Data-Driven Technical Hiring Decisions: Improve the Rate of Offer Acceptance, Retention and Payroll Budget Management

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Data-Driven Technical Hiring Decisions: Improve the Rate of Offer Acceptance, Retention and Payroll Budget Management

When a search to fill a technical position has resulted in finding the candidate that fits your firm’s skill requirements and culture, how can you be sure your offer will effectively compensate this qualified candidate? Offer too little, and you will either lose the candidate or de-motivate the new associate or your firm will be vulnerable to a relentlessly demanding technical labor market. Pay too much, and your budget is under pressure. If this decision is handled poorly, it may also introduce salary compression issues that de-stabilize the existing team members. 

Designing a Targeted Annual Earnings (TAE) model for your technical hires requires advanced planning and access to accurate labor market data. Optimally, at the point of candidate offer, this information should be evaluated in a three-part comparison: (1) your TAE draft offer, (2) the labor market TAE for roles and responsibilities with similar experience levels and (3) the candidate’s current compensation. 

How do you identify an accurate technical labor market compensation analysis? 

The best available information only considers data consistently responded to in a manner that ensures field-level input discipline. The best data for technical salary comparisons does not find its source from the social media active candidates. 

Instead, the best sources are firms who are hiring technically skilled professionals. These companies are under contract to provide their human resource data to companies that analyze the data. Their responses are required in a format that ensures high-quality data analysis can be applied. These contributing firms are motivated to do this because they can subscribe to the invaluable results. 

By sharing their compensation data, the participating firms have access to reliable high quality information made more meaningful by the thousands of HR department responses. This salary information has the value of the law of large numbers, which is in turn analyzed and presented by professional statisticians in a format that is easily consumed by participating firms. As a result, hiring departments have accurate access to market-based TAE by:

  • Specific technical skills and disciplines required to perform the role
  • Years of previous experience exercising these specific technical skills and disciplines
  • Geographic region of employment
  • Size of company

To statisticians, garbage in means garbage out. Accurate labor market compensation data cannot be derived from a collection of inputs with no data-field level disciplines, volunteered to a salary website by recently hired candidates. Inconsistently gathered information will result in information with a wide confidence interval ensuring little to no value. 

A data-driven TAE decision

Ensuring you have access to accurate labor market data is only the first step. Next you need to make sure your firm’s compensation package is aligned with the labor market in your region for similarly skilled roles and experience levels. The targeted alignment to achieve success should range between the 50th and 75th percentile, depending on local market conditions and other compensation package variables. This is why a nuanced understanding of your firm’s TAE for this role is necessary. Understanding the compensation package details across many categories of compensation types, including the numerous variables within each of these compensation categories is an advantage. In the TAE detail is a differentiation argument that can be made relative to the candidate’s current earnings profile. The value of understanding this information is that your firm’s offer, reinforced by accurate labor market data, can be effectively compared to the candidate’s current situation by your hiring team. 

Some compensation-related benefits are more monetizable by one candidate than another. Knowing the interest of your candidates will help you structure the most effective discussion. Clearly, the monetizable benefits of your offer can be made more transparent to the candidate and emphasized, such as the impact of the commute, PTO differences, 401(k) match advantages and tuition support. Often overlooked advantages, such as employer-side payroll tax contributions can be identified and pointed out to a candidate also reviewing a 1099 contractor role. Surprisingly, this is the kind of information that can be easily overlooked by candidates when comparing your offer to a 1099 offer that sounds better on the surface. When the math is demonstrated, it helps the candidate’s decision.

Making the Analysis Easy

A data-driven TAE decision by a firm will be optimized with three-columns of information juxtaposed and compared prior to an offer: (1) accurate and reliable labor-market compensation survey data, (2) the compensation package that your firm has prototyped for the open position and (3) some clear understanding of the candidate’s expectations and details relative to the candidate’s current situation. Some of these columns of data can be even more useful with specific detail. The most useful labor market data will provide averages for similarly skilled roles and experience at minimum for the 25th, 50th and 75th percentile by location across many compensation variables. These percentile markers are critical because if your company’s proposed TAE plan lines up at the labor market’s 25th percentile, this indicates that 75 of 100 individuals with a similar skill and experience profile are paid better in the labor market than your offer to this candidate. That information should at least be understood and considered relative to the other advantages of your offer.

The column headings (or “X” axis) for the data-driven analysis are (1) your TAE draft offer, (2) the labor market TAE for roles and responsibilities with similar experience levels and (3) the candidate’s current compensation. The rows of data (or "Y" axis) to be collected and compared should include these seven categories of data:

  • Annual earnings, including bonus and profit sharing opportunities
  • Employer-side payroll taxes
  • PTO, including holiday and vacation policy
  • Health coverage
  • Retirement benefits
  • Education assistance
  • Commute 

These seven categories of comparison may yield as many as 28 specific variables.  

Knowing the candidate’s current situation and aspirations will help, especially if payroll stubs and a W-2 validate this information. By staying with their current employer, the odds are a candidate’s average TAE increase will be improved by three percent per year (Note: The average technical employee increase in FY15 was 2.9 percent). For purposes of TAE package decision making, assuming all things remaining equal, a six percent to 10 percent TAE increase is the probabilistic range necessary to motivate a candidate’s decision to change from their current employer and accept your offer. 

Gain significant advantage

Over the long term, a company with a winning strategy who also hires with quality will gain significant advantage over their competition. The goal is to present the offer to the candidate at the level that is appropriate to their value. Additionally, the offer needs to be supportive of the client’s need for a sustainable, motivated relationship. All of these requirements need to be managed in a manner that meets departmental budget requirements. Having meaningful data that is organized quickly is the most effective way to hire and retain key technical employees. Better information will help hiring managers navigate the decisions and achieve both company and candidate needs. 

About the Author:

TopLine Strategies delivers the complete integration and development of sales, marketing and customer service technologies that enable corporate clientele to improve revenue streams and strengthen customer interactions. Our project management and consulting is designed to achieve timely delivery, 100 percent user adoption of the technologies we implement and deliver measurable returns on investments for our clients.

Comments (1)

Priya replied on

Great blog..You have clearly explained about the concept..Step by step explanation is too good to understand..Its very useful for me to understand..Keep on sharing..

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