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21 år med Google Analytics

Performancekonferansen - 2026

Fra voksesmerter til fremtidsrettet innsikt

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Eivind Savio

Lead Analyst

Knowit AI & Analytics

eivind.savio@knowit.no

https://www.knowit.no

https://www.savio.no

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21 år med Google Analytics

2023

UA Standard Sunset

Universal Analytics stopper å prosessere hits for standard kontoer.

2024

Tilgang UA interface stenges helt. GA4 står på egne ben.

UA legges helt ned

1997

Urchin Software Corp

Selskapet endrer fokus til serverlogganalyse.

1995

Paul Muret, Jack Ancone, og Brett Crosby grunnlegger forgjengeren til Urchin.

Web Depot grunnlagt

2005

Google kjøper Urchin for ~$30M og lanserer Google Analytics som en gratis tjeneste

Google oppkjøp

2007

ga.js (Classic)

Introduksjon av det nye sporingsbiblioteket, som går bort fra den eldre urchin.js-versjonen.

2012

UA (v3) lanseres, og introduserer kryss-device tracking og User ID

Universal Analytics

2017

Global Site Tag

gtag.js lanseres for å forene tracking på tvers av Ads og Analytics.

2020

Event-basert tracking er den nye standarden, og erstatter UA.

GA4 lanseres

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Google Analytics Evolution: From Urchin to GA4

This timeline tracks the major architectural shifts, acquisitions, and milestones in the history of Google Analytics.

1. The Pre-Google Era (1995–2004)

1995: The Foundation. Paul Muret, Jack Ancone, and Brett Crosby found Web Depot, a web hosting and design firm in San Diego.

1997: Urchin Software Corp. The company pivots and officially becomes Urchin Software Corp. They develop "Urchin," a tool that analyzes web server log files.

1998: Urchin 1.0 & 2.0. Urchin is sold as a licensed software that businesses install on their own servers.

2002: Urchin 5 & On-Demand. Urchin releases a hosted version (SaaS), moving away from just analyzing local log files to using a JavaScript-based tracking method.

2. The Google Acquisition & "Classic" GA (2005–2011)

March 2005: The Acquisition. Google acquires Urchin Software Corp for an undisclosed amount (estimated at $30M).

November 2005: Google Analytics Launch. Google rebrands Urchin on Demand as "Google Analytics" and makes it free to the public. Demand is so high that sign-ups are suspended within a week.

2006: The Invite System. GA remains invite-only for much of the year as Google scales its infrastructure.

2007: ga.js (Classic Analytics). Google introduces a new tracking code library, ga.js, which moves away from the old urchin.js.

2009: Asynchronous Tracking. Google launches the asynchronous tracking snippet. This allows the tracking code to load in the background without slowing down the page's visible content.

2011: Real-Time Reports. Google launches the Real-Time reporting interface, allowing users to see site activity as it happens.

3. The Era of Universal Analytics (2012–2018)

October 2012: Universal Analytics (UA) Beta. Google announces Universal Analytics (v3). This introduces the analytics.js library.

2013: Cross-Device Tracking. UA moves the focus from "visits" to "users" by introducing User ID tracking, allowing businesses to connect sessions across different devices.

2014: UA reaches General Availability. Universal Analytics becomes the official standard for all accounts.

2016: Google Analytics 360 Suite. Google rebrands its premium enterprise tier (formerly GA Premium) into the "Google Analytics 360 Suite."

2017: Global Site Tag (gtag.js). Google introduces gtag.js to streamline tagging across multiple Google products (Ads, Analytics, etc.) into a single framework.

4. The GA4 Revolution (2019–Present)

July 2019: "App + Web" Beta. Google launches a new property type designed to combine data from mobile apps and websites into a single reporting interface. This is the precursor to GA4.

October 2020: Google Analytics 4 (GA4) Launch. "App + Web" is officially rebranded as Google Analytics 4. It becomes the default for all new properties.

March 2022: The Sunset Announcement. Google officially announces that Universal Analytics will stop processing data, forcing the industry to migrate to GA4.

July 1, 2023: Standard UA Sunset. Standard Universal Analytics properties stop processing new hits.

July 1, 2024: The Final Cut. Google Analytics 360 (enterprise) properties stop processing data, and access to the Universal Analytics interface is completely shut down for all users.

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2026

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GA 2026

Hvorfor?

Rotårsaksanalyse, korrelasjoner, sesongvariasjoner, markedsføringsaktiviteter

Fra �Hva? til Hva nå?

Hvordan er utviklingen?

Benchmarking, uke-for-uke, segmenter

Hva?

Salg, sesjoner, brukere

Hva kommer til å skje?

Trendprediksjon, prognoser

Hva burde vi gjøre?

AI-drevne anbefalinger. Conversational Analytics

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AI-drevne anbefalinger

Do you want bullets: Klick the indent button to add our custom bullet to your point.

In this case

...tab will only give you an indent and

... the bullet button will give you a wrong bullet and a too large indent

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Budsjetteringsverktøy og scenarioplanlegger

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Budgeting in Google Analytics uses your property’s historical data driven attribution as the primary input to a Bayesian regression model to forecast the optimal total budget per channel over the defined time period. This regression model is inspired by Google’s Meridian, and is based on your property’s attribution data.

While this cross-channel budgeting is inspired by Google’s Meridian modeling, you should not expect consistency or reproducibility using Meridian due to Google Analytics use of Data-driven Attribution priors.

Bayesian regression modeling is a statistical analysis technique that measures the impact of historical marketing activities to guide future budget planning decisions. The Google Analytics Budgeting Bayesian regression model combines prior knowledge (your property’s data driven attribution historical data) with signals learned from data to estimate media effects. Budgeting in Google Analytics fundamentally considers what would have happened if the marketing spend had been different based on Data-driven Attribution priors (as an attribution input) and response curves (as a Bayesian regression output), ultimately recommending optimal budget allocation based on how marketing spend affects KPIs (revenue and conversions).

We use a time-series model to forecast the spending patterns on a per-channel basis. Revenue and conversions are estimated from response curves learned from historical data in the Google Analytics property. Based on the response curves and forecasted spend, we estimate revenue and conversions for future time periods. The ROI estimates are consistent with data driven attribution. Models are trained on 12 months of data to account for seasonality.

Budgeting models show results for paid channels where there is high model confidence based on the historic data availability. Model confidence levels are determined by a backtesting methodology for cost per channel - we run models on historic time periods to understand the accuracy of your property’s budgeting model. Similarly, for conversions and revenue, we look at historic time periods to see how well the trained model's channel-level estimates match the historical Data-driven attributed revenue/conversions. Typically, low model confidence for a channel means there were gaps in campaign data, changes in conversion implementation, changes in your primary channel grouping, or deleted conversions / channels.

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Oppgaver i GA

Datakvalitet

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Fokusområder

Datakvalitet og innsikt

Konto

Naming

Samtykke

Ecommerce

Product Linking

Left-menu

Cookie-consent

Google Signals

GA4 Admin Consent Settings

Data settings GA4

User ID

Consent Mode v2

Modelling

Reporting Identity

Custom Channel Grouping

User-Provided Data

Transaction ID

Shopping funnel

Search

List Attribution

Checkout funnel

Item scoped dimensions

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Modenhet

Datakvalitet og innsikt

GA4

Standard rapporter

GA4

Explore

GA4

Tilpasset venstre meny

Data Studio

Med GA4-connector

Data Studio

Med BigQuery Connector

Data Studio

Med GA4Dataform

BigQuery

Raw export

BigQuery

Med GA4Dataform

BigQuery

GA4Dataform �+ Ads-kilder

GTM

Web GTM

sGTM

Server-side

sGTM

First-party mode

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Standard event’er og parametere

Datakvalitet

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UTM�(Kampanjetagging)

Datakvalitet

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Kampanjetagging betalt SoMe

Datakvalitet

UTM	Verdier
utm_medium	paid
utm_source	{{site_source_name}}
utm_campaign	{{campaign.name}}
utm_id	{{campaign.id}}
utm_content	{{ad.name}}
utm_term	{{adset.name}}
utm_source_platform	meta

UTM	Verdier
utm_medium	paid
utm_source	tiktok
utm_campaign	__CAMPAIGN_NAME__
utm_id	__CAMPAIGN_ID__
utm_content	__CID_NAME__
utm_term	__AID_NAME__
utm_source_platform	__PLACEMENT__

UTM	Verdier
utm_medium	paid
utm_source	snapchat
utm_campaign	{{campaign.name}}
utm_id	{{campaign.id}}
utm_content	{{creative.name}}
utm_term	{{adSet.name }}
utm_source_platform	{{site_source_name}}

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Google annonsering

Datakvalitet

Google Ad Identfikatorer�(eg. gclid, dclid)

Aggregerte Identifikatorer�(eg. gbraid, gad_source, gad_campaignid)

Manual tagging�(Ads scripting)

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Conversational Analytics

Snakk med dine datasett og skap innsikt
BigQuery + Conversational
GA4 + MCP + AI-chat

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Bedre ROI

Google Analytics i 2026

Nettsted

GTM og SGTM

App

Firebase

Andre datakilder

?

Markedsføringsdata

UI eller API

Koble datakilder

Identifikatorer

User-provided data, User ID, UTM, Transaction ID, anbefalte event’er

Følg standarden

Google Signals

Forsterk signaler

Adferd og forretningsdata

Prediksjon m.m.

Performance

ROI og avanserte analyser

Budsjettering m.m.

Aktivering med Google AI

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Takk for meg!

Knowit AI & Analytics

Eivind Savio

eivind.savio@knowit.no

https://www.knowit.no

https://www.savio.no

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