Best Practices for Achieving Talent Success Maturity
Pipeline Analysis to Optimize Your Recruiting Process
With insight from
Matthew’s Take
Associate Professor of Business Administration at St. Norbert College
Matthew J. Stollak teaches courses that cover all aspects of human resources. Dr. Stollak’s articles have been published widely, and his academic interests include the impact of spirituality on job satisfaction, absenteeism, stress at work, and measuring the effectiveness of employee assistance programs. Dr. Stollak is active within the Society for Human Resource Management, serving at the local, regional, and national level, including serving as the advisor to the award-winning St. Norbert College SHRM student chapter. His blog, “True Faith HR,” has been well received, and has made numerous top-10 and top-25 lists. He also serves as a contributor to the Talent Advisor Portal at CareerBuilder’s The Hiring Site. He can be reached on Twitter at @akabruno.
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Who it’s for:
HR managers, recruiters -
What you’ll get:
Practical tips for analyzing your recruiting process -
Why you need it:
To efficiently analyze and improve your recruiting process -
When it applies in the talent success process:
Job posting through hiring
The goal of an effective recruiting process should be to maximize the number of A-players joining your company. Just as important as sourcing and attracting A-players is making sure that once you find them, your follow-up processes for screening, interviewing, and making offers to them all support that goal. Pipeline analysis offers a simple but powerful way to identify the obstacles in that process.
Pipeline Analysis: Recruiting as Sales
Talent pipelining takes a page from sales management and the funnel model of sales forecasting.
Sales and recruiting both:
- Start with a large pool (prospects for sales and applicants for recruiting)
- Screen those down to a smaller pool (qualified opportunities for sales and qualified candidates for recruiting)
- Propose to the most promising (proposals to buyers and offer letters to candidates)
- Celebrate wins (closed deals for sales and accepted offers for recruiting)
Sales and marketing leaders monitor their funnels on a constant basis, often daily, and they form an essential tool for planning budgets and headcount needs, and for forecasting results. Because recruiting is ultimately a form of sales, this proven approach is a best practice for increasing your company’s ability to hire the largest number of A-players.
For recruiting, using the funnel process to conduct pipeline analytics represents a shift from being reactive (filling an open role now) to being proactive.
Pipeline analysis will show you:
- Which candidate sources are the most productive
- How long it takes you to move qualified candidates through your funnel
- Which steps in your process — or which managers — create bottlenecks that cost you A-players
- Where the biggest opportunities are to improve time to hire, cost of hire, and quality of hire
Pipeline analysis is understanding that your entire recruiting process is a funnel and that you can apply some key methods for analyzing what’s performing well and what in the process may need some work.
Matthew’s Take
“Always Be Closing” has to become today’s mantra for HR/recruiting. With low unemployment, and significant competition for talent, it is crucial for recruiting professionals to identify quickly what the top candidates desire and make a convincing case to top brass about what it will take to bring them on board. As a result, choosing the right metrics will be crucial. While “time to fill” is easy to measure, top candidates may have already moved on by the time it takes you to circumnavigate your bureaucratic red tape. At the most basic level, however, are yield ratios demonstrating the number of candidates who passed through each successive stage of the selection process. This simple math can help you identify where in the process the organization is succeeding, and where problems need to be met. I’d also argue that an important, and often ignored, focus in on developing the internal talent pipeline. How well is the organization developing and keeping its top performers?
—Matthew Stollak, Associate Professor of Business Administration at St. Norbert College
The Talent Pipeline Stages
Talent pipeline processes only work successfully when you’re collecting and analyzing relevant data. The first step to getting relevant data is to understand the different stages of the hiring process and why each stage matters.
The Seven Stages of Pipeline Analysis
The process of creating a new workforce headcount is directly tied to budgeting. Later in this article, we discuss the circular relationship between budgeting and building an effective talent pipeline.
After you’ve identified a role that needs to be filled (because you’ve created a new headcount or organizational chart, or because an employee has left or was terminated), you can move to creating the job description and sourcing — the first of stage in the recruiting funnel and pipeline analysis.
After the new headcount or position is approved, the hiring process typically has seven stages:
- Job posting
- Sourcing applicants
- Collecting applications
- Initial candidate screenings
- Interviews with HR, hiring managers, and other relevant parties
- Offer extended
- Offer accepted
All the collection and interpretation of pipeline data that we’ll be discussing in the next sections of this article will come out of these seven stages of the recruiting process. You need to be gathering data for each stage.
What data should you gather? We’ll now walk through that process step by step.
Talent Pipelines Begin with Data
Sales and other functions have long been driven by data, and now HR is beginning to emerge as a data center in competitive organizations. And data is the essence of talent pipeline analysis. This page tells you what data is most beneficial for pipeline analysis and how to interpret that data.
Start with Capturing Your Data
Just as your sales funnel is driven by data about customers and leads, your talent pipeline will be driven by data about your candidates — how they move through the hiring process you’ve set up, and who’s ultimately hired. Talent pipeline analysis is predominantly an effort to apply more data science to what are often subjective hiring models. This begins with capturing data.
The Data You Need and How to Analyze It
The focal point of talent pipeline data is connected to two questions:
- How many applicants reached this stage of the process?
- How long did it take to get them there?
Often, though, talent pipeline data is boiled down to a single number: time to hire. According to research by the recruiting website Glassdoor and reported in The New York Times, the average time to hire in 2009 in the United States was 12 days. By 2013, time to hire had doubled to 24 days, from initial contact to hire. Read the full article.
The problem: Time-to-hire statistics alone don’t tell you why it takes a certain number of days to hire for a given role, or even if you’re making good hiring decisions. To design a process that meets requirements for time and quality of hire, you need a more granular view.
While not strictly required, a good applicant tracking system (ATS) makes pipeline analysis much more practical by automating the collection of process data and generating relevant reports. If you’re evaluating ATS options, make sure the system is capable of generating these metrics. Even if you’re not planning to use them right away, this will ensure that the data is there and usable for the future.
Time to Hire by Position
Data to Gather and How to Interpret It
This is the amount of time it takes every open position to move from “posting the job” to “offer accepted.” This data is measured at the “offer accepted” stage.
Look for positions that are taking the longest (and shortest) to fill. See if you can ascertain why. This is a good metric to pair with …
Time to Hire by Manager
Data to Gather and How to Interpret It
The amount of time a position is open grouped by the primary hiring manager for that position. If a specific manager has two to three open positions, average the time for those hires. This data is also measured at the “offer accepted” stage.
Look for managers whose time to hire is significantly longer or shorter than their peers. Once you have employee data on those hires (once a few reviews have occurred), you can begin to determine whether quick hiring processes led to poor hires and whether drawn-out hiring processes led to stellar hires. You can also begin to see which of your hiring managers might be subject to “analysis paralysis” when they have available headcount.
Average Length of Stage by Position
Data to Gather and How to Interpret It
For each open position, track the number of workdays that a candidate spends at each stage. This data is measured by looking at each stage and the time a candidate remains there before advancing or being funneled out of your hiring process.
These data points allow you to see where in the pipeline the process is slowing down, specific to each open position. You might notice that sales candidates are slowing in the interview stages, or that operations candidates take a long time from offer to acceptance. This allows you to figure out what areas need to be sped up. Remember: The candidate is probably exploring other opportunities as well. When your process slows down, you lose A-players.
Conversion Rates, Stage to Stage
Data to Gather and How to Interpret It
Think of this as how many people get from stage “N” (any stage in the recruiting process) to “N+1” (the next stage in the process). This data can then be measured at every stage.
This data is crucial because it shows you how many candidates are needed in the overall talent pipeline to make an effective hire. If you realize that only X percent of your candidates get from one identified stage to the next for a role, you can determine how many people you need to source to make a superstar hire for that role.
Overall Abandonment Rate
Data to Gather and How to Interpret It
This tells you how many candidates, across all positions, self-selected out of your process any given stage. You should measure this data at every stage.
You often see the term “abandonment rate” in e-commerce (people that leave a website after viewing it), but you see it much less in hiring. It’s a crucial metric because it shows you how elaborate your processes are. A-players will have competing offers. They most likely want to start talking with human beings about the job and their fit for the role. If they’re spending too much time filling out application information or waiting to hear back from you, they may abandon the process.
Abandonment Rate at the Application Collection Stage
Data to Gather and How to Interpret It
This data is only measured at the application collection stage. This can show you if your initial process and form are too complicated or time-consuming for candidates.
Abandonment Rate by Stage
Data to Gather and How to Interpret It
Across all positions, look at each stage of your hiring process. Which stages have the highest self-selected exits? This data is measured at each stage. What information is being presented to candidates at each stage prior to them self-selecting out of your hiring process? How could that be improved upon?
Interviews-to-offers Ratio
Data to Gather and How to Interpret It
For each position, how many interviews were done before selecting the candidates to make offers to? This data is determined by the numbers at Stage 5 (interviews) and Stage 6 (offers extended) in your funnel.
This is a common recruiting metric. It’s a measure of hiring manager time and (potentially) the effectiveness of the screening process.
Pipeline Analysis: One Sample
Pipeline analysis can be a big task, but getting started is easier if you have a sample to work from.
On the current pipeline below, you’ll see new candidates, in-review candidates, interviews, and offers.
- In the “Group By” column, this sample is sorted by hiring manager. Most pipelines will allow you to sort by other factors based on what’s captured in your ATS.
- The pie chart at the top left of the sample breaks down the “offer” level of your pipeline in terms of accepted, pending, reference checks, and more. You can typically sort this information by time (pending to accepted) and other facets, as well.
