FAQ

General

Core questions about what Digilytics helps with and what kinds of companies and setups are usually a good fit.

What does Digilytics help businesses with?

Digilytics helps companies get digital measurement and web analytics under control: from GA4 and Google Tag Manager to dashboards, data quality, privacy-first analytics, and more robust measurement architecture. The goal is not just more tooling, but measurement you can trust and actually use in real decisions.

What types of companies are a good fit for Digilytics?

Typically companies that want better control of their measurement, but do not necessarily need a large agency setup around them. That can mean smaller teams that need a solid GA4 baseline, but also organizations with higher requirements for data quality, governance, GDPR/ePrivacy, or more controlled analytics setups.

Do you need a large setup for analytics to be worth it?

No. For many companies, a clear foundation goes a long way: a correct GA4 setup, sensible events and conversions, proper channel tracking, and simple reporting. A smaller setup built properly is usually more valuable than a larger one nobody really trusts.

Why do numbers differ between GA4, ad platforms, and the CRM?

It is common and does not automatically mean something is broken. Differences usually come from attribution, time zones, lookback windows, consent, ad blockers, different conversion definitions, and the fact that different tools measure different things. The goal is rarely perfectly identical numbers, but a clear source of truth for different questions.

About Digilytics

Questions about how Digilytics works as a consultancy, how projects are typically scoped, and how to start at the right level.

Can you take over an existing setup built by someone else?

Yes. That is actually a common starting point. The work often begins with a review of the current setup: GA4, GTM, event structure, dashboards, consent flows, and any larger data quality issues. After that, the most important fixes are prioritized before building further.

Why do you show price ranges instead of fixed packages?

Because current state, scope, and technical complexity vary a lot between projects. One company may only need a fairly light GA4 cleanup, while another needs a measurement plan, consent integration, QA, and new reporting at the same time. The price ranges should therefore be seen as indicative levels, not prebuilt standard packages.

What affects the price the most in an analytics project?

Mainly the current setup, the number of events and conversions, the need for dashboards or QA, how consent/CMP affects the implementation, which platform is used, and whether hosting or more advanced infrastructure is involved. The more dependencies and uncertainty there are in the current state, the larger the project usually becomes.

Can you start smaller and build further later?

Yes — that is often the best approach. For many companies, starting with a stable GA4/GTM foundation, conversions, and simpler reporting is enough. Once the data becomes more reliable, it is much easier to decide whether the next step should be dashboards, consent work, Matomo/Piwik PRO, or more advanced privacy-first architecture.

Tools & implementation

Questions about GA4, GTM, Matomo, Piwik PRO, dashboards, and when more advanced implementation is actually needed.

Do you only work with GA4?

No. I also work with Matomo, Piwik PRO, dashboards in tools like Looker Studio or Power BI, and broader questions around measurement architecture, consent, and privacy-first analytics. Tool choice should be based on actual needs and risk level, not just habit.

When is Matomo or Piwik PRO better than GA4?

When you need more control over the data, clearer governance, or a higher level of privacy/compliance than the standard GA4 setup reasonably provides. That can mean self-hosted or EU-hosted environments, stricter retention, less third-party sharing, or a more privacy-first setup overall.

What does Google Tag Manager actually do?

Google Tag Manager is the layer used to control tracking and integrations without needing a new code deploy every time. Set up well, GTM makes measurement more structured and easier to maintain. Set up poorly, it often leads to duplicate events, incorrect conversions, and hard-to-manage tag logic.

Do we need server-side tracking?

Not always. Server-side tracking can improve control, make data collection more stable, and sometimes enable a more privacy-safe setup. But it also adds technical complexity, operations, and cost. It is usually relevant when there is a clear problem to solve, not as a default choice for everyone.

Can you build dashboards that are more than just pretty charts?

Yes. A good dashboard should help the team understand what changed, which KPIs matter, and where to investigate further. That is why I would rather build a few clear views that support decisions than large reporting packages nobody actually uses.

GDPR & consent

Practical questions about consent, CMPs, data minimization, and what privacy-first analytics means in the real world.

Do we need cookie consent for analytics?

That depends on the technology being used and what is stored or accessed on the user’s device, such as cookies or localStorage. I can help assess what actually requires consent and what a reasonable implementation can look like without making the data useless.

What is a CMP and why does it matter?

A CMP, or Consent Management Platform, handles the information, choices, and logic around consent. A good CMP makes it easier to be clear with users and to connect consent properly to tracking. A poorly implemented CMP setup often creates both compliance risk and poor data quality.

What is Google Consent Mode v2 in practice?

In practice, it is Google’s way of signaling consent status to services like GA4 and Google Ads. It can be relevant if you use Google advertising and need a more structured consent model. But it still needs to be implemented and tested correctly — otherwise you get neither good compliance nor good data.

Can you measure anything without consent?

In some cases, it is possible to get more aggregated and limited insights without identifiers. What is reasonable depends on the technology, data minimization, transparency, and the legal assessment. It is important here not to pretend everything can be solved with a technical trick, but to design the measurement at the right level.

What does data minimization mean in analytics in practice?

It means collecting only what you actually need. That can mean avoiding personal data in URLs or parameters, keeping custom dimensions under control, using sensible retention, and not collecting more detail than is actually used in analysis.

Privacy Engine

Short questions about Privacy Engine as an idea, direction, and how it relates to standard analytics today.

What is Privacy Engine?

Privacy Engine is Digilytics’ longer-term direction for how analytics can work with less dependence on individual tracking. The idea is not to recreate classic user-level tracking, but to work more with aggregated, reduced, and privacy-first analysis where that is relevant.

Is Privacy Engine a finished product today?

No. Privacy Engine should be understood as a development track and a model direction, not as a finished product that can simply be bought off the shelf today. But several of the principles behind it can already be applied when designing privacy-first analytics and more reduced measurement.

How is Privacy Engine different from standard analytics?

Standard analytics often relies on cookies, identifiers, and attempts to connect behavior over time. The Privacy Engine direction instead starts from the idea that the level of detail may need to be reduced, that individual tracking is not always reasonable, and that decision support sometimes has to be built on a more aggregated level.

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