Looking for revenue forecasting and RevOps best practices? David Ogden, VP of Revenue Operations at Omilia shares a few proven pointers in this SalesTechStar interview:
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How did you venture into RevOps, and what’s most exciting about working in this field today?
I moved into RevOps because the company I was working for at the time needed to scale its international operations fast and there wasn’t a playbook for it internally.
I’d had a good run as VP of EMEA Sales, but working for a US headquartered business made it obvious quite quickly that a GTM motion, a set of contracts and a regulatory approach built for the US doesn’t just translate to the UK, Europe & LATAM. Things like data privacy expectations, how contracts get negotiated and even how a sales cycle unfolds look different region to region. Someone needed to own that with an international lens rather than trying to force a US template to fit, and that’s what pulled me across.
What excites me about the role today is that RevOps hasn’t become less central, it’s become more so and AI is a big part of why. It takes a lot of the repetitive work off the table: chasing data, checking approvals, and flagging what’s slipping. That frees us up to spend more time helping shape the direction of the business rather than just being the department that enforces the rules.
What revenue operations and sales technology platforms do you rely on most to drive operational success?
There’s a myriad of tools available today compared to even a few years ago, and that’s not always a good thing. My approach is to work out where we need support first, then look at how best to fill that gap.
That thought process goes in the order: can we do it with what we already have; can we extend an existing tool to cover it; has someone in my network already solved this and can they point me to what worked for them? If the answer to many of these questions is no, only then do we begin looking at completely new tools. Boiling the ocean by evaluating every option on the market is a waste of time and resources when the answer is often sitting inside a tool you already pay for.
In practice, that covers everything from the core CRM through to conversation intelligence, data enrichment and contract or CPQ tooling. The question I ask about each is the same: does this genuinely close a capability gap or are we just buying a shinier version of something we already have?
I think we’re all a little guilty, at some point, of finding a reason to buy something new to plug into a gap. Unless you consider the whole stack, the integration and the ongoing support of a new solution, it can turn into a minefield fast.
How do you establish strong data governance and CRM hygiene across the organization? What best practices have proven most effective?
Data hygiene is the oldest problem in the RevOps and SalesOps playbook, and it hasn’t gone away just because the tooling has improved. Remote, dispersed sales teams who are busy trying to close deals will always put CRM hygiene lower down the list than we’d like, and that’s just realistic.
Where I’ve seen the tools genuinely earn their keep is by taking that burden off reps rather than just nagging them about it. Automated data entry, fields that stay current without anyone remembering to update them, and systems that flag or suggest what stage an opportunity or lead should actually be at — all of this reduces the manual load.
Validation gates between stages are table stakes at this point, but they’re still worth having properly enforced rather than treated as a formality. Just as important is having a way to spot opportunities and leads that are going stale, and getting an automated nudge to both the rep and their manager before it becomes a bigger problem.
I’d love to work somewhere where the CRM is 100% accurate and up to date all the time. That’s not realistic, so the aim must be the best outcome you can get, not a perfect one.
How do you create alignment between sales, marketing, and customer success to ensure everyone is working toward shared revenue goals?
Alignment starts with everyone being measured against the same number, not with a slide that says we’re all on one team. If marketing is measured by SQLs, sales on bookings and customer success on a satisfaction score, you’ve effectively built three separate businesses that happen to share a logo.
A RevOps leader I had the pleasure of working for would repeatedly say, “show me a result and I’ll show you the plan that drove it.” He usually said this about commissions, but I think it holds just as well across a whole GTM organization. The goal you set shapes the behavior you get. So, if the goal is different for each function, don’t be surprised when the behavior is too.
In practice, that can mean things like shared credit between sales and customer success on renewals and expansion, or marketing incentives tied to pipeline that converts rather than just volume of leads generated.
One thing I’d flag: I’ve seen goals set centrally far too often, usually built off US data, with an expectation they’ll work the same way everywhere. They don’t. Buying behavior, deal cycles and what “good” looks like all vary by region, so a regional lens on those shared goals matters more than people give it credit for.
What revenue forecasting methodology do you believe modern SaaS RevOps teams should adopt to navigate today’s business environment?
I don’t think any single forecasting method holds up well on its own, so I run a few in parallel and let them cross check each other rather than betting everything on one number.
The first is a day-of-quarter model. I take what’s already been won in the quarter, add a projection based on historical win rates weighted by ACV, and track that against where we’d expect to be on that specific day of the quarter based on past patterns, not just against the quarter-end target.
The second is a stage-based waterfall. Historical win rates by pipeline stage get applied to the current open pipeline, which gives a weighted view of what’s realistically likely to close rather than relying on what reps say is likely to close.
The third is a slippage model: tracking how much pipeline has historically been pushed out of the quarter by a given day of the month. That tells you how much to discount what’s sitting in the current quarter as the month goes on, rather than being surprised by push at the last minute.
Individually, large deals can move all three of these quite a bit on their own. My rule is to keep them out of the base case and call them out separately. State the core number with confidence, then flag that there’s $X of upside sitting in named deals that could move things if they land. That keeps the base forecast credible and the upside visible, without inflating the number you’re meant to be able to stand behind.
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How are you using AI across revenue operations, and where have you seen it deliver the greatest impact over the past few years?
AI is stitched into pretty much everything we do in RevOps now, and I don’t think its main value proposition is speed. Its real value lies in surfacing what we need to be paying attention to, things a person managing a full pipeline and a full team would otherwise miss.
Here are a few concrete examples. We use it to flag risk inside live opportunities and risk of customer churn, so reps and leaders are looking at the deals and accounts that need attention rather than just the loudest ones. It helps keep CRM data current by doing the unglamorous, repetitive work of chasing and validating fields, which ties back to the data hygiene point earlier.
I’ve built AI-driven skills that automate the creation of compensation plans and standardize the terms and structures across roles, so our approach to making a new hire is consistent and compliant from day one instead of being redrafted from scratch each time. We’ve applied the same thinking to commercial paperwork, keeping templates consistent across master agreements, order forms, and statements of work, which catches drafting inconsistencies before they turn into a negotiation problem. It’s also cut down the time it takes to pull together competitive and market research, so the team spends their time acting on the analysis instead of assembling it.
It’s changed how I work personally too. A good chunk of the job used to be reasoning things through in one-to-one conversations that only ever lived in a Slack thread or a DM. Now, more of that thinking can go into something durable and shared — a template, a standard, a framework — so the team can use it without needing me in the room every time. That shift, from doing the work to building the system that does the work at scale, is probably the biggest change AI has made to how I operate.
Finally, what are five pieces of advice you would leave with RevOps professionals looking to improve their organizations?
1. Fix the data before you fix the tech stack. A new platform bolted onto messy CRM data just gives you messy data faster. Sort the hygiene out first, then automate on top of it.
2. Diagnose the gap before you buy a tool to fill it. Work out whether you can do it with what you’ve already got, or whether someone in your network has already solved it, before you look at something new. Buying a new solution for every gap gets expensive, and half of it never gets properly adopted.
3. Put every function on the same revenue number. If marketing, sales, and customer success are all measured on different metrics, don’t be surprised when they pull in different directions. The goal shapes the behavior.
4. Treat forecasting as a discipline, not a guess with a spreadsheet attached. Use more than one method, be honest about where the risk sits, and keep large deal upside visible rather than folded into a number you’re meant to be able to stand behind.
5. Use AI to get RevOps into the room where decisions get made, not just to save time on admin. If it’s only clearing your inbox and tidying your CRM, you’re using a fraction of what it can do for the function.
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About Omilia
Omilia is a global standard for AI-driven customer service transformation, their native Self-Learning Agentic CX platform revolutionizes how enterprises engage with customers
About David Ogden
David Ogden is VP of Revenue Operations at Omilia
