Yummly, an AI-driven recipe app, prides itself in being the “smartest and most helpful food platform in existence.”
When Head of People, Stephanie Tate, joined the company, she inherited tedious manual compensation processes – which are unfortunately all-too-common at companies large and small.
“Typically, companies receive compensation reports from stale surveys twice a year, look at the job codes, and then manually enter them into a spreadsheet,” she says. “You cobble together something that doesn’t really fit your industry, pay ranges, or location – in a tool that’s highly prone to manual error.” “Then,” she says, “ you are left with a plan that’s more abstract art than carefully measured science… a dangerous way to manage a company’s largest source of spend.”
“Compensation planning has always been a painful and manual process. You know the whole time that you may be wrong, everything is just a thumb-in-the-wind best guess, and you wonder, ‘Am I in 1990?”
Tate decided she’d had enough frustration, mistakes, and 16-hour days. In search of a better, faster, and more reliable compensation process, she pursued a partner that could help Yummly streamline and efficiently deliver comp data to the managers and recruiters who used it. OpenComp continuously came out on top because of the breadth and depth of its features and capabilities.
“Customer service is everything to me, and the fact that the tool is so easy to use – there’s just no comparison. As I was setting things up in OpenComp, I got a little giddy. I knew right away that things were about to change.”
Yummly worked with OpenComp’s platform and support team to reinvent Yummly’s compensation processes and create a streamlined and affordable pay strategy for Yummly’s distributed workforce.
After connecting their HRIS to import their employee comp data, Tate and team used OpenComp to benchmark the raw employee data with over 95% accuracy in just minutes, which gave them an immediate, precise view of how Yummy’s compensation compares to peers.
Having saved weeks of work by eliminating the need for surveys and spreadsheets, Tate used OpenComp’s predictive analytics and multi-scenario modeling to learn the implications of various salary ranges and geographic pay strategies.
Then, when it was time to build and implement salary ranges, Tate and LaBranche could do in a few clicks what once took weeks or months – all with far more confidence.
“By making scenario modeling and salary ranges instantaneous, OpenComp saved one person months of work. I’ll never worry again about whether something is right or whether a job code matches.”
With a new accessible, aggregated pay strategy in place, Yummly is able to position recruiters and hiring managers to make pay decisions that are informed, rather than ‘best guesses’ based on stale comp survey data.
Finance also benefits from the new compensation consistency and clarity, with hyper-accurate salary ranges they can use in headcount planning.
“Finance is over the moon in terms of tracking burn and headcount planning. If she needs to hire four engineers and I’ve used OpenComp to forecast the salary range for each, then she doesn’t have to do a lot on her side. I can just give her that comp data.”
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