CASE STUDY
Data nobody trusted.
Accounts you couldn't prioritize.
RevOps stuck in repair mode.
How Zebra BI automated data ops – and turned RevOps from firefighting to strategy.
WHAT OUR CUSTOMER SAYS
Hear from Matic Užmah, Head of RevOps at Zebra BI:
THE SITUATION
Data existed. Trust didn't.
Zebra BI makes data visualization software for Power BI and Excel. Trials, webinars, demos – thousands of touchpoints creating HubSpot data.
The problem wasn't getting data in. It's what happened after.
"Revenue Potential" – expansion opportunity based on company size – was missing on 25% of accounts. How do you prioritize CSM assignments? Plan headcount? Know where to invest?
You don't. You guess.
Meanwhile, contacts linked to wrong companies. Duplicates multiplied. Lifecycle stages meant nothing. Every leadership meeting hit the same wall: "Can we even trust this data?"
When Matic joined as Head of RevOps, he saw it immediately. Two people working on data. Reports still questioned. RevOps was a repair function, not a strategic one.
"When you see that 20-25% of accounts have no growth potential data – you're wandering in the dark. You could assign your best people to accounts with no room to grow. Complete waste."
Matic Užmah, Head of RevOps
WHAT WAS BROKEN
Four problems compounding daily.
Growth potential blind spots
Growth potential shows how big a customer could become. It tells you which accounts get senior CSMs, which get self-service.
25% of accounts missing this data. No way to segment or prioritize. Resource allocation was guesswork.
Contacts disconnected
Same person appearing as multiple records. Contacts linked to wrong companies. Or not linked at all.
Account-level reporting unreliable. Activity history fragmented. No clear picture of who was engaging.
Lifecycle stages meant nothing
Lifecycle stages track the journey from lead to customer. They determine handoffs between marketing, sales, and customer success.
No clear rules for transitions. Handoffs breaking down. Teams arguing about lead quality – "What kind of leads are we sending?"
Reports unreliable
Same metric showing different numbers in different dashboards. Nobody knew which number was right.
Leadership couldn't commit to decisions. Every meeting: "Can we trust this data?"
The Real Problem
These weren't four separate issues. They were one system failing. Bad data meant bad reports. Bad reports meant no trust. No trust meant RevOps spent all its time defending numbers – instead of driving growth.
WHY INTERNAL FIXES FAILED
In-house is slow. Consultancies are slower.
In-house cleanup? Two people were already on it. Data still wasn't trusted. The math didn't work – too many fires, too little time for systematic fixes.
Big RevOps consultancies? Matic had seen them before. "Too heavy. Too abstract. Too slow." Scale-ups can't wait months for discovery phases and frameworks.
Native HubSpot tools? Help with simple cases. But parent-child company relationships? German companies not on LinkedIn? Contacts using personal emails? Native tools don't reach.
What Matic needed: experts who'd solved these exact problems at other companies. Who could move fast. Who understood that quick fixes decay – you need systems that run themselves.
"We needed adults in the room. Not just another tool. Someone who walked this path with other companies. Experts who've seen different cases and can help you with something another company already fixed."
Matic Užmah, Head of RevOps
What Matic Needed
- Experts with cross-company experience
- Speed without sacrificing quality
- Solutions designed for future scalability
- A partner who could say "that won't work" when needed
THE SOLUTION
From cleanup to continuous data ops.
Zebra BI started with a simple goal: make the data trustworthy. It evolved into a complete automated system running 24/7.
"What I liked working with Sellestial is that you figure it out very quickly what is working, what not, and then you suggest what should be the alternative."
Matic Užmah, Head of RevOps
The expansion that set the direction
Early on, Sellestial built AI-personalized nurturing campaigns for Zebra BI's self-service trials. Open rates improved. But conversions didn't.
The insight: personalization without accurate data context is noise. You can't write relevant emails if you don't know who you're writing to.
So we expanded. Enrichment and cleaning first. Then – and only then – smart outreach.
Data Foundation
Fixed company associations. Merged duplicates. Standardized fields. Created the clean base everything else would build on.
Revenue Potential Enrichment
Filled missing employee counts from multiple data sources. Built custom agent for edge cases – German companies without LinkedIn presence, fallback logic based on company characteristics.
Visitor Identification
Identified accounts visiting the website before email capture. Enabled outbound engagement earlier in the funnel – before competitors even knew there was interest.
Job Change Monitoring
Tracked when contacts with closed deals changed companies. Surfaced champion opportunities automatically – people who know the product, now at new companies ready to buy again.
ICP Segmentation
Clear classification: ICP vs non-ICP. Better targeting. Resources focused where the opportunity actually exists.
THE RESULTS
From repair to strategy.
Six months. Multiple automated pipelines. Continuous operations.
This isn't from one cleanup sprint. It's from data ops that never stop running.
Remember the four problems?
Growth potential blind spots
→ Now known on 99% of accounts. Resource decisions based on data, not guesses.
Contacts disconnected
→ Clean associations. One record per person. Account-level reporting that holds up.
Lifecycle stages chaos
→ Clear rules. Working handoffs. Marketing and sales aligned on lead quality.
Reports unreliable
→ Same metric, same number, every dashboard. No more Excel backups to verify.
"Employees now see RevOps as a strategic or growth function – not just the support or repair function. That's quite important for RevOps teams. Once you have trustworthy data, you can focus on strategic stuff – instead of cleaning it and figuring out why it doesn't make sense."
Matic Užmah, Head of RevOps
IN PRACTICE
Two capabilities. Two examples.
What does automated data ops look like in action? Two illustrations from Zebra BI's system.
Surfacing 836 warm opportunities hiding in plain sight
When someone buys your product, then changes jobs – they take that knowledge with them. They know how it works. They trust it. They can champion it internally at their new company.
These are your warmest possible leads. But without tracking job changes, they're invisible. Competitors close deals that should have been yours.
What we built
Job monitoring running automatically on all contacts with closed deals. When someone's LinkedIn shows a new company or new position – the system flags it.
One in four.
"We have almost 1,000 contacts with champions at new companies. The velocity would be quite fast – they know the product, they know the procurement process. It's a very straightforward sale."
Matic, Head of RevOps
Why it matters
Each of these 836 contacts:
- Already bought once and saw value
- Knows the procurement process
- Can advocate internally at the new company
- Means shorter sales cycles, higher win rates
This wasn't a problem Zebra BI knew they had. Sellestial surfaced the opportunity. Now it runs automatically – every contact with a closed deal gets monitored.
From 75% to 99% – knowing where to invest
Growth potential tells you how big an account could become – based on company size. It determines where you invest: which accounts get senior CSMs, which get self-service, where you hire next.
Zebra BI was missing this data on 25% of accounts. Every resource decision was partly a guess.
The challenge
Sellestial's standard enrichment got coverage from 75% to 95%. But the remaining 5%? Many were German companies – common in their market – that don't have LinkedIn pages. Standard data sources couldn't find employee counts. These accounts stayed invisible.
The solution
Sellestial built a custom agent with fallback logic:
- Check primary enrichment sources
- If no match, search the web for company data
- If still nothing, use proxy signals (company characteristics that indicate size)
- Apply appropriate growth potential
"It was quite simple to explain. If there is no Revenue Potential data on the account, go check on the web. If there is no LinkedIn, take the fallback number that is usually correct for this type of account. It's working pretty well."
Matic, Head of RevOps
Why it matters
With complete coverage, Zebra BI can now:
- Segment accounts by expansion opportunity
- Assign senior CSMs to high-potential accounts
- Plan headcount based on where growth actually exists
- Make resource decisions based on data – not guesses
WHY IT WORKED
Speed. Experience. Built to last.
Matic didn't need another tool. He needed a partner who could move fast, had seen these problems before, and built solutions that wouldn't decay the moment you stop watching.
"What I liked working with Sellestial is that you figure it out very quickly what is working, what not, and then you suggest what should be the alternative. Instead of taking us weeks, it took us a couple of days."
Matic Užmah, Head of RevOps
Data first, then workflows
Not jumping to automation before fixing the foundation. The nurturing campaign pivot proved it – personalization without good data is just sophisticated spam. Clean data enables everything else.
Experience from many companies
Patterns that would take weeks to discover internally? Already seen and solved elsewhere. No learning curve on common problems. Faster path to solutions that actually work.
Future-thinking by default
Every solution designed to run perpetually. Not one-off projects that decay the moment attention shifts.
"You were thinking about the future scalability, not just the quick fixes. Always thinking: what will happen if we set up this workflow? How can we make sure that in the future, we will not get more messy?"
Matic Užmah, Head of RevOps
Partnership, not just execution
Regular meetings. Fast responses. Proactive suggestions. The job change monitoring capability? Sellestial's idea – Matic hadn't considered it before.
THE BIGGER PICTURE
RevOps as growth function.
"Employees now see RevOps as a strategic and growth function – not just the support or repair function. That's quite important for RevOps teams.
Once you have trustworthy data, you get trustworthy reports. Then you get real insights. Then you can take meaningful actions.
Now you can focus on strategic stuff. Can we build another agent for handoffs? For customer success insights? Instead of cleaning up the data and figuring out why it doesn't make sense.
I'm helping teams now – not just receiving requests about data accuracy."
Matic Užmah, Head of RevOps, Zebra BI
ADVICE
Trash in, trash out. Start there.
"First, define what you really want to solve. Be prepared – have a list of issues, ideas, and what to expect.
You need adults in the room. People who are fast and have years of experience. They can stop you when you're wrong. They've walked this path with other companies.
Know what you want to build. Tell the vendor what are the issues, where is the highest priority. Then discuss: can we solve this together?
And remember – trash in, trash out. If you want insights from your reports, you have to first have the data ready. That's where everything starts."
Matic Užmah, Head of RevOps, Zebra BI
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