Pave

?? Save the date: Total Rewards Live is back, and it’s bigger and better than before! Join us in San Francisco this September 9-10 for a day and a half of cutting-edge insights, trends, and best practices shaping the future of compensation. Don’t miss out—join the waitlist now to be the first to get updates on TRL 2025: https://lnkd.in/gVZCXMGX #Pave #Compensation #TotalRewards #TRL2025

Calling all Transform attendees! Our team is at Booth 339 until 5pm today, and we’re playing ‘The Pay is Right’. Ready to live out your Showcase Showdown fantasy? Come on dooown! Can't make it in person? Schedule a virtual meeting to speak with us soon: https://lnkd.in/gJkWK7jC #Pave #Compensation #HRTransform

?? The Pave team is excited to introduce Cycle Insights, a new set of features now available in our Compensation Planning tool! These enhanced dashboards and powerful visualizations transform cycle management into a data-driven and transparent process. With Cycle Insights, you can get a clear view of budget allocations, track people manager progress, understand bottlenecks at the department level, and much more. Discover the ways Cycle Insights can help you take charge of your merit cycles like never before: https://lnkd.in/gcRMbbgv #Pave #Compensation

As companies scale, sales teams get proportionally larger. Meanwhile, marketing teams get proportionally smaller. A common org chart pattern is that in the early days of PMF-hunting, a higher proportion of your company’s headcount likely lives in R&D. But as you scale, it’s common that you’ll begin hiring sales-and-marketing hires at a proportionally faster clip, and GTM thus begins to consume a larger part of the org chart. Let's go a bit deeper and look specifically at sales team vs marketing team sizes over time as a percentage of total headcount. And how might the trends highlighted below change as the latest Gen AI tooling continues to arm GTM teams with even more automated leverage? ____________ Results: ?????????????????? ?????????? ???????? ???? ?????? ???????????????????????????? ?????????????? ???? ?? ?????????????? ????????????. ??????????????????, ?????????? ?????????? ?????? ???????????????????????????? ????????????. Here are the median benchmarks (and see the attached chart for 25th and 75th percentiles): ? 51-100 Employees: 4.2% of company in Marketing, 10.6% in Sales? ? 101-200 Employees: 3.8% of company in Marketing, 11.4% in Sales? ? 201-500 Employees: 3.4% of company in Marketing, 12.2% in Sales? ? 501-1,000 Employees: 3.5% of company in Marketing, 12.4% in Sales? ? 1,001-3,000 Employees: 3.3% of company in Marketing, 14.2% in Sales? ? 3,001+ Employees: 2.6% of company in Marketing, 15.4% in Sales How does your company compare to the market median benchmarks? Are you over or understaffed in sales and marketing requisite with the median company for your company stage bucket? ____________ Methodology: Data source: 2,914 Pave customers with more than 50 employees. Pave’s data science team grouped the following job families into the “sales” distinction: 'Sales - Generalist', 'Sales - New Business', 'Sales - Strategic Accounts', 'Account Management - Generalist', 'Sales Development', 'Business Development - Generalist', 'Sales Engineering', 'Implementation', 'Solutions Engineering - Post-Sales', 'Solutions Engineering', 'Customer Success', 'Relationship Management', 'Sales Operations - Generalist', 'Deal Desk Management', 'Sales Enablement' And the following job families into the “marketing” distinction:?'Marketing', 'Communications and PR', 'Product Marketing', 'Growth Marketing', 'Community Operations', 'Copywriting', 'Media Production', 'Graphic Design', 'Brand Marketing', 'Content Marketing’ #pave #sales #marketing #orgchart #benchmarks

Compensation for software engineers varies by company stage... but how much exactly? Explore the latest findings from Matt and the Pave data team:

At Pave, our data team fields tons of questions about org charts. Things like: How big should my R&D team be? How many HRBPs should I have on staff? How many direct reports should our CEO have? To answer these common questions, we dug into our dataset and shared the findings on our blog. Ready to dive into some of the most popular org chart benchmarks and see how your company stacks up? Explore this helpful roundup: https://lnkd.in/guamr8Kb #Pave #Compensation

This Employee Appreciation Day, we have to give a big shoutout to all Pavers, the best team in the game! Pave is made up of an all-star group of smart, talented people who play to win and put the customer first. We’re really feeling that hug of jawn today (iykyk). If you want to be a part of the magic happening here, check out our open roles: https://lnkd.in/gmJza_Nv ?? #Pave #Compensation

?? Did you know? Pave customers save roughly 17 days between their first and second merit cycles with Pave's Compensation Planner. So whether you're finishing a cycle, or gearing up for the next one, we're here to help make merit cycles FASTER. ? Our team will be at Transform in Vegas at Booth #339, and we're ready to chat all things compensation. Grab some time with us: https://lnkd.in/gJkWK7jC

Transform is right around the corner! Don’t miss the chance to connect with the Pave team at booth #339. See our latest features in action, chat with our compensation experts, and maybe even win big! ?? Whether you’re refining pay structures or tackling new challenges, we’d love to meet you. ?? See you in Vegas! ?? ? #Pave #Compensation #HRTransform

Compa ratios of tenured employees vs. new hires: which R&D candidates have the most offer letter negotiation leverage? A couple weeks ago, we looked at the preferential treatment that candidates tend to get over employees as demonstrated by the compa differentials between candidates & employees. In short, we found that “New Hires” somewhat consistently have higher compa ratios than “Tenured Employees”, especially at the more senior IC levels. This is likely due in part to the fact that employees have the most negotiation leverage when they are candidates at the point of the offer letter. This “compa ratio disparity” between candidates and employees can also serve as a new assessment of market demand if you break down the results by job family. Let’s take a look. _______________ The Results: Our data science team took a look at compa ratio distributions for companies in Pave’s dataset where we have access to reliable salary ranges. Results were filtered to only include P3 employees. Then, the results were broken down into two mutually exclusive groups: [A] “New Hires” – employees who joined within the last 12 months [B] “Tenured Employees” – all employees who have more than 12 months of tenure Lastly, the results were broken down by job family. And to keep things interesting for this post, we decided to only look at the prominent R&D job families. _______________ Takeaways: 1?? ????/???? ?????????????????????? ???????????? ?????????????????? ???? ???? ??????-????????. The median compa ratio for AI/ML engineers is 1.05 for “New Hires” and 0.97 for “Tenured Employees”. This “compa ratio disparity” is more pronounced than the other R&D job families analyzed and serves as yet another signal of the heightened market demand for AI/ML talent. 2?? ?????????????????? ???? ?????? ?????? ???????????????? ???????? ???????????????? ?????????????????????? ???????????????? ???????? ???????? ?????? ???????????????????? ???? ?????? ?????????? ???? ?????? ?????????? ????????????. Candidate leverage is somewhat present across the board but is most pronounced in the “hot” job families. 3?? ???????? ?????????? ?????????? ?????????? ???????????? ?????? ?????????????? ???? ???????? ?????? ????????????????????????? Offer letter practices vary. I have heard customers make strong cases for two approaches: [??] “??????-????-??????” (e.g. 0.85 to 1.0 compa ratio) => allows recruiters to anchor towards a lower range so that candidates do not feel mild demoralized for being placed so low in the range. [??] “??????-????-??????” (e.g. 0.85 to 1.15 compa ratio) => more transparent for candidates, highlighting more upward raise potential within the job level for employees who perform well over time. Which of the two approaches do you think is optimal? #pave #compa #benchmarks