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Three Compensation Nightmares Startups Can Avoid With Benchmarking

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Thanh Nguyen is co-founder and CEO of OpenCompon which pre-IPO startups rely to win and retain top talent, while strengthening the runway.

High-growth companies need lots of talented people to scale. To get that talent, they need to expand competitive offerings, without blowing their budget. Finding this balance requires strategy and is becoming increasingly important to venture capitalists inspect money burning with new research as funding slows.

Enter: compensation benchmarking.

Simply put, benchmarking compares the pay for a particular role at one organization to that of similar roles at other companies. Doing it right is much easier said than done. In this article, I’ll share three common compensation missteps, the financial and operational nightmares they cause, and how to avoid them with proper benchmarking.

Misstep 1: Select One-Size-Fits-All Compensation Data

Suppose an early stage startup decides to extend an offer to a technical candidate. The recruiter performs a Google search to find compensation data to inform the offer, and finally selects a data set. She doesn’t know, but only employees of public companies reported the data in the compensation survey. The recruiter and the hiring manager prepare a quote based on the figures.

What’s the problem? While the recruiter’s instinct to base the offer on data is good, the data she used was irrelevant to her early business. It was also not reliable because employees sometimes misrepresent salaries. As a result, the recruiter creates an offer that completely deviates from competitive benchmarks for the startup’s funding stage and industry, jeopardizing monthly burnout and the organization’s commitment to paying equity.

Not all comp benchmarking data is created equal. To confirm that your compensation dataset is competitive, make sure your data consists of three things:

• Relevant: Good comp data represents your industry, financial firm stats, and company size. Make sure you can analyze it by role, experience (years and/or skills), responsibility/business impact and location.

• Composite: While employee-reported data is often unverified, good data is verified by professionals and obtained through Human Resources Information Systems (HRIS).

• On time: Truly accurate comp data is updated in real time by HRIS integrations. Apart from real-time information, data should not be older than nine months or the correct age.

Misstep 2: Comparing apples to oranges

Let’s look at another scenario. A growing Web3 company decides to adjust compensation for its business development and partnerships department to pay the team more competitively. They use data from a compensation survey of e-commerce companies from the previous year. Several months after the new comp figures roll out, the team is facing attrition, with employees calling it pay.

Here’s what went wrong: Employers can’t assume that one job title is the same for all companies; title taxonomies are broad. The remuneration can differ greatly per sector, but also per company size and financing phase.

To avoid this fate, make sure your compensation benchmarks are comparing apples to apples. Raw data based on job title is not enough. It is critical to invest time and resources in normalizing your titles and leveling criteria to properly align with your underlying market data. This is critical to making your compensation process scalable.

Misstep 3: Acting without modeling hiring plans

At the beginning of this year Peloton and Glossier announced layoffs subsequent periods of strong growth. Their pace of recruitment had compromised the burn. Often times, companies find themselves in this position when they haven’t properly modeled various hiring scenarios, which Peleton’s CEO boldly admitted.

Salaries are the largest expense of companies, so even in an employee labor market like the one we find ourselves in, employers must balance competitive wages with what is financially sustainable for their company.

It goes without saying that it is essential to use competitive compensation data and benchmarks to inform your scenario models. When companies use competitive compensation benchmarks and modeling tools, they change the game. Without benchmarking, compensation is just a very expensive gamble.


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