I help marketing and business leaders make sense of their data — clearly, ethically, and without unnecessary complexity.

With over 15 years of experience in digital analytics, SEO, and marketing, I work with B2B companies that want to understand what truly drives results online. 

My role is to make analytics trustworthy, privacy-compliant, and genuinely helpful for decision-making.

I use the CARE framework — Collect, Analyse, Recommend, Experiment — to help organisations move from fragmented data and ad-hoc reports to systematic, sustainable insight generation.

What I do:
• Clarify what should be measured and why.
• Build tagging and reporting processes that teams can actually maintain.
• Align marketing analytics with business goals, not just metrics.
• Help organisations balance insight with privacy.
• Translate analytics into plain language for management and marketing teams.

Analytics should support human judgment, not replace it. My goal is to help teams trust their data — and act on it with confidence.

We’ve implemented product analytics for mobile and web applications using GA4, Matomo, Piwik PRO and Amplitude.

What we've found out is this:

All tools can track events and build reports.

-- GA4 works well for marketing analytics but feels constrained for deep product analysis. It's usability for non-analyst users is limited.

-- Matomo and Piwik PRO are easy to use and strong in privacy-sensitive environments but remain too close to traditional web analytics.

-- Amplitude is built specifically for product analytics. Funnels, retention, cohorts and behavioral segmentation are native and intuitive. It enables product and growth teams to answer complex questions without SQL and supports structured event governance from the start.

If the goal is serious product analytics, Amplitude is the clear winner.

Let's talk if you need a consultant to help with product analytics!

Mikko Piippo

I am co-founder and partner of Hopkins. I help organisations to CARE about their digital analytics data:

✅ C - Collect data that matters 
✅ A - Analyse the data
✅ R - Recommend actions
✅ E - Experiment & execute

Hopkins is a digital marketing agency located in Helsinki, Finland. I launched it in 2016 with two friends. Now we are 39 people ready to help you to improve your digital marketing. 

We are happy to work with international clients looking for a top partner in digital analytics & marketing, either internationally or when they need an agency partner for the Finnish and neighbouring markets.

I regularly speak at events and conferences about web analytics & digital marketing. For example,

📆 Analytics Conference 2016, Sydney & Melbourne.
📆 Marketing Analytics Summit Milan 2019, 2020, 2021.
📆 Crawl & Indexation Summit 2020.

Contact me if you want me to speak at your conference or event!

Digital analytics interventions | GA4, Piwik PRO, Matomo Analytics | Hopkins co-founder, board member, senior advisor

Google Ads consultants today are fighting Google’s AI. That’s the uncomfortable truth.

Why?

Because many of the “AI-powered improvements” are structurally designed to increase spend. Broader targeting. Less visibility. More automation. Higher auction pressure. 

That maximizes Google’s revenue. 

It does not automatically maximize advertiser profit.

So consultants have become defensive operators. Their job is no longer just growth. It is damage control.

AI in advertising isn’t neutral. It follows incentives.

And when the platform earns more by spending more, someone has to protect the client’s margin.

Attribution is a trap for B2B marketers.

For them, CRM should matter more than GA4.

GA4 shows clicks. 

CRM shows cash.

In B2B, no deal is “sourced” by one channel. There are ads, webinars, emails, calls, demos, buying committees. Attribution models just create the illusion of control.

The real question isn’t: Which channel got the lead?

It’s: Does Marketing move pipeline and revenue?

That answer lives in your CRM.

Stop obsessing over marketing sourced revenue. You'll never be able to identify it, not by integrating GA4 with CRM, not by MMM.

Start tracking:
-- Pipeline Velocity by segment
-- CAC Payback by segment

Less credit. More causality. More revenue.

CMOs: before you approve another “analytics” budget, pause.

In my experience, most decision-makers don't know what they're buying and what they should buy instead.

In most organizations, three very different needs are bundled under one word.

1️⃣ Planning -- this is measurement architecture.

--> What are we trying to influence?
--> How do we define qualified pipeline?
--> Which metrics connect marketing to margin?
--> What decisions should the data support?

If this layer is unclear, everything downstream becomes political.

2️⃣ Tagging -- this is technical execution.

--> Are events firing correctly?
--> Is consent handled properly?
--> Is the data structured and documented?
--> Is reporting stable across tools?
If this layer is weak, your dashboards are unreliable.

3️⃣ Analytics -- this is commercial judgment.

--> Where is profit created or destroyed?
--> Which channels deserve more investment?
--> What should we stop funding?
--> How does marketing contribute to enterprise value?

If this layer is missing, you have data but no direction.

The same partner can deliver all three. Yet, it is almost impossible for one person to excel in all three.

Start buy agreeing on which capability gap you are solving.

The strategic question is not “Which vendor?” or "Which tool?".

It is:

Are we fixing definitions, fixing data, or fixing decisions?

I have used Piwik PRO and Matomo on premise extensively.

And I think most comparisons between them start from the wrong premise.

This is not primarily a “proprietary vs open source” debate.

The real question is operational:

Do you want to run analytics infrastructure — or use it?

Piwik PRO is a managed solution. ClickHouse-based architecture. Integrated consent management. Strong performance out of the box. Minimal tuning. 

It allows teams to focus on analysis, reporting and business impact instead of database optimisation and server maintenance.

Matomo’s strength lies elsewhere. The self-hosted version gives you full technical autonomy. You control the stack. You decide how everything is configured. 

But that control requires internal capability: updates, scaling, performance tuning, monitoring.

So the choice is less about features and more about responsibility.

-- If you value efficiency and predictability, a managed setup is usually the rational option.
-- If architectural sovereignty is a strategic priority, self-hosting can be justified.

Both tools are serious.

The difference is where you want to allocate complexity: to a vendor, or to your own team.

In my latest client case, I did not start with GA4.

-- I reviewed organic search results.
--  I analysed Google Trends for selected queries.
--  I used data from Statistics Finland – industry figures, demographics and economic data.

And yes, I checked GA4 as well.

Digital analytics is not just about GA4, GTM or ad platforms.

If you are responsible for growth, you need a wider view.

You need external data to answer basic strategic questions:
-- Is the market growing or shrinking?
-- How do we compare to competitors?
-- Are they stronger than us in organic or paid search?
-- Are we gaining or losing share of demand?

When you combine internal data with external market data, you see whether the issue is market conditions or your own execution.

Without that context, it is easy to draw the wrong conclusions.

Dear CMO: Digital analytics tools are infrastructure. Infrastructure does not create insight.

If you want insight, the necessary intervention is simple but structural:

--> Start with revenue-critical business questions.

Not metrics. Not reports. Questions that require decisions.

--> Build an insight backlog around hypotheses.

Every analysis should aim to confirm, reject, or refine a business assumption.

--> Combine clean, consent-aware data with behavioural and qualitative input.

Dashboards show patterns. Analysts interpret causes.

--> Measure analytics by decisions influenced and experiments launched.

Not by number of dashboards or tracking coverage.

If you invest in infrastructure, you will get infrastructure.

If you invest in analytical capability and structured decision processes, you will get insight.

And insight is what moves revenue.

𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗮 𝗯𝘆-𝗽𝗿𝗼𝗱𝘂𝗰𝘁

CMOs: tag management and dashboards do not create insight.

They create infrastructure. And infrastructure alone doesn’t increase revenue.

I often see this:
-- Server-side tagging implemented
-- GA4 / Piwik PRO configured
-- Clean-looking dashboards

But when leadership asks:
-- “Why are we losing customers at checkout?”
-- “Why is ROAS dropping?”

There’s no clear answer, because insights don’t come from tools.

They come from:
-- Clear business questions
-- Clean, consent-aware data
-- Behavioural and qualitative analysis
-- Strategic interpretation

𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 ≠ 𝗶𝗻𝘀𝗶𝗴𝗵𝘁.

If you invest in infrastructure, you will get infrastructure. If you invest in smart analysts, you'll get insights.

Most marketing agencies optimise based on clicks, ad platform data and GA4 conversions.

CMOs should ensure everyone (also the partner agency) in marketing has access to relevant sales data: 

- pipeline
- revenue
- win rates and 
- customer lifetime value.

When teams see real revenue outcomes, they optimise for business impact, not platform metrics.

I have seen this repeatedly in SEO work.

The team asks experts to write about a specific topic aligned with a target keyword. The intention is good. The result is usually silence.

Experts are busy. Writing from scratch feels like extra work. Deadlines slip. The SEO plan stalls.

This happens in our own team, and in client organisations.

The issue is not motivation. It is workflow design.

Instead of asking for a blog post, capture what already exists. Record a presentation. Use a sales call. Transcribe it. 

Let AI structure it around search intent and build a draft. Then ask the expert to review and sharpen it.

Now SEO is no longer dependent on spare time and goodwill.  It becomes a process that systematically converts expertise into searchable content.

Most digital analysts stop after analysis. They do not move to recommendations. Why?

They are trained to be objective, not decisive. Organizational culture separates “analysis” from “decision-making.”

Recommending action feels risky. And incentives often reward reports delivered, not impact created.

Presenting data is safe. Proposing change is not.

If you want recommendations, you have to make them part of the role.

“Our data is sacred,” a CMO once told me when I asked about ecommerce return rates.

We didn’t get the data.

The campaign performance looked strong. ROAS was healthy. Conversion rates were improving. On paper, everything worked.

But without return rates, we were optimising blind.

If 25–40% of orders come back, revenue is not revenue. 

The “best” campaign on the dashboard may be the worst campaign for the business.

This is what happens when marketing is measured on incomplete metrics. 

Teams optimise for what they are allowed to see. If they only see gross revenue, they maximise gross revenue. 

If they see net revenue and contribution margin, their decisions change.

Protecting data may feel responsible. 

In reality, withholding data from the people driving growth creates huge problems.

If you want profitable growth, share the data that makes it measurable.

Without access to return rates and margin data, marketing optimises for illusionary revenue. 

Growth requires transparency.

Dear CMO,

You don’t need more dashboards to turn data into value.

Dashboards are rarely the bottleneck. Most teams already see the numbers. The real constraints are different.

Lack of analysis. Reporting shows what happened. Analysis explains why—and what to do next.

Fear of recommending action. Data without a clear point of view doesn’t change budget allocation or strategy.

Lack of experimentation. Insights without structured testing never translate into revenue impact.

If you want more value from data, demand sharper analysis, explicit recommendations, and a disciplined experimentation model.

Dashboards inform. Decisions create growth.

Welcome to our digital analytics meetup focused on data quality, privacy on governance. It will be a perfect combination of short presentations, networking, food and drinks at Hopkins' office in central Helsinki!

Partnerships | Driving Data-Driven Decision Making | Data Integrity & Privacy Compliance

Helsinki, don't miss this! 🇫🇮 

On March 4th, Michael Fong and I will be joining the Hopkins
team with Simo Ahava to dive into web governance, digital privacy, and analytics - insights you won’t get anywhere else.

🥂 Food, drinks, and networking with top local teams included… but spots are almost gone!

⏳ Don’t miss your chance -  link in the comments to grab your spot!


#WebAnalytics #DigitalPrivacy #DataGovernance #Networking #HelsinkiEvents ObservePoint Hopkins Simmer

CMOs, are you getting insights and recommendations or just data and reports?

Many teams deliver accurate numbers and clean dashboards. That is useful. 

But when performance drops, you do not need another report. You need a clear explanation and a concrete next step.

Data describes. Insight interprets. Recommendations drive change.

Make it a rule: every analysis must end with a proposed action and expected business impact.

No report without a recommendation.

That is when analytics delivers revenue.

Most marketing teams optimise for data volume. CMOs should optimise for learning speed.

More data does not create advantage. Faster hypotheses, quicker feedback loops and systematic experimentation do.

A smaller dataset that drives weekly decisions beats a massive dataset that leads to slow discussions.

Dear CMO:  Your digital analytics team may be working hard, but does it move revenue?

They collect data. They build reports. Sometimes they even deliver solid analysis. Then the process stops.

Revenue pressure does not.

This is why I use the CARE model:
-- Collect clean, consent-aware data.
-- Analyse it against real business questions.
-- Recommend concrete actions with expected impact.
-- Experiment to validate and scale what works.

If you stop at collecting, you get infrastructure.

If you stop at reporting, you get visibility.

Only when you move to Recommend and Experiment does analytics start influencing revenue.

Historians and social scientists make great digital analysts.

(I know, I studied history.)

People often think digital analytics is about tools, javascript, Python, dashboards, and SQL.

It isn’t.

It’s about interpretation.

And people trained in history or social sciences are, at their core, interpretation engines.

We are trained to ask:

-- What does this really mean?
-- What assumptions are hidden here?
-- What alternative explanations exist?
-- What context are we missing?

That mindset is exactly what strong digital analytics requires.

Digital analytics is not just about data. It’s about interpretation. And interpretation matters in every single step of the CARE model.

If your marketing team doesn't interpret data, they leave money on the table.

Mission impossible in enterprise B2B digital analytics:

Connecting revenue to the original traffic source.

Enterprise B2B is not browser-based. It is not even contact-based. It is account-based.

An opportunity belongs to a company. Not to a cookie. Not to a single email address.

Inside that company:

-- There are multiple contacts.
-- Some are identified online.
-- Some never fill out a form.
-- Some block tracking.
-- The actual decision-maker might never attend your webinar, download your whitepaper, or click your ad.

Yet revenue is generated at the account level.

So what exactly are we attributing?

Passing a GA4 client ID to the CRM does not solve this. It connects a browser to a contact. It does not connect an account to revenue across a complex buying group.

Enterprise B2B attribution requires:

-- Account-level data models
-- Mapping multiple contacts to one buying group
-- Offline touchpoints integrated into the same system
-- Clear opportunity ownership in CRM
-- Governance strong enough to survive a 12-month sales cycle

The core problem is structural:
-- Revenue lives at the account level.
-- Digital analytics lives at the browser level.

Bridging that gap is not a tracking tweak.

It is a strategic data design decision. And a difficult one to implement.

Lately, I have experimented with using LLM tools to analyse our CRM.

Key learning:

The data quality is always the main challenge.

taplio

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