Armin Kakas

Revenue Growth Analytics advisor to executives in charge of growing Net Revenue, Gross Profit, and Sales Productivity | Posts and articles about Revenue Management + Machine Learning insights, methods, and processes.

As the founder of Revology Analytics, I have the privilege of offering mid-market businesses unparalleled expertise in advanced Revenue Growth Analytics & Management, focusing on three core advisory areas: Pricing & Revenue Management, Sales & Marketing Enablement and Commercial Analytics Transformation efforts. 

Beyond my advisory work, I am the creator of the Revology Analytics Insider, a bi-weekly newsletter offering readers unique insights into the world of Revenue Growth Analytics. You can catch the latest issues on LinkedIn and our website, revologyanalytics.com/articles-insights.

To learn more, please visit revologyanalytics.com

Founder | Managing Partner

Revology Analytics is a Revenue Growth Analytics consultancy for middle-market companies in the Manufacturing, Retail, and Distribution industries.

We are an impact- and execution-oriented advisory firm founded on Outcome-Based Analytics™, with focused specializations in AI/ML-enabled Margin Analytics & Optimization, Sales & Customer Growth Analytics and Promotion Effectiveness & Optimization.

MarketingLens

https://marketinglens.com/​

​MarketingLens helps organizations such as (but not limited to) financial institutions, e-commerce companies and start-ups grow and monetize their user base by designing, optimizing and executing their digital marketing strategy, using a holistic and data-driven approach.

Head of Analytics

LoupeDigital

https://loupe.digital

- Analytics strategy and end-to-end solution design / implementation for customer & marketing analytics use cases.
- Advising clients on best practices, picking the right tools and stitching them together to build a successful analytics stack for martech reporting and/or statistical analysis.
- Visual analytics to enhance digital marketing operations such as advertising, search engine optimization, analysis of user/visitor behaviour.
- Data science, machine learning and framework development, with an emphasis on transparency and knowledge transfer.

Head Of Analytics

Idiro Analytics

Strategy advisor for Idiro Analytics and working together at certain clients.

Advisor

Specialties:
- "Analytics Factory" / machine learning engineering (streamlined model development, deployment and monitoring frameworks)
- designing, building, operating end-to-end analytics solutions
- hands-on data preparation (incl. data architecture) and statistical modelling when required
- analytics capability assessment (various clients)
Leading the Idiro team at one of our Financial Services clients. Planning (both strategic and task level), managing client & Idiro resources and vendors, overseeing third party projects (statement of work, project tracking), liaising with stakeholders (business & IT).
In-house Idiro responsibilities: contributing to business development, recruitment, strategic directions, company operations, marketing Red Sqirl, Idiro's Big Data analytics product.

Senior Data Scientist & Consulting Manager

Conjura

Visual and advanced customer and marketing/advertising analytics across all digital channels as well as in-house enterprise data.
https://conjura.com/

Engagement Manager

Accenture

Project / work stream / team management since May 2014. Main areas:
- Fraud detection - advanced analytics
- Analytics solution architecture, infrastructure and end-to-end methodology
- Visual analytics
- Data governance and architecture (data modelling)
- Internal processes - knowledge sharing, collaboration, new joiner onboarding

Analytics Consultant

Oracle

Systems Quality Analytics & Engineering. Statistical data analysis of hardware reliability data. Developing OBIEE reports and dashboards to highlight quality issues. Using data mining to explore hidden patterns and identify/reveal characteristic failures that can be worked out to reduce service and warranty cost.
“Innovation in Warranty” Award for the warranty reporting solution by the Global Warranty and Service Contract Association.

Statistical Data Analyst - Hardware Quality Engineering

Multivariate data analysis (chemometrics). Hyperspectral chemical imaging. Investigation of unknown (illegal and counterfeit) drugs. Application of statistics and chemometrics in analytical chemistry. Analysis of pharmaceuticals, pharmaceutical engineering.
First author of six and co-author of nine academic publications in academic journals (with impact factor). Published further papers in journals without impact factor and in Hungarian journals. Held numerous oral presentations in various international and Hungarian conferences.

PhD candidate

Besides my own research work, I continuously had assigments for analytical tasks in joint industrial-academic research, development and process troubleshooting projects, using hyperspectral imaging and data mining/chemometrics. Major industrial partners included Gedeon Richter Plc, Hovione, Sanofi-Aventis, Cyclolab Ltd and EGIS Pharmaceuticals.
Many of these assignments were later studied in scientific details and were incorporated into my research. This enabled to provide such scientific results that can be directly used in industrial practice.

analytical chemist, part-time

Participated in a data mining project in retail sector with the supervision of Dr. Gabor Horvath PhD, who was the Practice Manager at Oracle Consulting Hungary, responsible for BI and Datawarehouse projects at that time.

Data analyst, part-time

EGIS Pharmaceuticals PLC

Manufacturing pharmaceutical products with various technologies and application of hyperspectral Raman imaging and multivariate data analysis to aid formulation development.

summer intern

Creating advanced digital marketing data solutions mostly (though not exclusively) centered around Google BigQuery, and spreading knowledge where possible to help others do the same successfully.

My team and I build:
- marketing data lakes & warehouses
- purpose-built BI dashboards - mostly focusing on connecting the dots between marketing spend and getting the actual sales & value (and anythings that support that)
- end-to-end data pipelines for enhanced conversion tracking (esp. for combined online/offline sales journeys) and audience management 
- building machine learning models for advanced marketing use cases (e.g. conversion modelling and boosting for businesses with long sales cycles or low conversion )
- personalization engines and solutions utilizing Customer Data Platforms (or building tailored frameworks better than those with much less cost)

Working with GA4, Google Ads, Meta, Search Console, Doubleclick, CRM, Google My Business and in-house data, integrating them to get deeper insights into how business can be improved. Leveraging first party data for activation to boost advertising or at least simply to provide clarity on what brings value in the business. Providing insights around sales attribution.

Our marketing department is lead by ex-Googlers managing Google Ads accounts in a best-practice and strongly data-driven manner. Our process is based on simple but very effective pillars:
- best practice account & campaign set-up, also leveraging knowledge gained from inside Google
- conversion tracking & data pipelines to make sure Google Ads' bidding AI engine is supplied with the most possible and most accurate / relevant data, essential to optimize bidding and ad distribution
- consulting to optimize the landing pages and website to help you convert the traffic you get and turn it into value
- services on surrounding disciplines that are required to get the most out of Google Advertising: accurate tracking and measurement (incl. server side), search engine optimization, competitor analysis, and data privacy (GDPR & e-privacy)

We work with many renowned organizations like (multiple) large European banks, e-commerce companies.

Balazs Vajna

Principles for modelling #GA4 data in #BigQuery: enormously high turn-out yesterday on Timo Dechau's webinar on this topic!

I love Timo's talks for the clear, no-bs thought process behind them. Never any bloat or unnecessary complexity.

The term “data modelling” in the GA4 world is a “loaded” topic. GA4 data is essentially a “one-big-table” object where information below event level granularity is stored in nested arrays.

One of the powers of this data is that even in its rawest form, it contains rich, detailed customer journey data, which can be used to analyze what customers are going through in your digital platforms. It can also be integrated with offline touchpoint data if you have that — the reason I’m mentioning it here is because the principles Timo talked about are not GA4 specific and can be used on any journey analysis concept. (The online journey side of it can also be based on Amplitude, Snowplow data etc.)

So what is there to “model” about this data? This is what the webinar was about, and it’s worth watching when Timo makes it available. Some highlights:

0️⃣ GA4Dataform by Superform Labs was mentioned as a way to make it easy to digest and analyze. It’s beside the core point for now, but it did come up and I think it’s a good first step regardless of your expertise level and your org’s maturity.

1️⃣ Identity analysis. For customer journeys, this is the most important part, and the most time during the webinar was spent on this. You need to work on these questions and considerations:
🔹what anonymous identifiers do you have?
🔸do you have a logged-in identifier (user_id). How can you (do you want to?) increase login rate?
▪️if not, can you collect user information e.g. emails that you can then tie to anonymous identifiers? E.g. requiring email address for whitepaper downloads. Note that you have to capture the anon. identifier and the email in the same place, e.g. your CRM, to get the match. You can collect the mapping in GA4 too, provided that you hash the emails, to comply with GA4 policies.

2️⃣ Identifying touchpoints. You don’t need everything from the vast GA4 data. What are key events, and most importantly, what are your conversions.

3️⃣ Selecting the “baseline” attribution columns, which further work will be based on — to tell which channel a session / event came from.

Based on these above, you can create a refined customer journey table. It’s not “data modelling” in the purist sense as it’s still on the same (event level) granularity, but now it’s much more usable to gather journey related (e.g. attribution) insights. This is what the next talk with build on.

By the way, if you want to bring these concepts to life, we have a couple of articles on GA4BigQuery regarding cookie vs user_id stitching, and also building customer journey data and performing attribution.

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Head of Analytics at MarketingLens | BigQuery guy

If you want #CampaignManager360 data in #BigQuery, you basically have two options:
💀 the complicated one
✅ the practical one
Most teams pick the wrong one first.

The Data Transfer service in BigQuery is a very handy (and usually free) option to load data from various digital platforms into the database. There is a data transfer for Campaign Manager too.

The problem is, that it is very difficult to set up, but it’s also very difficult to analyze, and lastly, due to GDPR constraints, the most promising analyses on this data usually cannot be pulled off. (Why? There is a User_ID field that would enable seeing the full customer journey in terms of ad impressions; however, most of the time it's simply not populated.)

There is an alternative: the native Export mechanism, using the Instant Reporting feature in Campaign Manager. While the data transfer “promises” event level DCM data (which is true, but unfortunately it can’t be used for much), this approach yields an aggregated data set, with the dimensions and metrics you are actually interested in. (You can pick the desired combination in the instant report.)

It’s relatively easy to set up, once you know that this option is there. A detailed step by step guide has been released on GA4BigQuery — check it out under the “DWH - Data Ingestion” section. It is a free article.

There is only one drawback: the export creates a new table every time it runs, i.e. after a year you’ll have 365 tables in the dataset. Of course, usually one needs to analyze the data over a bigger timeframe, i.e. across multiple tables. Stay tuned for the next article that will explain how to tackle this.

(Has anyone tried the data transfer or this export? What has been your experience?)

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User stitching is one of the strongest use cases of using #GA4 data in #BigQuery.

Have a read of Krisztián's post below for more detail.

User_ID collection, since it's an internal customer identifier, does not fall under the same treatment as cookies and Google Consent Mode does not deal with it. Analyzing patterns off the back of user_id is usually done under the "legitimate interest" lawful basis. Beware though: you can only rely on this if you have done your paperwork right by going through a DPIA (data protection impact assessment), and more importantly - as part of that - you have carried out and documented a balancing test. 

It's worth the effort though, because this is where the really interesting patterns emerge. Back over to Krisztián 👇 

—
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Analytics Engineer @ Data to Value | Co-Founder @ GA4Dataform.com | Freelance Analytics Consultant

User identity in GA4 + BigQuery is one of those topics that affects nearly everything you care about: user counts, conversion rates, attribution, retention analysis.

As it is with a lot of things, user identity is a double-edged sword. You want to preserve the privacy of the users, but you also care about understanding their behaviour.

By default, a "user" in GA4 is just a cookie in a single browser. Same person on their phone and laptop? That's two users. Cookie gets cleared? New user. It's not great, but sometimes it's all you have.

When you do have a user_id though - from a login, an email click, a form fill - that's where it gets both interesting and challenging.

Here's what you can do with it:

1. User stitching at the session level

This is one of our Premium features in GA4Dataform by Superform Labs. You can retroactively "fill in the gaps" for sessions where a user didn't identify themselves, but did so in another session - past or future.

Configuration is straightforward:
USER_STITCHING: { 
   enabled: true,
   dimensions: [],
   lookBackDays: 90,
   lookAheadDays: 10
  }

The result: a more complete user journey. 

2. Identity mapping table

Maintain an incremental table of unique ID combinations (user_pseudo_id, user_id, etc.) that you can join to any other table in your warehouse. This is especially useful for more advanced analysis and activation use cases.
One thing to watch out for: many-to-many relationships between IDs can cause row duplication if you're not careful with your join logic.

3. Offline conversion bridging

A reliable identifier lets you connect online behaviour with offline outcomes - in-store purchases, call center interactions, subscription renewals. This opens up both analysis and data activation possibilities that are impossible with anonymous cookie data alone.

Important context: all of this depends heavily on two things - your consent rates and what percentage of your users actually identify themselves. If only 5% of users log in, even the best stitching logic has limited reach.

This is just scratching the surface - there are probabilistic and algorithmic resolution methods that go much further. But you'd be surprised how far a clean deterministic approach gets you.

And naturally: consult your legal team before implementing any of this. Privacy compliance isn't optional!

#GA4 #Dataform #BigQuery #DataEngineering #Analytics #GCP

If you’re into #BigQuery and digital analytics, don’t miss this webinar by GA4Dataform by Superform Labs. You don’t often get access to this much brain power in one place.

No matter what people say about marketing measurement, attribution has its place, especially when you are in control (and that is the case with event level #GA4 data).

Highly recommended for beginners and seasoned experts alike.

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View company: GA4Dataform by Superform Labs

💥LIVE WEBINAR: Solving GA4 attribution at scale

Attribution modelling is still one of the most widely discussed topics in analytics - and rightfully so, it tries to answer the most important question there is: what drives value?

There are a lot of ways to go about doing it and in our next seminar (with Jules, Johan, Simon, Artem and Krisztián) we'll take a look at what is available natively in the GA4 export and what you can do with it.

📅 March 18 • 3:00 PM CET (9 AM EST)

What we'll cover:
→ Short GA4Dataform walkthrough
→ Attribution modelling with GA4Dataform: available fields, methodologies, theories
→ Your questions answered (submit during registration)
→ Open discussion/Q&A about GA4 export, BigQuery, Dataform, GA4Dataform

Ideal for agencies, analysts and data teams who work closely with user acquisition data.

~50 minutes, informal, no sales pitch.

Sign up:
https://lnkd.in/dtPt72Sq

After the webinar, you will receive a whitepaper on attribution modelling best practices, the GA4Dataform approach, and some SQL code to get you started!

#GA4 #BigQuery #Dataform #AnalyticsEngineering #DataEngineering

Eric's on a roll!
His second freebie this week: #GA4 Audit tool.
I've only used the Sheets add-on version, but that was great - and now this is an upgraded version.
Great kick-starter for #BigQuery folks like me who don't do #GTM and GA frontend that well.

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Follow me, follow Eric, sign up to GA4BigQuery, you know the drill.

Your ecommerce data partner | A full data team without the hiring

I built the best GA4 Audit Tool on the market. No joke. Link in the comments.

Here is what makes it different.

It analyzes your data, not just your settings. Most tools check if GA4 is configured. This one runs 50+ checks against your live data to find what is actually broken. Duplicate transactions. Payment gateways killing attribution. PII leaking into URLs. UTMs silently splitting your data.

It runs on a schedule. Run weekly or monthly audits automatically and email the results to your team. GA4 issues appear gradually. The only way to catch them early is to check regularly without having to remember.

The email shows what changed. Not just what failed. A check-by-check diff against the previous audit so you know in 30 seconds what got better and what needs attention.

You set the thresholds. You decide what counts as a failure for your property. No more false positives.

Your score is tracked over time. Every audit is saved and plotted on a trend chart so you can see whether your data quality is improving or getting worse.

Link in the comments.

We’re hiring!
#ppc #googleads #digitalmarketing

Is your career stuck in a winter rut? It’s time to bloom. 🌸

We are looking for a Medior Performance Marketing Specialist to join our team at MarketingLens & Overlapp Digital—an Irish-Hungarian agency that values your sanity as much as your ROAS.

Why this isn’t just "another PPC job": 
🌿 A Healthy Climate: Say goodbye to toxic agency grind. We trade ego and "crunch time" for kindness and total transparency. 
🏠 100% Remote: Work from your own secret garden (or cozy home office). 🌍 Global Exposure: Manage international and local campaigns across B2B, e-commerce, and app growth. 
🧠 Expert Mentorship: Learn from ex-Google and ex-Oracle seniors who actually want to see you grow. 
💰 European Growth: A salary that respects your 3-5 years of expertise (500k-700k HUF net/month).

Who are we looking for? A Mid/ Senior level PPC pro who is native in Hungarian, strong in English, and knows Google Ads / GA4 like the back of their hand. If you’re ready to shed the "burnout culture" and start a fresh chapter, we want to meet you! Ready to turn over a new leaf? 🍃

Check out the full details and apply in 5 minutes here: 👉 https://lnkd.in/dffu6kJC

#PPCjob #PerformanceMarketing #DigitalMarketingJob #WorkLifeBalance #SpringRenewal MarketingLens Ltd.

The February updates on #BigQuery are coming soon in the GA4BigQuery newsletter — but since the January updates were not circulated on LinkedIn, I thought I’d quickly plug that gap first. Thinking it might be useful for people who don’t receive the newsletter or haven’t gotten around to reading it.

🤖 Conversational Analytics in BQ brings the long-promised ability to “chat with your data” closer to reality. Agents can be configured quickly with guardrails such as knowledge sources, verified queries, glossary metadata, and query limits to keep answers accurate and costs under control, while also generating charts for ad-hoc analysis. The feature works best on top of a well-structured data mart, similarly to dashboards, and is unlikely to replace them anytime soon — but it offers a strong new option for self-serve analytics. I think it carries real adoption potential. See posts by Katie, Johan and myself.

Data Transfer updates:
🛍️The native Shopify transfer brings customer, order, and product (etc) data alongside GA4, allowing analysts to combine clean back-end revenue data with behavioral tracking for more reliable e-commerce performance analysis.
📧The mailchimp transfer makes it possible to analyze owned media performance end-to-end in BigQuery, connecting email sends and clicks with GA4 sessions and conversions to get a full funnel view (assuming proper UTM tagging).

UI improvements
✨ Gemini-based autocomplete operational in the SQL editor (free and baked in — it's not bad!)
📑 Temporary (italic font) vs permanent tabs in Studio (rather confusing!!!)

Other updates
💬 Vibe querying via SQL comments (Sreekanth)
🔐 Strict ActAs / service account enforcement (Yikai & JF)
🔌 JDBC driver support (Matt)
💸 New slot / cost metrics in INFORMATION_SCHEMA (Sayle)
🛡️ New IAM roles (Tim)

Community highlights around BigQuery
📱 Great videos on Firebase / GA4 mobile testing methods (Michal)
🤖 Detecting AI prompts in GSC (Marco)
⚠️ There was a bug with the events_fresh table, containing much less records than usual — fortunately resolved. 
🧠 BigQuery tips collection (Natalie)
🔎 When to use Search Index (Katie)
📚 Dataform vs dbt savings story (Baris)
⚙️ Slot efficiency guide (Yuliia)
📡 Trigger BigQuery jobs via Pub/Sub (Matt)
🗄️ Querying unstructured data / BigLake / GenAI (ManojKumar)
💸 Labels + query limits help control cost (Matt)

Beyond BigQuery 👇
🎤 You can still reach out to me for Superweek notes!
📊 Conversion rate metric map (Dmitry)
🧩 Google Tag Gateway + GCP (Simo)
📘 GA4 admin sheet upgrade (Eivind)
📊 Google Ads dashboard / Share of Search tool (Kishalaya)
🛒 Universal Commerce Protocol (Ben)
🧠 A great ChatGPT pre-prompt idea (Mikko)
📈 GA4 scenario planning (Nichelle)
⚙️ GTM performance best practices (Doug)
🧭 GTM dependency visualization (giordano tabacco)

Links & details in the blog!

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taplio

The Battle

Armin Kakas

Balazs Vajna

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taplio

The Battle

Armin Kakas

Balazs Vajna

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Balazs Vajna's LinkedIn Performance

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