How Founder-led sales can be done right? • Founder at Sellestial • 3rd-time Founder • Don't be a stranger, follow me and reach out

Is one of your main projects for 2026 cleaning up your HubSpot data?  Want to run AI in production?

If you answered yes to anything, keep on; otherwise, stop reading this.

👉 How we are different to tackle data hygiene with your internal team only:

1. Deep Data Cleaning & Enrichment Expertise
We're data cleaning and enrichment specialists who know this problem space and its traps inside out. By cleaning and enriching 100+ HubSpot portals, we've developed a deep understanding of edge cases that first-timers don't anticipate. This is not general HubSpot admin work; it's pattern recognition from seeing hundreds of messy databases.

2. Dedicated Bandwidth + Project Discipline
Data cleaning is important but rarely urgent, which means it gets perpetually deprioritized by internal teams. We provide dedicated focus with clear project boundaries that prevent the "cleaning to infinity" trap or arbitrary abandonment that internal teams tend to fall into.

3. Production-Grade AI Expertise for Data Maturity
We've built specialized AI infrastructure designed for accuracy and reliability. We are regularly deploying 7+ different AI models for different parts of the process. We tested and evaluated over 50 LLM models over the course of the last few years, and have a comprehensive understanding of their capabilities and caveats. 

The ROI for you: Your internal teams and HubSpot admins can focus on what they do best while we handle the extremely complex, time-intensive data cleanup and/or enrichment with the AI resources your team doesn't have access to.


👉 Want to talk about Sellestial?
Shoot me a DM, and we can schedule an introductory call to discuss data.

Klemen Hrovat

Want better HubSpot marketing email open rate?

Fix this.

Regularly review your marketing contacts:
- remove non-personal contacts
- remove disposable domains
- check for mx records of domains
- remove fake contacts
- check contact's employment status

Clean recipient lists  --> higher open rates

#EmailMarketing #HubSpot #marketinghub

Talks about HubSpot data + AI daily | HubSpot Community Champion

A customer asked us to help them with lead scoring in their HubSpot

saying "We have data in a separate system."

They're paying for a subscription to an industry-specific database. 
The data is supposed to help them score leads.

But the HubSpot integration?
Broken.
And the vendor has zero intention to fix it.

So they asked: "Can you help us get this information into HubSpot somehow?"

We built an AI agent instead.

It takes basic contact and company info from their HubSpot, searches for the specific information they need, and writes it back to the CRM automatically.

Here's where it gets interesting:
The agent finds more accurate information than their paid database.
It's more up-to-date.
And it has a higher success rate at finding what they need.

They're now questioning why they're paying for that subscription at all.

This isn't magic.
It's just what happens when you design data flows around what you actually need, not around what vendors decided to give you.

The real unlock?

When the right signals live in your HubSpot, lead scoring works.
Relevant lead prioritization becomes possible. 
Marketing is handing over good leads.
Sales stops wasting time.


Curious to hear from other RevOps and HubSpot admins.

What's a data source you're paying for that you suspect could be replaced or improved by something smarter?

Your reps are wasting 5+ hours per week chasing wrong accounts that:

- Are duplicates of accounts that they already reached out to
- Closed down companies
- Moved to another job 8 months ago
- Aren't actually a good fit

This isn't a process problem.

It's a data problem.

I saw that in a portal recently ... 9 duplicates of Coca Cola, missing subsidiaries, wrongly enriched company records ...  the list goes on up to 43 companies with something like Coca Cola as a company name.

Multiply that by every corporation in your HubSpot 🤯 

Before you hire more people or buy another shiny AI tool, audit your CRM. 

You will be surprised by the value hidden in the data you already have. 

What was the strangest duplicate situation you found in your HubSpot?

I got an idea to help HubSpot users understand their data AI readiness.

What could be improved in how I set it up in HubSpot?

I developed a form with 10 questions in HubSpot.

Each question is a new property with 3 possible answers.

Each of the possible answers has a different score (0, 1, or 3)

I didn't know how to weight specific values in properties to build the logic for how many points you get for each question natively in HubSpot. So I built a simple script in Sellestial that calculates the AI readiness score and writes it to a property in HubSpot.

I set up a workflow that, based on the calculated score, customizes the email with the score and some thoughts about their AI readiness and sends it via a HubSpot sequence.

HubSpot experts, what could I do better?

p.s. Want to get your AI readiness score? Linki in the comment

RevOps teams are accountable for outcomes they can't control.


- You own forecast accuracy but don't control pipeline creation.
- You own the tech stack but can't enforce process adoption.
- You're measured on conversion rates but have no authority over how leads get handed off.

That's not tools problem.
That's not a headcount problem.
That's a mandate problem.

What does RevOps actually own?

Have you set up the cap on marketing email frequency?

If you have already or plan to, be cautious.

This lets you cap the number of marketing emails each contact receives within a set period to prevent over-communication.

I keep seeing that challenge with HubSpot users I talk to.
Marketing sets up several/numerous/countless workflows. 
They automate marketing emails and lead nurturing.
But they don’t think about customer’s experience. 

A human got 2 emails right away, in the first 10 minutes after form submission.
Who knows what they’ll get today 🙂

By setting this up, you define a cap for regular marketing emails, workflow automated emails, and blog notifications. It does not apply to transactional emails, one-to-one emails, feedback surveys, or conversations inbox emails.

The cap is calculated per contact on a rolling time basis. If someone receives an email on Friday with a weekly cap of 1, they won't get another until the following Friday.


Think about these things before you turn this on.

1. You can’t guarantee that emails will be really sent to a contact. If you have some emails that you want to make sure you send, you can exempt specific emails (like critical announcements or promotions) from the cap by unchecking "Apply send frequency cap" in the email's send settings.

2. With that turned on, each email you send should stand on its own; it should not assume the person has received the previous email in the workflow.

When was the last time you reviewed your HubSpot contacts?

The 90-day data decay problem
Why your HubSpot Data Cleanup won't stick
and what to build instead


You just finished a cleanup project.
Reports finally make sense.
Properties standardized.
Duplicates merged.

Give it 90 days. 

It'll be broken again.

Why cleanups fail

We've audited over 100 HubSpot portals. The pattern is always the same: a team spends weeks (or months) cleaning their CRM. Three months later, they're back where they started, or worse.

The problem isn't the cleanup. It's the cleanup model.

One-off fixes are a lie.
They feel productive but decay within months. 
Nobody owns ongoing accuracy.

The only real solution is a system that run themselves.

We call this Perpetual DataOps. 

It's a framework for building HubSpot data infrastructure that stays clean, not for a quarter, but forever.

The goal of the Perpetual Data Framework: Stop fixing data. Configure a system that prevents data decay.

Three pillars

1) CLEAN DATA - Duplicates merged. Gaps filled. Properties standardized.
- <2% duplicate rate on contacts and companies (continuously maintained, not one-time)
- 95%+ fill rate on "must-have" properties
- Standardized formats (not enforced at entry, but standardized right afterwards)

2) RICH DATA - ICP scores. Buying signals. Lead prioritization.
- Contact + company enrichment running on every new record
- Waterfall enrichment from reliable sources
- ICP fit score configured and maintained
- Relevant signals captured and associated to the right records

3) UNIFIED DATA - Systems synced and unified.
- Field mappings explicit (no "sync everything and hope")
- Potential data overwrites defined with guardrails

The build plan

Days 0–10 | Audit & scope
Document your current data model
Identify your "must-have" fields per object (the 10–15 that actually matter)
Map where data enters: forms, imports, integrations, manual entry
Quantify the problem

Days 11–30 | Build the foundation
Deploy automated deduplication
Implement waterfall enrichment on contacts and companies
Configure processes to standardize field formats

Days 31–45 | Add intelligence
Build lead scoring based on firmographics + engagement signals
Configure lead prioritization logic
Set up routing rules based on clean, scored data

Days 46–60 | Activate & monitor
Turn on 24/7 operation
Document: how it works, when to intervene, how to extend


Stop cleaning your HubSpot. Start building infrastructure that keeps it clean.

The companies winning with AI, with ABM, with outbound at scale - they're not the ones with 100% perfect data. They're the ones with systems that keep data good enough, perpetually.

Ship the system. Let it run. Move on to strategy.

p.s. Want to know where your HubSpot data actually stands? We can audit your HubSpot to show you what's broken, what it's costing you, and what it takes to fix it permanently. No obligation to continue after the audit. DM or comment below.

Stop adding to the mess (before you start cleaning it up)
Everyone wants to clean their historical CRM data.
That's the wrong place to start.

The first quick win in data hygiene
STOP ADDING TO THE PROBLEM

Perform a thorough HubSpot data audit to understand what's broken, with a focus on understanding root causes, not just symptoms.

Start cleaning up your CRM after you understand:
- the current state of data
- what's causing the mess
- where data is coming into HubSpot
- what data is relevant and important

By knowing where you currently stand, you can design an implementation plan toward clean data. 

Do you plan to clean up your HubSpot in 2026? Where do you plan to start?

Unified Customer View: Pipedream or Realistic Goal?
Join me, Casey, and Klemen Hrovat for a live workshop moving through the Unified Customer View Playbook.

🎙️ New to streaming or looking to level up? Check out StreamYard and get $10 discount! 😍 https://lnkd.in/dAaAJzce

Had a great conversation with Karen Ng at INBOUND about what it actually takes to run AI in production.

The insight that stuck with me: "Evals has become a core skill that product managers need to understand."

AI is non-deterministic. 

Same prompt, different results. 

So how do you trust it?

You build guardrails:
→ Human in the loop
→ Evaluations at every level (prompts, outputs, actions)
→ Citations that trace back to source data

Watch the clip for Karen's full breakdown on how HubSpot approaches this.

"How many customers do you have?"

Asked a few of our customers when we started working together. 

None of them could answer it from their HubSpot.

Here are the reasons
Enrichment tools were overwriting properties. 
Duplicate companies were created. 
Churned customers were not marked accordingly.

Everyone and every system creates data. Nobody owns the mess.

So many CRM can't answer basic questions:

- Who are our active customers? Unclear. 
- Where's our biggest opportunity? Depends who you ask. 
- Which segment is growing? Check the spreadsheet backup.
- What do we report at BoD? Give me a week to prepare the slides.

You can't segment. Can't forecast. Can't prioritize.

I've watched RevOps spend hours manually building lists because they couldn't trust data. 

Seen execs commit to decisions based on numbers that were flat wrong.

All because nobody defined, implemented, and maintained data standards.

Want better insights? Fix data first.

And here's the part most teams skip: it can't be a one-time project.

Data decays.
People leave.
Processes drift.

Whatever you build needs to run itself, or you're back here in six months.

If you don't protect the foundation, nothing built on top will hold.

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