CASE STUDY
How BMG LABTECH built automated data ops – and doubled their email open rates.

WHAT OUR CUSTOMER SAYS
Hear from Markus Hartmann, Online Marketing Specialist at BMG LABTECH:
THE SITUATION
BMG LABTECH makes scientific instruments for laboratories worldwide. Researchers download their application notes, ebooks, and technical guides – thousands of form submissions every month.
Each submission creates a HubSpot contact.
The problem isn't the contacts. It's what happens after.
Scientists change jobs. Email addresses go stale. Data fields stay empty. Duplicates pile up from multiple downloads. And there's no way to tell good data from bad.
Markus Hartmann runs all of BMG LABTECH's digital marketing alone – SEO, content, campaigns, HubSpot admin. Keeping 70,000 contact records accurate? Impossible.
"I can do that as a one-time task – it's okay. But I cannot do that every week or every month. That's too much manual work."
Markus Hartmann, Online Marketing Specialist
WHAT WAS BROKEN
Scientists download multiple resources over time. They use different emails – personal, university, institutional. Each submission creates a new record. No reliable way to detect or merge them.
Which contacts match the ICP? Which companies are in the right industry? Without enrichment, no way to segment meaningfully or prioritize outreach.
Scientists often use personal email addresses. These can't automatically link to companies. Thousands of contacts had no company association – invisible to account-based reporting.
A contact opts in at Company A. Two years later, they've moved to Company B. But the old record stays. Emails go to abandoned inboxes. Deliverability suffers.
Field of research – critical for sending relevant content – was empty for almost everyone. Marketers couldn't match content to interest.
Markus manually checked one incoming list. Half the contacts had missing or wrong company data – impossible to segment properly. Then the real problem hit him: he'd been importing lists like this for years. Without checking. If one list was 50% unusable, what about the other 70,000 records?
WHY INTERNAL FIXES FAILED
40 new form submissions daily. 70,000 existing records decaying. One person.
Manual data maintenance? Mathematically impossible.
HubSpot's native tools help with simple cases. But fuzzy duplicate matching across multiple email addresses? Enrichment for contacts using personal emails? Tracking job changes over time?
Not built in.
Then HubSpot discontinued automatic company enrichment. The gap widened.
"We're getting 40 new contacts daily through the website, plus imports, plus lead lists from external portals. 2,000 to 4,000 new contacts a month. Checking them manually isn't doable – not with all the other tasks I have."
Markus Hartmann
THE SOLUTION
BMG LABTECH started with one problem: company enrichment. It evolved into a complete automated system.
Replaced HubSpot's discontinued feature. Company data filled automatically for every new contact.
Custom-built for BMG LABTECH. When contacts used personal emails, an agent searched the web to find and associate the right company.
Added LinkedIn profile URLs. Enabled duplicate detection across different email addresses – same person, one record.
Screened new form submissions for invalid entries – typos, incomplete data, bot submissions – before they entered the database.
Custom-built. Automatically assigned research fields based on web data – enabling content-to-interest matching at scale.
Standardized name formatting. "marcus" became "Marcus." Personalization looked professional in every email.
Tracked employment status over time. Flagged contacts who had left their companies – protecting sender reputation and deliverability.
THE RESULTS
One year. Seven automated pipelines. Continuous operations on 70,000+ records.
This isn't the result of one campaign or one cleanup. It's the cumulative effect of data ops that never stop running.
"Our campaigns are now hitting 30% open rate. That's really good compared to industry benchmarks."
Markus Hartmann
IN PRACTICE
What does automated data ops look like in action? Two illustrations from BMG LABTECH's system.
December 2025. BMG LABTECH ran their Christmas campaign list through Sellestial's job monitoring agent.
214 contacts - 17% of those verified – had changed jobs since opting in.
Their old work emails weren't bouncing. Not yet. But they weren't being read either. Dead weight on open rates and sender reputation.
All 214 removed before send.
"Job monitoring is the most effective one. I didn't expect it when we started. But 10 to 20% of our database had old email addresses."
Markus Hartmann
BMG LABTECH needed to match content to interest. A microbiologist should get microbiology updates. A quality control specialist should get QC content.
The problem: field of research was empty for almost everyone.
This wasn't a one-time backfill. The enrichment runs continuously – every new contact gets classified automatically.
"The target group for each field of research went from 100 or 200 addresses to 2,000 or 3,000 for each list. It's good for our customers too – they only receive content relevant to them."
Markus Hartmann
WHY IT WORKED
Markus didn't need a tool. He needed a partner who understood his specific challenges and built solutions for them.
"The most important thing was finding Sellestial – because now we're always building solutions specific to our problems."
Markus Hartmann
Sellestial brought solutions Markus hadn't considered – job monitoring, field of research enrichment, company discovery for personal emails.
Not generic templates. Custom agents designed for how scientists behave, how research data flows, what segmentation actually requires.
What started as company enrichment became full data operations. The system grows as new needs emerge.
"I didn't expect that evolution when we started. It's more a process – getting deeper involved in the topic, finding more issues, thinking about what's possible. I'm very excited what the future brings."
Markus Hartmann
THE BIGGER PICTURE
"When we set up HubSpot, I thought: it's just for sending marketing emails. Data quality doesn't matter here.
I was wrong. Bad data means emails going to people who left their jobs. Missing information to segment properly. Poor deliverability. Getting it clean turned out to be a massive effort – but it matters a lot."
Markus Hartmann
ADVICE
"Start manually. Look at the data coming in for a few days. Look exactly at the contacts – what is their email address, is the data right? Look at the company – does it fit your ideal customer profile?
Then you will automatically get a feeling for whether there is a lot of mess in the system.
And then – automate the cleanup."
Markus Hartmann, Online Marketing Specialist, BMG LABTECH
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