Ops in the Modern Enterprise
“What, exactly, is Operations?” I asked myself when applying to some generic Operations Manager job posting on the Uber website. After scouring JDs and Googling around, I came to one conclusion: “Uh, I guess it covers… everything?”
Needless to say, it was not particularly useful.
Yet 4 years later, I’d brought Uber products to-market in 24 cities and 7 countries across Asia, LatAm, and the US. My teammates, Josh and Lindsey, built sales planning, operating models, and processes to support a 300-seller, $6B GMV Uber Eats business. We saw firsthand the power of a well-oiled Ops Machine.
At Uber, Ops innovation outpaced Product. To put it simply, Ops teams could solve problems fast. They could interpret user signals quickly and work at breakneck speed to maneuver what was required to grow new SKUs or markets. A few examples:
- Local restaurant Ops teams physically arrived at restaurants, armed with just an iPad, to manually onboard them to the new Uber Eats platform. Throughout this process, they cycled through wildly different iterations to refine and accelerate onboardings from 1 restaurant per day to 1,000 locations per day.
- Sales Ops teams carved out territories, crafted segmentation models, and designed compensation incentives. Each week, they monitored if these were achieving acquisition, churn, and unit economic goals. If not, they went back to the drawing board to re-design. This cycle of design, implement, and measure is what built the foundation of a humming revenue engine that moved from $1M to billions in GMV with minimal leakage.
- City Ops teams owned pulling the optimal levers for growth for their market. Oftentimes this meant replicating tried and true strategies based on market maturity (such as revising centralized marketing with local languages during the growth stage). But it also meant creating new ones, then scaling these strategies to other cities when successful (like using a splashy promotion like Uber Ice Cream at launch). Ultimately this combination developed a sturdier channel strategy and an execution playbook with proven ROI.
In that same time period, we saw the rise of other Ops-centric companies with the likes of Doordash, Amazon, Airbnb, Opendoor, and more. These Ops teams all applied systems-thinking to find creative solutions for a vast range of business problems. Airbnb onboarded early hosts manually using a 12-point checklist. Doordash’s NYC business was unprofitable until they focused operational efforts on the suburbs. In almost all cases, Ops learnings that were proven successful were implemented in products at a grander scale.
The success of Ops impact on tech is no surprise. As a founding team, we have a deep respect for the core operational principles governing our world. Ops has been the underpinning of the physical supply chain for centuries. It’s how goods and services are produced, assembled, and transported across all corners of the globe. And it’s now morphing to accommodate the modern, data-driven enterprise.
So, What is Operations?
Look around you – Ops teams are becoming increasingly prevalent across modern, data-driven companies. You’ve probably noticed the rise of “Business Operations” and various “x” Ops roles (People Ops, Product Ops, Revenue Ops, and more). Everyone from data experts to VCs to notable startups have thrown in their 2 cents. Yet, despite their growing popularity, there is still a poor understanding of what Ops teams do and what precisely is the value they drive.
Which brings us back to the original question: “What, exactly, is Operations?”
Operations is a transformative function that focuses on designing, implementing, and maintaining a self-scaling system as efficiently as possible. They take a company’s resources (people, software, physical assets) and turn them into repeatable rhythms to what’s needed to achieve high priority objectives — whether that’s revenue flywheels, smooth functioning supply chains, or error-free factory floors.
Think about the core infrastructure of your house – plumbing, lighting, heat. If we were to guess, you probably never think about any of those things until they stop working. Ops is that infrastructure. They keep the lights on and the toilet working. They have tried and true solutions that they can pull out of their pocket whenever needed. But they do more than just maintenance. In a modern enterprise, Ops is not a plumber or an electrician but a tinkerer. The one that figures out how to make stuff “work” in new, exciting, and creative ways, then scales their learnings to be turned into standard practice.
A Primer on RevOps
What does this actually mean in practice? Let’s look at Revenue Operations. The generally accepted definition of RevOps is that they align sales, marketing, and customer success along a unified strategy.
We don’t disagree with that. But let’s get more specific. RevOps teams design, implement, and maintain the revenue generation engine. This should not be confused with handling ad hoc data requests, or chasing down an accurate sales roster. If these tasks are not done in service of the system as a whole… well, then they aren’t an Ops job.
Oftentimes these are distractions from their core responsibilities: understanding their company’s revenue goals, the means by which they hope to achieve those goals (people, tools, technology), and how to bring these disparate resources together.
Example: Let’s take ad hoc data questions. RevOps teams often field ad hoc data requests from leadership, managers, and other functional teams. It’s a flurry of “hey, quick question” pings on Slack to get the perfect snapshot of data to fit a chapter of the narrative. Or demands fixated around a few records in Salesforce that aren’t unique or squeaky clean. When these questions arise, RevOps teams should be focused on identifying a systematic solution. Such as building and maintaining self-serve dashboards for the most common reporting requests. Then as new requests come up, triaging appropriately and collaborating with data and engineering to provide business context. Or if there are questions around duplicate records, maintaining a hierarchy of sources and setting up an escalation process to handle requests.
This is when RevOps teams operate at their best – when they are empowered as mechanics, maniacally focused on designing and redesigning an ever smoother, ever more predictable revenue engine.
Investing in RevOps
Ten years ago, the function of RevOps didn’t even exist. Now Revenue Operations is the US’s fastest-growing job. Orgs that deploy RevOps reduce go-to-market expenses by 30% and grow revenue 3x faster than those without. Public companies with RevOps also have 71% higher stock performance on average.
Yet despite the proven success of investing in RevOps infrastructure, companies misunderstand or underestimate this importance. Let’s be frank: you can’t grow revenue in today’s macro climate unless you build sturdy, underlying systems. But more often than not, RevOps are being relegated to “Salesforce admins” or “the annoying people who ask you to update your OKRs” — rather than equipping them with the proper tools to maximize outcomes they could be driving.
In fact, when companies don’t set RevOps teams up for success, then the traits we value in great Ops — scrappiness, thriving-in-ambiguity, creativity — lead to bad, leaky, non-scalable GTM systems. This causes far more harm than good.
We believe we can change that narrative.
At Punchcard, we believe RevOps teams should have more time for strategic work, direct answers to questions, and data that reflects operational reality.
Enter Punchcard 1.0, a novel data exploration tool that gives B2B revenue teams superpowers. Our application is built for those who want to take swift action on the most impactful levers that grow revenue. Using powerful data transformation, LLMs, and AI-assisted search, Punchcard gives everyone the skills of a seasoned data professional.
Here’s what you can expect:
- Seamless integration with your existing software stack (Salesforce, Gong, and more)
- Out-of-the-box reports for pipeline history, stage-to-stage conversion, rep productivity, and more
- Novel insights, such as loss & churn intelligence from 1,000s of customer conversations
Our early customers have increased seller productivity by 150%, driven larger deals through identifying the highest quality opportunities, and eliminated a weeks-long data assembly project.
We still have a few spots open in our private beta this summer! We work best with high-growth B2B companies with 20+ sellers, ambitious targets, and who care about high quality, data-driven decisions. If interested, shoot us a note at firstname.lastname@example.org.
Special thanks to Bec Hu (Channeled), Wensheen Tong (AtoB), Gracie Kneapler (Figma), Sabina Del Rosso (Mill), Liz Chanin (Monte Carlo), Ashna Reddy (Opendoor) and Kenneth Luu (Instawork) for their thoughts and feedback.