Yes, everyone is saying stuff like "data is the new oil" these days. In your organization however, data has the look-and-feel of radioactive mud? You are not alone.
dim28 knows what the reality of Marketing Technology and Analytics looks like - behind buzzwords and self-lauding tool vendors.
Client- or Server-Side Tag Management for websites, mobile apps or IOT - get the data where you need it in a sustainable and scalable way. No need to fear ad blockers, but still privacy-compliant.
Great talent is hard to find. Rent me as your Interim DIgital Analytics Expert or Analytics Project Manager and get your Digital Analytics setup or team up to speed - or back on track. I can also organize additional resources.
Drowning in thousands of Adobe Analytics Segments, Metrics, Dimensions or Date Ranges? Stay afloat and help your users with the Adobe Analytics Bulk Component Manager for Google Sheets.
From Real-Time Multi-ID User Stitching to Triggering Flexible Actions
The number of vendors claiming to offer a Customer Data Platform (CDP) has skyrocketed. From e-mail specialists to tag management providers — as long as a product used to offer something with customer data and had some APIs for data input and output, it now is likely called a “CDP”. The CDP Institute’s “Vendor Comparison” lists 53 solutions alone! Let’s try to define a CDP and look at its most important components.
Heavenly and Devilish Examples of Server-Side Tracking
Server-side tracking, especially in the form of server-side Tag Management, is one of the hottest trends in Digital Analytics and Marketing. This article gives an introduction and highlights the potential for good and bad. And when did “first-party data” mutate into an irony of itself?
How to Measure User Adoption of your Adobe Analytics Setup and Prevent Co-Workers from Churning
The account usage logs of Adobe Analytics offer a lot of cool data to monitor if the Digital Analytics team is doing their job. However, they are not without pitfalls. A Project View may not be what you think it is. How fresh is the data? And how do you deal with the different logging languages? A checklist and some code examples in the Part 2 should help.
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.
When you tell Google you earn 100 dollars, but in fact you earn 47, it is time for "Bottom-Line Analytics"!
Lukas's first webinar at the renowned SDEC showed why Conversions can be bad for you - due to the so-called "blind spot of Marketing Measurement". He showed, with real-life examples, how to shed light on the often vast gaps between tracked and actual "bottom-line" revenue. Moreover, Lukas outlined how you to get Ad Cost and Bottom-Line Revenue and Profit into Adobe Analytics.