Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

What once felt simple and under control, now runs on many moving parts...

Marketing channels are disconnected from sales, LTV:CAC ratio is a rough guess, and your churn data is a month old.

‍The data to make sense of it all is scattered across Stripe, Hubspot, and product analytics, leaving your team to manually piece it together.

The result?

A collection of outdated reports that no one fully uses.

You’ve tried to fix it, asking people to update spreadsheets, buying new tools, maybe even hiring a specialist. But nothing sticks. Your own engineers are brilliant, but they’re busy building the product customers pay for.

In your mind, it’s simple: you just want accurate numbers so you can stop second-guessing and make the right calls.

At this stage, you face a choice.

You can continue guessing which free users will convert and which sales activities actually close deals, all while your teams debate why customers churn.

Or, you can choose a path to regaining control. The path where you:

- Increase conversion rates from trial to paid users.
- Attract customers who pay more and stay longer.
- Eliminate data debates between your teams.
- Truly understand why churn happens.
- Slash the time it takes to close deals.

We build the system that delivers these results, turning your scattered data into efficient, repeatable ARR growth.

The first step is a conversation. During a 30-minute call, we talk through your situation and what you're hoping to achieve. We'll be honest about whether we're the right fit to help. Our promise: you'll walk away with a fresh perspective, no strings attached.

Check our website for more info: www.datadrilldown.com

Thomas Varekamp

It took me 2+ years to figure this out.

And I still find ways to optimize every week.

Most B2B SaaS companies overcomplicate their data.

They scrap together some open-source tools + write tons of custom code.

A fun project for the engineer.
A nightmare for the business.

Because as soon as you scale:

→ engineers have other priorities.
→ dashboards break and stay broken.
→ maintenance takes hours every week.

The myth: data tools are expensive.
The truth: you can run a complete stack for <$100 /mo.

An engineer spending 1 hour every month on a data is more expensive than using tools.

So I always recommend to keep it simple: minimal code and managed by a single person.

Here is the toolkit we use to make that happen:

1. Extract Data
  
→ Don't write code, use managed connectors.
Examples: Fivetran, Airbyte, Stitch

2. Store Data

→ Centralize everything in one warehouse.
Examples: BigQuery, Snowflake

3. Transform Data

→ Define your logic once, not inside every dashboard.
Examples: Dataform, dbt Labs

4. Visualize Data

→ The final layer for the business.
Examples: Looker Studio, Power BI, Tableau

You don't need complex engineering for business analytics.

You just need the right tools.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

Landed in Bali last week. 

Mixed feelings on this place so far.

What I like:

I found a spot called Omni, a combined co-working, wellness, and gym.

It's basically a hub to get tons of work done with no distractions:

- solid gym
- top-notch service.
- great, healthy food.
- great co-working space.

Everyone is limited by time and energy. This place lets you really maximize both by having everything in one spot.

Also, prices are about 50% cheaper than in The Netherlands. Big arbitrage.

So, here’s what I don't like about Bali (so far):

1. The traffic is crazy. You literally cannot walk here. No pedestrian paths, just cars and tons of scooters.

2. Weird inequality. The moment you step outside of your nice co-working space, you see run-down buildings, broken roads, and trash. 

People that have been in Bali, any recommendations?

This weekend I’m going to explore the Gili Islands, excited for that one!

This is Mex, a Dutch entrepreneur I met here in Bangkok.

We bumped into each other at a club, and I instantly recognized the Dutch 🇳🇱.

A few days later we met for coffee, and that’s when he told me his story.

It all started years ago with Minecraft.

In Minecraft, you can create custom servers, basically private worlds with your own rules. 

Mex originally built one just for his friends, but after a while opened it to the public. The server was so good that people kept joining.

For the first 4 years it was purely a passion project. 

But as the community grew, Mex figured out how to turn this into a business.  

He began offering:

- custom game modes.
- memberships with extra privileges.
- custom skins for players to show off.

The model worked. The server grew steadily at around 20% per year. Then, 2 major events caused an explosion:

1. The pandemic
  
With everyone stuck at home, growth shot up to 100% year-over-year.
  
2. The Minecraft Movie
  
Its release brought in a whole new wave of players.

He scaled up, built a team, and hired an operator to manage the day-to-day.

After a while, he acquired another Minecraft server from a competitor (he told me they finalized the deal in a McDonald's lol)

Now he lives in Bangkok and runs the business remotely.

This is why I love talking to other entrepreneurs. 

You find out about businesses you didn’t even know were a thing.

A dashboard full of metrics means nothing if no one knows what they mean.

At many B2B SaaS companies we work with, this is common:

→ data is stored in some spreadsheets.
→ definitions are not 100% clear.
→ people don’t use the data.

The result: another data projects fails.

We solve this by creating a Metrics Dictionary.

It’s a simple Google Sheets that makes sure we all know what each metric means.

(this makes working with data a lot easier) 

Here’s what we include:

1. unit → what it's measured in
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source →where the data comes from

I included the template we use in the comments.

Copy it, tweak it, use it.

Good luck!

I'm launching a Youtube channel.

In the last 3 years, I’ve helped 20+ B2B SaaS companies build their data systems and create content about it.

Linkedin is great, but it's hard to show how to actually fix a broken ARR calculation in a 60-second clip.

Youtube allows me to go much deeper.

The goal is the same: help B2B SaaS companies regain control by turning their data into direction.

Topics:

- how to use data for ARR growth.
- how to align your team using data.
- how to operationalize B2B SaaS metrics.
- how to analyze your B2B SaaS business.

Basically everything a B2B SaaS leader needs to build a data-driven business.

The first video is live: https://lnkd.in/egPNPSs5

I show the exact data intelligence system we set up for most of our clients.

The plan: upload every week.

Check it out and let me know what you think!

The past 12 months were pretty stressful.

I’ve been in Asia for 2 months now, thinking a lot about what I actually want from work and life.

My goal with business has always been simple: work when I want, on projects I care about. 

Being here made me realize I’ve more or less reached that point. 

The money isn’t endless, but it’s enough for now, and I don’t need much to enjoy my life. I know being able to say that is a privilege, and I’m really grateful for it.

That made me ask: if I could design a day I’d never get bored of, what would it look like?

(still working on the answer)

Back in the Netherlands I was always in my head about the future, next milestone, next project, skipping fun because “work had to be done.” 

Here, I’m more in the moment. 

Most days I feel relaxed, rested, and creative. I follow more of what I 'feel' like doing instead of what I think I 'must' do.

It’s freeing, and it’s brought up a lot of emotions I’d pushed away. I keep having these random moments of: “Wow, life is actually pretty nice,” lol.

Not sure why I’m sharing this, just felt right.

Maybe it’ll resonate with someone.

Enjoy your weekend.

10 dashboards we’ve built for GTM teams to fix deal pipelines, missed targets, and slow leads.

Over the past few years, we’ve built many custom dashboards for B2B SaaS companies in the $2M–$10M ARR range. 

Below are 10 dashboards we have built for marketing & sales teams to make better decisions:

1. Deals Pipeline
2. Lead Lifecycle
3. Sales Rep Overview
4. Sales Funnel
5. Campaign Performance
6. Sales Overview
7. Target Account Overview
8. Advertisement Performance
9. Website Insights
10. Lead Journey

Scroll to see what each dashboard looks like ↓

Marketing__Sales_Dashboard_Showcase

I fixed a $3.2M ARR B2B SaaS's broken data system

(here’s how)

There is a dangerous transition for B2B SaaS companies around $2M ARR.

In the early days, data is a "favor."

You ask an engineer to pull a quick CSV. They write some code. You get the data.

But as the business grows, that method breaks:

- volume gets bigger.
- questions get harder.
- data is more scattered.

You move from needing a quick export to needing insights on daily basis.

And the engineer who used to run those scripts?

- they are not specialized in analytics.
- they are busy building the product.
- or worse: they left the company.

This creates a massive bottleneck.

And was exactly the case for a $3.2M ARR B2B SaaS I worked with last year. 

The CEO was waiting days for basic insights, and the data pipelines were impossible to maintain.

You can’t lead a company if your data is outdated or incorrect.

I just uploaded a video of how I completely overhauled the data infrastructure for a $3.2M ARR B2B SaaS.

In my new video on Youtube, I break down:

- the specific 15 dashboards I built.
- how I reduced data processing time by 98%. 
- how I aligned the teams on the right metrics. 
- how to drill from ARR to customer details in 2 clicks.

Check it here: https://lnkd.in/eAEps9T7

Our 5-dashboard system that gives B2B SaaS teams 80% of the insights they need.

Many B2B SaaS use data in:

1. spreadsheets
2. some pdf slides
3. a handful of dashboards

But this spread-out approach makes it hard to pinpoint exactly what’s driving growth, or what’s causing churn.

We fix this by building a 5-dashboard system.

It's designed for one purpose: give you complete visibility across everything that leads to ARR:

1. The ‘What’ (Management Overview)

Your high-level Command Center. It's the executive summary of all your key subscription metrics

2. The ‘Why’ (Revenue Overview)

The deep dive on your revenue. The ARR waterfall chart is the most important part. It shows you precisely what's driving ARR change.

3. & 4. The ‘Where’ (Country and Industry Overview)

This is how you validate your GTM strategy. You drill down into a specific segment and see its exact performance.

5. The ‘Who’ (Account View)

The 360-degree view of a single customer. You see their entire MRR history , sales activity, health, and user activity, all in one place.

You can drill down from your top-level ARR number... to the 'Agriculture' industry that's underperforming... all the way down to the specific customer who left.

Leading to:

→ more confident decisions. 
→ better team collaboration.
→ fewer data discussions. 

I put these 5 dashboards in the document below.

Check 'em ↓

The 5 dashboards that cover 80% of the insights (B2B SaaS)

One thing I’ve loved about Bangkok is how easy it is for founders and remote workers to meet people.

There are events almost every single day.

You can literally go to an event on a Tuesday afternoon and meet 10+ people all working on interesting projects.

My favorite example: there’s a weekly co-working meetup hosted at IKEA.

Sounds random, but it’s pretty cool. About 10 people of show up every time. 

We work together, share ideas, and just get to know each other.

I’ve joined twice since arriving, and every time I leave with a couple of new connections and fresh perspectives.

If you are a remote founder / worker, you can find a ton of these events on the Meetup app. 

Highly recommend!

At some point, data starts living in too many places.

That’s when even simple questions like “What’s our churn?...” become surprisingly hard to answer.

Teams pull numbers from different systems, rebuild metrics in spreadsheets, and show up to meetings with dashboards that don’t match.

The result? 

Decisions made on bad data.

At this stage, aligning your data stack becomes essential. But knowing where to start isn’t always obvious.

So here’s the 3-layer data stack we frequently work with:

𝟭. 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀
Where raw data lives across your business.

→ PostgreSQL:  product usage and event data.
→ Stripe: tracks subscriptions and billing activity.
→ HubSpot: contains commercial customer data.
→ Intercom: logs support tickets to capture feedback.
→ Google Analytics: measures website engagement.

𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
Moves, syncs, and activates data across tools.

→ Weld: centralizes data from all sources.
→ Twilio Segment: sends events across apps.
→ Hightouch: pushes insights back into business tools.

𝟯. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
Where raw data becomes actionable insight.

→ BigQuery: storage for all company data.
→ Dataform: models data into analysis-ready tables.
→ CompanyEnrich: data enrichment for segmentation.
→ Looker Studio: visualizes metrics for the team.
→ Python / Sheets: enables deep dives and prototyping.

If you're feeling the pain of inconsistent metrics or messy reporting, this structure might show you exactly what's missing.

And possibly solve the problem you've been trying to fix with spreadsheets.

It took levelworks 12 hours every month just to get accurate insights.

(this is Tim from levelworks, check out the video 👇)

This is a classic scaling problem: the most important data lives in manually updated spreadsheets. 

It’s hard to grow when you’re spending all your time just trying to get basic numbers right.

We stepped in and built them a real data foundation.

The result? 

In Tim’s words: “It basically saves us 2 to 3 hours a week.”

More importantly, the team’s conversations are now deeper, more strategic, and lead to higher-quality decisions.

And that’s the point: not pretty charts, but time saved and better decision-making.

Better data → better decisions → better business.

What got you to $2M ARR won't get you to $10M ARR.

Scaling from $0 → $2M is mainly about gut feel.

You're a small team. You're all in the same room. You're 100% focused on finding product-market fit. 

Making decisions on intuition works.

Scaling from $2M → $10M is about systems.

It's not that your gut feel is wrong. 

The problem is that it's not scalable.

You can't be in every sales call, every marketing meeting, and every product debate anymore. 

To scale, your team needs to make their own decisions.

That requires a better data system. 

One that 𝗮𝗹𝗶𝗴𝗻𝘀 everyone on what's important (dashboards) and 𝗲𝗻𝗮𝗯𝗹𝗲𝘀 them to find what's working (analytics).

We identified 𝟲 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘀𝗶𝗴𝗻𝘀 that tell you your current data stack is breaking.

Check the carousel below to see if you recognize them ↓

6 Signs It's Time to Upgrade Your Data System

6 Dashboards we’ve built for a B2B SaaS to give them end-to-end visibility. 

At Drilldown, we help B2B SaaS leaders regain control by turning data into direction. 

Below are 6 dashboards we built to help their Management, RevOps, Sales, and Customer teams make better decisions:

1. Management Overview
Single overview of the business.

2. RevOps 
Understand how revenue is generated.

3. Account Overview
All account-level info in one spot.

4. Self-Serve Funnel
How users move through the self-serve funnel.

5. Sales Deals Pipeline
Complete view of all deals.

6. Account Health Overview
Spot churn risks & growth opportunities.

Swipe to see them ↓

6 B2B SaaS Dashboards for End-to-End Visibility

Ecochain was struggling with a loss of control.

This is a classic growth problem: their most important data was not readily available.

It’s hard to lead a company when you don’t have a clear picture of what's happening.

We stepped in and built the dashboards + data infrastructure they needed.

The result?

As the CEO Frans-Willem put it:

"The moment I realized it was working was when we could compare the previous results with the current results and see the trends."

They now have the ability to show the results in a better way to the board and can see where they really have the challenges... and to get it resolved.

And that’s the point of data: not dashboards or nice charts. 

But improved understanding, control, and ultimately, better decisions.

The reason you have 20+ unused dashboards? Big chance you skipped some steps.

Here’s what happens:

- you needs insights. 
- you buy a BI tool. 
- you tell an engineer to "build dashboards"

A month later, there are 20+ outdated reports nobody uses.

If this is the case, you probably skipped some foundational work.

Here's the full 5-step system we use to build useful & accurate dashboards 👇

1. Choose metrics

Answer:

- what are your goals? 
- what KPIs track it?
- what are the metrics driving the KPIs? 

This helps to focus on the essentials. 

2. Create a metrics dictionary 

- write down what "ARR" means. 
- what's the calculation? 
- what's the data source? 

This document ends discussions about data. 

3. Extract, Transform & Load (ETL)  

This is the "lean" data stack we recommend for almost everyone:

- extraction: Fivetran / Airbyte / Weld
- warehouse: BigQuery
- transformation: Dataform
- visualization: Looker Studio

You don't need big, complex stack. This setup is cheap, scalable, and powerful.

4. Build dashboards 

- take your metrics dictionary
- start building charts for the metrics
- ask feedback from users

5. Integrate in daily work 

Data means nothing if no trust it.

- give every metric an owner.
- use the dashboard in your meetings.
- review your metrics definitions every quarter.

This is how you go from random dashboards to a data intelligence system that helps everyone.

'How seriously should we take our data?'

Usually, founders know they have a problem. They spend too much time in spreadsheets. They know it's messy.

But they don't know if it's the right time to fix it.

Here is my rule of thumb:

𝗣𝗵𝗮𝘀𝗲 𝟭: $𝟬 - $𝟮𝗠 𝗔𝗥𝗥
Don't invest too much in analytics.

Your Stripe dashboard + spreadsheet is fine.
You are just trying stuff out. 
No need to optimize yet.

𝗣𝗵𝗮𝘀𝗲 𝟮: $𝟮𝗠+ 𝗔𝗥𝗥
This is the tipping point.

You have product-market fit. 
You probably have +30 team members.
You aren't running the day-to-day anymore.

But most importantly: you have a new management team.

These people don't have the historical founders knowledge you have.

When they join, the first thing they usually ask is: "Where do we stand? What are the metrics?"

That's when it's time to invest in better data.

If you want them to lead, you have to give them the data to run the business.

Here's how Ecochain finally got insights they can trust.

Ecochain helps businesses measure their environmental impact.

After setting ambitious growth targets, they faced challenges with data:

→ data scattered across different systems.
→ arguing about which numbers were right.
→ manually exporting data for every report.

You can't grow efficiently when you don’t have the right insights.

So we build their data foundation:

1. Start with a workshop (not a dashboard)

We aligned their teams on the right metrics to track (Trial Users, Conversion Rate, Churn).

2. Automated the data flow

We connected all their key tools (Stripe, Mixpanel, HubSpot) into a central data warehouse. 

This stopped manual exports.

3. Built dashboards

Once the foundation was set up, we built the dashboards. 

Now the data is updated every day and easy to work with.

The result?

→ teams aligned on one set of numbers.
→ faster access to custom data insights.
→ hours saved every week.

Check out the full case study 👇

Case Study Ecochain

A dashboard that takes 10 minutes to load is pretty much worthless.

Nobody will use it. 

This was the reality for Rentman after raising $22M.

They were ready to scale, but their data setup was holding them back:

→ dashboards took 10+ min to load.
→ data was scattered across many tools.
→ teams struggled to use the messy data.

So we came in to rebuild their infrastructure from the ground up.

Here is the 3-step process we used:

1. The deep clean

We worked with their CTO to overhaul BigQuery. We split "Raw Data" from "Production Data" to ensure stability and speed.

2. Smart data orchestration

We set up tools to transform the data automatically. No more manually typing numbers in spreadsheets.

3. Actionable dashboards

We connected all their data points (like NPS & Subscriptions) into single views. Then built fast-loading dashboards for the teams to use.

Now the data is live, reliable, and accessible every day.

The results:

→ instant data visibility.
→ marketing, Sales, and CS aligned.
→ No more waiting 10 minutes for insights.

Check out the full breakdown in the slides below ↓

Case Study Rentman

We recently built a data intelligence system for Expandi, a LinkedIn automation platform doing $10M ARR.

The challenge: 

They had BigQuery and dashboards, but teams were misaligned, with no central place to find all data insights.

We started with a workshop to agree on the top priorities.

Then we rebuilt their data foundations from the ground up:

→ enhanced customer data (30% to 90% coverage).
→ connected user behavior to company metrics.
→ implemented data checks with Slack alerts.
→ cleaned and centralized all data sources.
→ built dedicated datasets for the teams.
→ deployed 10 production dashboards.

The result: 

1. hours saved to focus on strategy instead of data.
2. better alignment between teams.
3. more efficient meetings.

Very happy how this one turned out.

Check the full case study below ↓

Case Study Expandi ($10M ARR)

Saudi Electricity Company

• Worked with Engineering & Design Department (E&DD) on the design of High Voltage (HV) Substations and Underground Cables. 
• Conducted Feasibility Studies to Assess the Technical Feasibility of New Projects.
• Prepared Project Technical Specifications (PTS) / Scope of Work (SOW) documents for HV Substations and Underground Cables Projects. 
• Reviewed contractor’s submittals including drawings, design calculations, and materials for HV Substations and Underground Cables.

Electrical Engineer I

Identitypass

• Worked on a project about fraud detection using machine learning.
• Researched appropriate and relevant open-source datasets.
• Prepared and preprocessed the data.
• Used the preprocessed data to build machine learning models.

Artificial Intelligence Engineer

Saudi Digital Academy

Topics covered during the training period include:
• Performing Exploratory Data Analysis (EDA) using SQL, R, and Python programming languages to derive useful and actionable insights from databases.
• Visualizing data using Tableau, Excel, and R and Python programming languages to communicate data findings.
• Building predictive Machine Learning models based on historical data using Python and R.
• The training was based on daily assignments in which trainees applied the material they learned daily, and there were weekly weekend projects where trainees work in groups on a comprehensive project
covering all the topics discussed that week. Trainees are then required to present their work on the 
subsequent Sunday.

Data Science Bootcamp Trainee - متدرب في معسكر همة لعلم البيانات

Aramco

• Worked with Saudi Aramco Project Management Team (SAPMT) on the Security System Package of Haradh & Hawiyah Gas Compression Projects.
• Attended the weekly meeting with the contractor company where the progress of the project is discussed.
• Reviewed the work of the contractor company, e.g. drawings and other submittals, to understand the system.
• Took several short courses in the area of project management.

Summer Trainee

King Fahd University of Petroleum & Minerals - KFUPM

• Assisted faculty members in grading exams and homework assignments.

Grader

Power Systems Engineer | Data Scientist

A fresh graduate from the Electrical Engineering Department, KFUPM who is interested in data science, and its applications to electrical engineering disciplines.

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Thomas Varekamp

Abdullah Alhussain

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