podcast, plus on some of his favorite online writing about product, growth, and career - Lennybot
The VC Talent Who’s Who - Cory Bolotsky

📚 Playbooks:

Your Guide to Evaluating and Selecting an ATS: A guide with helpful process frameworks, and evaluation templates to help you make an informed decision about selecting the ATS that is the best fit for your company.
Navigating an IPO: A guide to help you navigate your team through the IPO process. It includes tools and tactics to prepare your team, proactively communicate to pending candidates, and evolve your recruiting operations to support the shift in business.