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Benchmark Metrics to Improve Your Recruiting Funnel

Benchmark Metrics to Improve Your Recruiting Funnel

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Nischal V Chadaga
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December 17, 2024
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5 min read
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In a competitive job market, recruiting the right talent efficiently and effectively can set your organization apart. However, even with a streamlined hiring process, there’s always room for improvement. This is where benchmarking metrics for your recruiting funnel come into play. By evaluating the key stages of your recruitment process against industry standards, you can identify areas of improvement, optimize hiring strategies, and ultimately make better hiring decisions.

The recruiting funnel represents the journey a candidate takes from applying for a job to being hired. Just like any other business process, recruitment needs constant evaluation and optimization. By measuring the right metrics at each stage of the funnel, you can make data-driven decisions that improve your overall recruitment strategy.

In this blog, we’ll explore the benchmark metrics that can help you enhance each phase of the recruitment funnel, and how platforms like HackerEarth can support your efforts through skills-based evaluations.

1. Time to Fill

What it is:
Time to Fill is the metric that measures the total time taken to fill a job opening, starting from when the job is posted to when the candidate accepts the offer. It’s a critical metric because it helps identify how efficient your recruiting process is.

Why it’s important:
Long hiring timelines can be costly in terms of lost productivity and potential candidates accepting offers elsewhere. Shortening Time to Fill can reduce hiring costs and improve your employer brand, as candidates tend to prefer swift and responsive recruitment processes.

Industry benchmark:
The average Time to Fill can vary by industry, but for tech roles, it can range from 30 to 45 days. However, some top-performing companies manage to fill positions in as little as 14 days, especially when leveraging automated screening tools and skill-based assessments.

How HackerEarth can help:
HackerEarth’s automated coding challenges and assessments allow you to quickly filter candidates based on their technical skills. With its pre-configured skills tests, your recruitment team can cut down on time-consuming manual screenings and accelerate the hiring process.

2. Quality of Hire

What it is:
Quality of Hire (QoH) is a metric used to measure the effectiveness of your hiring decisions. It evaluates how well new employees perform in their roles and how they contribute to the organization.

Why it’s important:
A high-quality hire leads to increased productivity, engagement, and retention, while a poor hire can lead to increased turnover, low morale, and wasted resources. Tracking QoH helps you ensure that you’re selecting candidates who not only meet the job requirements but also align with the company’s long-term goals.

Industry benchmark:
While there isn’t a standardized metric for QoH, it’s typically measured using performance evaluations, manager feedback, and retention rates. A 2021 study by the Society for Human Resource Management (SHRM) suggests that organizations with a well-defined recruitment strategy see an improvement of 30-40% in QoH.

How HackerEarth can help:
HackerEarth’s platform allows you to assess candidates’ skills through realistic coding challenges and problem-solving scenarios. By evaluating candidates based on skills rather than just resumes, you can ensure that you’re hiring individuals who have proven their abilities and are more likely to excel in the role.

3. Candidate Conversion Rate

What it is:
The candidate conversion rate is the percentage of applicants who move from one stage of the hiring process to the next, such as from application to interview, or from interview to offer. This metric helps you understand how effective each stage of your recruitment funnel is at advancing candidates.

Why it’s important:
A low conversion rate may signal inefficiencies or challenges in your recruiting process. It may also indicate that you’re attracting the wrong type of candidates, or that candidates are dropping off due to poor candidate experience.

Industry benchmark:
Typically, the conversion rate from initial application to interview should be around 30%, and from interview to offer around 50%. However, these numbers can vary based on the type of role and the quality of your candidate pool.

How HackerEarth can help:
By utilizing HackerEarth’s pre-employment skill assessments, you can ensure that the candidates moving forward in the process are truly qualified. This helps increase your conversion rate by automatically filtering out candidates who may not have the necessary skills, improving the overall quality of those advancing through the funnel.

4. Offer Acceptance Rate

What it is:
Offer Acceptance Rate measures the percentage of candidates who accept a job offer after it has been extended. This metric is crucial because even after investing time and resources into hiring, there’s always the risk that candidates will decline your offer.

Why it’s important:
A low acceptance rate can indicate that candidates are not interested in the role, the company, or the compensation package. This can often be addressed by improving the candidate experience or offering a more competitive package.

Industry benchmark:
On average, offer acceptance rates hover around 85-90%. However, for tech roles, especially those in high demand, the acceptance rate can vary. Companies with strong employer brands and competitive offers tend to have higher acceptance rates.

How HackerEarth can help:
HackerEarth can help reduce this risk by providing a positive, skill-based assessment experience for candidates. Candidates who are assessed based on their skills are more likely to view the recruitment process as transparent and fair, improving their overall experience and increasing the likelihood of accepting an offer.

5. Cost per Hire

What it is:
Cost per Hire (CPH) measures the total cost incurred during the hiring process, including advertising, agency fees, recruiter salaries, technology, and assessment tools. It’s an essential metric to evaluate the financial efficiency of your recruitment process.

Why it’s important:
Understanding the true cost of hiring helps you optimize your recruitment budget and allocate resources effectively. Reducing unnecessary costs without sacrificing the quality of hires is key to scaling the recruitment function.

Industry benchmark:
On average, the cost per hire in the tech industry can range from $3,000 to $4,000, but high-performing companies using automation and skills-based assessments may lower this cost by up to 30%.

How HackerEarth can help:
By using HackerEarth’s automated skill-based assessments, you can reduce the manual effort involved in the hiring process, cutting down on recruiter time and associated costs. You can also eliminate the need for costly third-party recruitment services and job board advertisements.

6. Time to Hire

What it is:
Time to Hire refers to the total time taken from when a candidate is first contacted to when they accept the job offer. It is a vital metric that reflects the speed and efficiency of your hiring process.

Why it’s important:
A longer Time to Hire often indicates inefficiency in the recruitment funnel, which could lead to losing top candidates to competitors. Shortening this timeline improves the candidate experience and ensures you secure talent before they are hired elsewhere.

Industry benchmark:
For tech roles, Time to Hire can average anywhere from 25 to 40 days, but with streamlined processes, some companies can hire within two weeks.

How HackerEarth can help:
HackerEarth enables recruiters to quickly screen candidates and assess technical proficiency using coding challenges. With its automated workflows, recruiters can significantly reduce Time to Hire, accelerating the recruitment process and ensuring top candidates don’t slip through the cracks.

How HackerEarth helps tech organizations optimize their recruitment funnel

By integrating HackerEarth’s skills assessments and coding challenges into your recruitment funnel, you can increase the efficiency of every stage. From the initial application to the final offer, HackerEarth’s platform helps companies evaluate technical expertise quickly, accurately, and fairly.

With features like automated assessments, coding challenges, and hackathons, HackerEarth ensures that recruiters are making data-driven decisions, reducing time-to-hire, and improving the quality of hires. The platform’s skill-first approach to recruitment helps companies filter out unqualified candidates early in the process, focusing on what really matters—skills and potential.

Conclusion

Optimizing your recruiting funnel is essential for staying competitive in today’s talent market. By tracking key metrics like Time to Fill, Quality of Hire, Candidate Conversion Rate, Offer Acceptance Rate, and Cost per Hire, you can identify areas for improvement and refine your strategy. Tools like HackerEarth make it easier to implement data-driven assessments at every stage of the funnel, ensuring that you hire the right candidates quickly and efficiently. With the right metrics and the right technology, your recruitment process can be faster, smarter, and more effective.

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Author
Nischal V Chadaga
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December 17, 2024
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5 min read
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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