Digital Analytics Expertise, written by Lukas Oldenburg

A collection of recent articles by Lukas Oldenburg, Owner of dim28.

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Bots & Analytics: Common Failing Approaches to Bot Filtering, including AI, and how to solve them

From Filtering out Bots to Filtering in Humans
This is the never-ending story in 2 acts on how to deal with Bots in your Google or Adobe Analytics data.
In part 1, I review common, yet usually insufficient or even completely failing approaches. Why did I give up on AI-driven solutions like ReCaptcha, Akamai Bot Manager or Ad Fraud Detection tools? How good are the built-in Bot Filters? Should you at least maintain Bot Filters/Segments on top of GA views/AA Virtual Report Suites? Why does Server-Side Tracking exacerbate the Bot issues?

In part 2, we will look at a client who saw Bot Traffic surging to over 40%, a case which made me reconsider entirely how to approach Bot Filtering. We show a 2-layered system which turns traditional approaches upside dow - instead of filtering out Bots, the focus is on filtering in humans.

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Use Google Tag Manager Server-Side for Visitor Stitching by Querying a GCP Database in Real-Time

How to use Server-Side GTM + Pubsub + Python + Firestore to get attributes from a database about a User, Products, or anything

I show (including code examples) how you can use Server-Side Google Tag Manager to enrich client-side data (e.g. from a data layer) by querying a database on the Google Cloud Platform in real-time and returning the output to the (server or browser) client.  It uses Cloud Functions, Template Data Storage for Caching and PubSub to generate asynchronous responses. You can of course query any other data in similar fashion, e.g. sensitive product data (e.g. profit margins) or anything else.

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7 Steps to Clean up Adobe Analytics in a Data-Driven Way

Merge duplicate segments and calculated metrics, delete unused components, and get rid of those thousands of old workspaces

Thanks to the Adobe Analytics API, you can finally get your Analytics setup back under control and put an end to user (and admin) frustration because of seemingly endless lists of components with similar names, duplicates etc.

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Find and delete 100’s of unused Adobe Analytics Segments with a few clicks [VIDEO]

The Component Usage Tab in the Google Sheets Adobe Analytics Bulk Component Editor shows what your users (don’t) use

Some months ago, I presented my new pet tool, the Adobe Analytics Bulk Component Editor for Google Sheets. In this video/article, I present an update: There is now a “Component Usage” Tab which shows you not only all Components, i.e. Calculated Metrics, Success Events, Segments, Dimensions or Date Ranges.

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Life with Adobe Launch 3/3: Classic Tag Management skills are not enough

What you need for developing Custom Launch Extensions

In the third part of “Life with Adobe Launch”, I look at the entry bar for developing a Custom Launch Extension in terms of skills and structure. Tag Management specialists tend to have some development skills, but rarely work in the same environments nor with the tools that full-stack developers do. Developing a Launch Extension changes that: You have to learn how to work with the tools of today’s professional developers. Thus, the entry bar may be high. It was for me. But I don’t regret it and learned so many useful things along the way.

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Life with Adobe Launch 2/3: Make it scale with a Custom Extension

How to ensure a centralized and scalable data collection standard across websites with a Launch Company Extension

Part I covered the rocky history of Adobe Tag Management Systems (TMS). It also showed that Launch still has many of the scaling issues of its predecessor Dynamic Tag Management (DTM). Fortunately, Launch offers a way out: Custom Extensions. So if you have multiple sites and want them to adhere to a common tracking standard, such an Extension is a must. Otherwise: innovation freeze and data quality disaster.

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Life with Adobe Launch (1/3): The rocky history of Adobe Tag Managers

Shining at the fancy things while failing at scaling

As amazing as Adobe is in Analytics, as inglorious is Adobe’s history with Tag Management Systems. Launch is the 3rd TMS in 6 years. After the failed “Adobe Tag Manager”, Adobe “DTM” did the fancy, unimportant things well, but choked at what is most important, especially for large enterprises: scale. Launch still has many of these same problems, but offers solutions now.


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Between Hype Machine & Impostor Syndrome — How I became self-employed, Part II

In I looked back at my first steps in self-employment. In this part, I compare today with my initial plans and summarize why things went well — and the one thing that is at the root of all my failures. Also, some thoughts on why it is hard to find out “the truth” on the Googles and Adobes out there.

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“I didn’t do it my way (yet)” — how I became self-employed in Analytics, Part I

Get ready for the attack by the dangerous and risky free market!

Becoming self-employed was quite a change, especially for someone as risk-averse as myself. More than two years later, I look back — and forward. And maybe help you whether self-employment is something you should consider.

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Google Sheets Bulk Component Editor for Adobe Analytics [Video]

Add or remove components from multiple Virtual Report Suites at once, changing (curated or default) names and descriptions of components — all in a simple Google Sheet and with a click of a button, outside the tedious Adobe Analytics Admin interface.

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Video: How to Create Metrics that Count once per Visit or Visitor in Adobe Analytics

One of the first things a Digital Analyst learns is how to ask “what are we counting exactly?” Visits? Hits? Visitors? Visits with Search? Visits from First-Time Visitors? The answer is often not obvious at all, and some tools only offer very limited counting methods. In Adobe Analytics at least, it is very easy to make things count the way they should. This video shows how.

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Why Adobe Analytics’ E-Commerce Data Model is Superior to Google’s

And why Adobe’s Product Management should not forget this

No data siloes, powerful segment-based metrics that work for both product- and non-product dimensions, product-specific attribution, offline data enrichments, all in an intuitive drag-and-drop interface that requires no tech skills to work with — why I think Adobe has the best offer for E-Commerce Analytics.

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Cookie Banners — a Case of Stubborn Minorities without Skin in the Game?

Data protection is like #blacklivesmatter: In public, everyone jumps on the bandwagon. In practice, things look different.

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Beyond the Conversion Rate — powerful Analytics E-Commerce Metrics to Rank Winners and Losers

How “Runner” and “Bummer” scores rank products and make simple reports actionable.

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Es genügt, “nur noch bei wirklich lebenden Beamten nachzufragen”

Wenn immer mehr, aber immer weniger nützliche Daten lähmen — was wir aus dem Niedergang des “landesherrlichen Visitationsverfahrens” im 18. Jahrhundert für heute lernen

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When the Past Changes: The Technology Behind Profit Margins & Actual Revenue in Analytics (Part II)

Datacroft Cost Importer, Consistent Marketing Channel and Campaign Setup, Google Cloud, and Transaction ID Data Source Imports

Part I showed how important it is to become aware of “Actual” Revenue to counter the “Tracked” Revenue, and why a true Performance Marketer should not look only at the (Tracked) Revenue per Cost (misleadingly called Return on Ad Spend (ROAS)), but at the Profit Margin per Ad Cost. Part I also showed: For such a complete picture, you need to blend a lot of data sources. Thus, part II highlights the components required to get all that data together into Adobe Analytics. The “changing past” becomes a special challenge here.

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Profit Margins & Real Revenue in Adobe Analytics: Part I

Erasing the Blind Spot of Marketing Measurement.

Your Analytics Revenue is misleading— in some cases it is over 50% off. But not because of tracking errors! Start to understand your real Marketing performance: Analyze your Campaigns (and anything else people do on your site) by the actually invoiced Revenue and Profit Margin — all while staying within Adobe Analytics.

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Google, please stop hyping Firebase and Web+App Properties like the Garden of Eden!

Over the last year, a client has been rolling out a large online shop’s new website and mobile app. Since the customer is all in on Google’s Analytics and Marketing solutions and the old Google Analytics Mobile App SDK was announced to be sunset, “Google Analytics for Firebase” and the promising “Web+App Properties” seemed like the logical solution for tracking the app. After all, there was no Google event, no Google blog post where Firebase was not hyped as if it were the new Garden of Eden. Maybe the less finished a product is, the more it needs to be hyped.

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So how many Users do these evil Ad Blockers kill? A Checklist & Guide for Comparing Apples to Apples

I struggled with finding a decent title for this second part of my series on Ad Blockers, ITP & other browser restrictions, and Server-Side Tracking. I tried “When an Order is not an Order”, “Help! Ad Blockers eat up 60% Percent of our Revenue in Analytics!”. As I studied Cultural Sciences about 70 years ago, I finally wanted to go for a more academic branding: “Comparing Apples to Apples or: A Primer in Constructivism”.But that’s not “SEO-/Social-Media-friendly”. Best of/Pest of Lists are. Users getting wiped out (from your Analytics data), too. So you get the headline you deserve. Full of blood and exaggerrations. You still want to know what Constructivism has to do with Ad Blockers? Great. …

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3 steps to a Tealium Queue to avoid lost data on Single-Page Applications

How to enhance Tealium with a “utagQueue” to avoid losing data if users start clicking around before the Tealium library has loaded.

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Ad Blockers and Server-Side Tracking, Part 1: The Ever More Challenging World of Client-Side Tracking

In the first part of this series on Ad Blockers and Server-Side Tracking, I will look at how the proliferation of technologies like Safari’s ITP have put the good old “client-side” tracking into limbo. This will ultimately lead not to less tracking, but instead to less transparent tracking — at least for those companies that can afford it.

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7 Steps to Clean up Adobe Analytics in a Data-Driven Way

Merge duplicate segments and calculated metrics, delete unused components, and get rid of those thousands of old workspaces

Thanks to the Adobe Analytics API, you can finally get your Analytics setup back under control and put an end to user (and admin) frustration because of seemingly endless lists of components with similar names, duplicates etc.

Read Full article on medium

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