Benchmarking Every Process And Data Change Gives Revenue Leaders Numbers They Can Trust

Revenue operations often gets described by its tools, from the CRM and dashboards to the automations running between them. The harder part of the job is knowing why a number moved and which change moved it. Teams now add AI features and automations faster than anyone can test them, and a report that shifts without explanation weakens trust in every report after it. Revenue operations earns that trust by making the revenue system traceable from end to end. Leaders can then see what changed and why, whether the change came from a person, a process, or an AI tool.

Hipatia Preis is Head of Revenue Operations and Data at Eque2, where she has worked for nearly a decade across sales and revenue operations roles. She builds automation workflows, manages the integration between the company's systems, and converts the data they produce into reporting for commercial leaders. Earlier in her career, Preis held sales operations and channel roles at Tripwire, Tenable Network Security, and Positive Technologies. She's also a member of the RevOps Co-op, a community for go-to-market operations professionals.

"If we make any changes to any process or to data, we always have a benchmark. Unless you have those pit stops in place, it's really difficult to go back and see what made that 1% really amazing, or that 1% a complete disaster," says Preis. Her benchmarks let the team trace any swing, up or down, to the change that caused it. The same discipline applies whether the change was made to the data, a process, or a system.

Pit stops in the data

Before a change goes on the record, Preis makes sure senior leaders agree on the goal it's meant to serve. Her team then records a benchmark at the point of change, so the numbers before and after can be compared directly. One change might move a number by 1% and another by 25%, and the benchmark shows which change caused which result. The processes also have to run without her. "If I get hit by a bus tomorrow, it needs to be able to continue," notes Preis. "RevOps cannot be the stopper in any of that engine."

The same rule covers automation. Preis' team uses AI for repetitive tasks it can verify. She treats each automation the way she'd treat a new employee, checking that its output matches her expectations. The goal is an operation where every result can be explained. "You could create little bots, but you need to make sure that what they're producing is what you expect to see," she explains. "There shouldn't be any surprises."

Preis applies that check to analysis as well. She has seen people run a data set through an AI assistant and call the result an analysis, without checking whether its findings make sense. Some AI tools also add new features faster than a team can test them, so they can't tell whether one feature helped before the next one arrives. Knowing the goal in advance is what makes the output useful, since a person who knows the data can tell when the conclusion is off. "It's an amazing tool, but it requires a human touch," adds Preis.

Reading from quote to cash

Preis starts every audit by talking to the people who use the systems, before she opens any data. She asks each team who owns what, what's breaking, and what makes their work harder. Hearing from sales and marketing separately shows her where their problems overlap. "Sometimes you find commonalities that a system can fix, a dashboard can bring together, data can bring together," says Preis. "It's about connecting those parts."

Systems have to agree with each other before leaders can trust what they report. At a past employer, Preis notes the dashboards lived in separate Excel tabs, and one person updating a table could break every report built on it. Today, her audits check that the CRM, financial software, and reporting tools pass the right data between them, so that commercial leaders see one set of numbers. "Without it, you have 20 different dashboards in different languages, with different people and different stats," she explains.

When a team says it needs more pipeline, Preis looks at the whole revenue process from the first quote to the final payment, to find where it's breaking down. She checks how well reps are converting and engaging customers, and whether marketing has changed its messaging or campaigns. At a previous company, Preis notes the team believed webinars produced pipeline. The analysis showed webinars brought interest, but the follow-up activity after them produced the pipeline. "It's a collection of activities, as opposed to a one-off," says Preis.

Data as ammunition

Preis' team keeps the company's dashboards in one place, so every team works from the same numbers. When those numbers flag a problem, she raises it with the person responsible, not in front of a meeting. Her team shows what changed, where the person performed well, and where results slipped, then leaves the fix to them. "It's really important to give the ammunition to people," notes Preis. "In this case, data is power."

Sharing data openly also changes how the rest of the company treats revenue operations. Teams that have numbers presented to them as criticism tend to stop sharing their own. Teams that can ask why a figure moved, in either direction, start bringing their questions to Preis' group. Senior leadership backs that approach by expecting decisions to rest on data. "That has made us a friend as opposed to a foe," she explains. "People are okay to come to us and say, I have an issue, I have a question about this."

Preis takes a practical view of the idea of a single source of truth. A company's CRM, dashboards, and AI tools can each produce a different version of the same figure. Her answer ties back to the benchmarks and audit trail behind the rest of her team's work. "The source of truth is where the journey began," Preis concludes. "As long as you can track the whole journey from the start to any changes, you'll always have a source of truth."