Common RevOps Pitfalls & What Good Looks Like

So, you’re ready to dive into RevOps but want to avoid the chaos? I feel you.

Most clients come to me after trying to piece things together, only to sink time and money into it with little to show for their efforts.

The good news is that the same pitfalls show up again and again. If you know what to watch for early on, you can save yourself a massive headache.

So, what are the biggest mistakes I see? Let’s break them down.

1. Your Processes Are Breaking Before They Begin

"Our data is a mess" – I hear this all the time. But here's the thing: messy data isn't the problem. It's a symptom of broken processes upstream.

Think about it this way: When a restaurant's kitchen is chaotic, the problem isn't the pile of dirty dishes—the lack of systems that caused them to pile up in the first place.

There is no prep schedule, cleaning rotation, or clear roles. The mess is just the visible outcome of deeper operational issues.

I am saying this as someone who worked in the restaurant industry for ten years.

The same applies to your RevOps setup. When marketing blames sales for not following up on leads and sales complain that the leads are junk, we're not looking at a data problem.

We're looking at misaligned processes and missing standards.

Here's what's happening upstream:

• Your forms aren't validating data at entry

• Sales reps are freestyling in how they input customer information

• No one owns data quality across the customer journey

• Teams are working in silos with their own "standards."

• Automation is either missing or making assumptions about data that doesn't exist

And downstream? That's where you see the symptoms everyone loves to complain about:

• Duplicate records clogging your pipeline

• Contact information that looks like it was typed by a cat walking across a keyboard

• Deal values that somehow include emojis

• Reports that make your CEO question everything

You'll never have perfect data – that's not the goal.

The goal is to have reliable data that predictably flows through your systems. That means:

• Clear ownership of data quality at each stage

• Standard operating procedures that teams follow

• Validation rules that catch issues before they enter your system

• Regular audits that identify process breakdowns, not just dirty data

• Training that focuses on why these processes matter, not just how to follow them

Otherwise, you're just playing an endless game of data cleanup. And trust me, that's a game you can't win.

2. You're Asking the Wrong Questions About Attribution

"Google Ads drive all our revenue!"

If I had a dollar for every time I heard that, I'd have enough to fund another Series A. The problem is not that the statement is wrong; we're asking the wrong questions about attribution in the first place.

Most companies obsess over which attribution model to use when they should be asking, "What decisions are we trying to make with this data?"

Think of it like a detective trying to solve a case. You wouldn't just look at where the crime started (first touch) or where it ended (last touch). You'd look at the whole story—motives, alibis, security footage, and witness statements. Your customer journey needs the same treatment.

Here's what's happening:

• Marketing celebrates the blog post that brought in the lead

• Sales takes credit for the demo that closed the deal

• Customer Success discovers an existing customer referred the prospect

• Meanwhile, your CEO just wants to know where to invest more money

And everyone's right – which means everyone's wrong.

The problem isn't that first-touch or last-touch attribution is broken. The problem is that we're trying to force a complex buyer journey into a simple model because it's convenient for our reporting.

Instead of fighting over which attribution model is "right," here's what works:

• Track multiple attribution models simultaneously (yes, all of them)

• Look for patterns across models rather than perfect attribution

• Build feedback loops with your customers (shocking idea: ask them about their buying journey)

• Create a source map that includes both trackable and untrackable touchpoints

• Accept that some influences will never show up in your data (like that conversation at a conference)

Will you ever have perfect attribution? Not unless Elon makes good on those Neuralink promises – and even then, good luck tracking word-of-mouth over neural networks. But perfect attribution isn't the goal. Making confident decisions about where to invest your resources.

This means being data-informed rather than data-obsessed. It means understanding that your attribution data is a compass, not a GPS. It points you in the right direction, but you must still use your brain to reach the destination.

The companies that get this right don't have the fanciest attribution models. They combine quantitative data with qualitative insights to build a complete picture of their customer journey. They ask "why" instead of just "where."

3. You Think More Tech = Better Results

More tools don’t fix broken processes. Most companies have too many tools that don’t talk to each other, aren’t correctly used, or create more problems than they solve.

Whenever there's a problem, someone suggests another tool to fix it. Before you know it, you're paying for fourteen platforms that all promise to be "the single source of truth."

Here's what your team probably sounds like right now:

• Sales: "We need Gong to track our calls!"

• Marketing: "This new tool will solve our landing page problems!"

• CS: "Can we get another dashboard tool? This one has better graphs!"

• Finance: "Why are we paying $50K/month for software nobody uses?"

Look, I get it. New technology is shiny and promises to solve all your problems.

But adding more tools to a broken process is like adding more cars to a traffic jam—it just makes the mess bigger and more expensive.

What's actually happening under the hood:

• Your CRM doesn't talk to your marketing automation

• Your sales team has given up and is using spreadsheets

• Half your integrations are held together with Zapier and prayers

• Nobody remembers the login for that analytics tool you bought last year

• Your tech stack diagram looks like a bowl of spaghetti (if you even have one)

The problem isn't that you need more technology. The problem is that you never stopped to ask, "What are we trying to achieve, and what's the simplest way to get there?"

Here's what good actually looks like:

• A clear system architecture that a fifth-grader could understand

• Documented ownership for every piece of the stack

• Regular audits of tool usage and ROI

• A strict "one in, one out" policy for new tools

• Integration requirements that are defined before, not after, purchase

Build a real system architecture diagram that shows how data flows through your business. Not the pretty version from marketing – the honest one that'll stop you from discovering at renewal time that you're paying triple for the same functionality.

The best tech stack isn't the one with the most tools—it's the one that allows your team to do their best work without wanting to throw their laptops out the window.

4. Your Adoption Strategy is an Afterthought

Buying software is easy. Getting people to use it is another story.

I see this all the time: Companies drop $20K on a new platform, send out a Slack announcement, schedule one training session (that half the team misses), and then wonder why adoption is in the single digits three months later.

Here's what's really happening:

• Your sales team lives in spreadsheets because "the CRM is too complicated."

• Marketing automated everything, but nobody knows how to fix it when it breaks

• New hires are piecing together processes from six different Google docs

• Your power users are drowning in support questions from their teammates

• Everyone's built their own "workarounds" that would make a developer cry

This isn't a training problem – it's a change management failure. Your fancy new tech isn't just another tool; it's asking people to change how they work fundamentally. And nobody likes change, especially when trying to hit their quarterly numbers.

What works:

• Build a training program that matches how people learn (hint: not through two-hour Zoom sessions)

• Create process documentation and use AI to help build out SOPs

• Create user stories that map jobs to be done to the technology you’re using

• Make your documentation findable, readable, and valuable – we use Supered.io

The goal isn't perfect adoption – it's making sure your expensive new tool doesn't turn into next quarter's "why did we buy this again?" conversation.

5. You’re Treating Symptoms, Not the Root Cause

Do you know what my first client conversation usually sounds like?

"Our data is a mess." "We need better audience segmentation." "We can't track our pipeline effectively."

It's like going to physical therapy for back pain when the real problem is tight hamstrings.

Everyone is so focused on where the pain is that they miss what's causing it.

Here's what's walking into my office:

• "Our data is a mess" = Your go-to-market strategy is unclear

• "We need better segmentation" = You never defined your ideal customer

• "Pipeline tracking is broken" = Your sales process doesn't match how customers buy

The real problem is that your entire revenue engine runs on assumptions from six months ago when you were half the size and selling to different customers.

It's like trying to fix a sports car's engine by changing the radio station. Sure, the music's clearer, but you're still not going to win the race.

The only way out is to step back and look at the whole machine:

• Map your actual customer journey (not the one in that fancy pitch deck)

• Track where leads get stuck (and why they really get stuck)

• Identify the handoffs that work (there are probably fewer than you think)

• Document the shortcuts your best performers are taking

• Figure out what data drives decisions

Because here's the truth: You don't have a data problem, a segmentation problem, or a pipeline problem. You have a system problem. And until you fix the system, you're just putting expensive bandaids on broken processes.

The Takeaway

RevOps isn’t just about aligning teams; it’s about building a system where decisions predictably impact revenue. And that doesn’t happen with another tool or a fancy (but misguided) dashboard. It happens through the unsexy stuff: process, training, and adoption.

These things tend to get pushed to the bottom of the priority list, but they make RevOps work.

Here's the uncomfortable truth: Most younger companies don't need a RevOps function (yet). They need to understand how their business makes money. Start by mapping that system and then build the processes to scale it.

Because if you're waiting until you have a mess to start thinking about RevOps, you've already lost.

You're not building an engine – you're just becoming efficient at cleaning up chaos.