Timo Dechau

Help your future self. In a previous post, I talked about DATA HELL. This is Part 2! The CEOs in ecommerce that are just a couple of years ahead of you - they run into this problem: They didn’t manage their data – at the point in time YOU are now in. Then later, they’re in serious trouble. Just like you, they were busy getting their business off the ground. They were busy playing that game of simply making money, here and now. Later, many of them go through DATA HELL. Here’s a great little bonus list of data you should capture well in the earliest stage of scaling your business: • Go all in on improving product data quality. E.g. if you have tagged a product with e.g. age group, it creates opportunities to identify the age of the customers (or the customers' parents). If someone buys something for the age group 1-2 years, you know when you should target them with stuff for 2-3 years. Use a uniform taxonomy and don’t get creative with adding age brackets ad hoc. • Understanding which products are the best basket openers and get customers to come back (Product LTV + Retention rate). • What people are searching for and not finding. • Tracking when people buy which products (time of day/week). We can easily add 30 more data points but these can turn into gold later. So, dear ecommerce founder, CEO, innovator or whatever you call yourself. If you’re in that early stage of fast growth, know this: Every hour spent today on improving your data will give you 100 hours back in a couple of years. Help your future self. Just add 🧠 Kasper Refskou Jensen Hello Retail

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Got an interesting question from a $100M+ online retailer. How can we improve site speed before holiday (without impacting our tracking)? These guys have: - Dozens of marketing pixels and tracking scripts - GA/GTM stack - Some server-side tracking (not all) Here's the quick approach they (and you) can take: Pixel Audit and Cleanup - Strip out unused libraries and pixels - Disable any redundant or outdated events - Hunt down and fix broken pixels You'll also find that analytics and other 3rd party pixels (often) aren't the primary site speed blockers. Instead, some upstream resource. So run your site through Google Lighthouse to identify blockers, and improve your "first contenful paint". Tag Managers Migrate all 3rd party scripts to a tag manager (GTM, Tealium, Adobe Launch, etc.) Benefits: - Better data governance - standardization - script modification - caching - sequencing control Server-Side Tagging Move clientside tracking to server-side capabilities for platforms like Meta, Google, TikTok SS Tagging can reduce browser load (crucial for mobile), but more importantly improves data quality to ad platforms. More data = more signal = smarter algo = increased ROAS, lower CAC. Smart Tag Sequencing - Defer non-critical pixels to load later - Use tag manager features for async loading The TLDR - Ditch unused pixels/libraries - Run your site through Lighthouse, fix blockers - Use a tag manager - Fix any broken tracking - Optimize load sequence - Set up server-side where possible This approach should significantly boost mobile performance without sacrificing your marketing data. Remember, with the holiday rush and heavy mobile traffic, every millisecond counts. What other tracking or site speed tactics would you add to prep for the Black Friday pop? Drop your thoughts below. #ecommerce #siteoptimization #blackfridayprep

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Ecom Brands: 10 Common Data Issues We Come Across 1/ Attribution Missteps Relying on outdated MTA tools or last/first touch attribution instead of contribution and incrementality models. This can lead to wasted ad spend. Start with simple geolift (MMT) and hold-out tests to measure incrementality. Understand that not all channels can be tracked w/ click-based tools. 2/ Pixel Problems Incorrect, duplicative, or missing ad pixels lead to inaccurate data collection. Audit your pixels monthly for accuracy. GA, Meta, Google, TikTok, Affiliate platforms, etc. 3/ Server-side Tracking Not using server-side tracking, resulting in signal loss and hindering ad optimization. You can be losing 20-30% of your conversions w/o proper SS setup. 4/ Excel Hell & Reporting Inefficiencies Manual and infrequent updating of reports, often trapped in spreadsheets. Automate common reports. Even basic KPIs updated daily for the team will be a big win. 5/ Product Catalogs are Stale Bloated or incorrect product feeds with outdated information and mismatched pixel data. Audit your catalogs frequently. Segment your feeds. Setup alerts. Use a centralized feed source or vendor to streamline updates. 6/ UTM Inconsistencies Poor campaign and UTM naming standards make performance breakdowns difficult. Create a standards document and ensure that all your marketers and agencies use it religiously. 7/ Customer Data Fragmentation Lack of a single source of customer truth, with customer and 1P data spread across various systems. Start to ingest your 1P data into a data warehouse. You should own and know how to govern your customer data. 8/ KPI Blindness Limited understanding of critical metrics like New Customers, LTV, MER, Contribution Margin, CAC... Knowing which to use and when. Understanding driver trees and ensuring your team knows the key metrics for their channel. 9/ Inaccurate Forecast Failures Poorly constructed forecasts or a complete lack of forward-looking projections. Even a basic forecast, shared across the team will help. 10/ Data Silos Isolated data and poor transparency across different teams and departments. Centralize datasets, KPIs and reports where possible. Better communication and data access. The good news is that fixing these issues is 25% technical cleanup and 75% education/process improvement. What other data challenges do you face in your e-commerce operations? #ecommerceanalytics #dtcmarketing #measure #ecommerce

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🚀 Autumn is here, and these are the posts I've bookmarked for further reading, focusing on Google Analytics 4, GTM, BigQuery, and more. 🎥 Timo Dechau has started a video series comparing Amplitude with Google Analytics 4, As of now, there are three parts available. Amplitude is a fresh and modern alternative, definitely worth checking out.   https://lnkd.in/d3zJye_S 📝 Taneli Salonen, the Dataform guru, has written a post about how to document your tables using Dataform, one of many convenient features. It's a great and clear guide.   https://lnkd.in/dzzdQyWr 📊 Marco Giordano put together an interesting presentation about working with Google Search Console exports in BigQuery. The presentation is both a good introduction to BigQuery and its structure, and it also offers some smart use cases for blending data from Search Console and Google Analytics 4. Definitely worth bookmarking!   https://lnkd.in/dy4Hi3xT 🔐 Kolja Siegmund has written a neat post about hashed data and common misconceptions related to personal data and GDPR. Well worth reading before diving into data privacy discussions.   https://lnkd.in/dEekQR5F ⚖️ “Cookie banners show everything that's wrong with the EU”, says David Heinemeier Hansson. I couldn’t agree more. It's a short blog post and sums up the current landscape of regulations.    https://lnkd.in/dZEeUmVn 📈 We keep getting a lot of interesting and useful updates to Looker Studio. (If only GA4 had the same pace of progress!) A handy new feature is Funnel Charts. Imagine wanting to visualize how users proceed through different steps in a customer journey such as a checkout. Check the demo on Mehdi Oudjidas post. https://lnkd.in/dwmX-RHf 🤖 AI is becoming truly useful now that it's finding its way into our tools. Gemini is now available in Looker Studio and can help generate presentation slides with graphs and insights directly from your dashboard. This is truly helpful!   https://lnkd.in/djRrJrX7 🎙️ Finally, MeasureSummit is happening from October 9-11. It’s an online conference, and you can watch a lot of great sessions even without paying. Grab a coffee and dive in for a knowledge injection.   https://measuresummit.com 📅 If you're around Malmö on October 23rd, make sure not to miss the next Digital Analytics Meetup by Ctrl Digital, this time with a focus on privacy! They're looking to add more seats, so join the waitlist or reach out if you're interested in attending. https://lnkd.in/dqiu_Npq

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If you have just woken up and your thoughts were clear - omg, I have so many questions about Amplitude and Google Analytics 4. Or if you have a dedicated notebook where you write down all the questions, just in case when Timo, at some point, will do an AMA about GA4 and Amplitude. And if all of this is not true for you, you should not miss it. I will do an ask-me-anything session about Amplitude and GA4 today. I have collected some questions, but I would love to hear yours. The link to the live event is here on LinkedIn in the comments.

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Champify's homepage hero is so good: 💪 promises a reasonable and valuable capability 🚚 explains which feature makes delivering that capability possible 📍 summarizes it all with a believable outcome 🎥 gives a "show me" next step 🤨 gives a "let me see" next step 🙋 describes customers in a flattering and relatable way Do more of what Taylor Udell does.

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🚨 Whenever you use a closed funnel in #GA4, make sure that you are tracking each step of the funnel. Otherwise, your report won't produce accurate visualization, and your data analysis won't be accurate. For example, By default, both 'Purchase Journey' and 'Checkout Journey' monetization reports employ a closed funnel approach. What that means is that they only include data for users who successfully navigated through each step of the funnel. Users are only tallied in the specific steps they complete in the designated order. If a user skips a step, they are considered to have exited the funnel and are not included in the subsequent steps' count. . . Similarly, if you skip tracking a particular step, it is equivalent to users skipping a funnel step. So, they are considered to have exited the funnel and are not included in the subsequent steps' count. Since, by default, the user purchase journey report is a closed funnel, it is useful for data analysis only when you are tracking all of the following events: 'session_start', 'view_item', 'add_to_cart', 'begin_checkout', 'purchase' (or 'in_app_purchase') events. If you are not tracking any or all of these events, you won't see accurate data visualization in the purchase journey report. . . For example, If you are tracking 'session_start' but not 'view_item' event, then you may not see any progression in your user purchase journey report after 'Session Start' even when you are tracking all other relevant events ('add_to_cart', 'begin_checkout' and 'purchase' events). . . The same logic applies to the 'Checkout Journey' report. Check the screenshot below. If you are tracking 'begin_checkout' and 'purchase' events but not 'add_shipping' and 'add_payment' events, you may not see any progression in your 'Checkout Journey' after 'begin_checkout'. Takeaway: In a closed funnel, if users skip a step, they are considered to have abandoned the funnel at that point. They are not included in the analysis for any steps beyond the one they skipped.

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Renaming events in #GA4 is not recommended, as it can skew your analytics data for good. Renaming a GA4 event does not retroactively change the historical data. The new event name will only apply to data collected after the change. Your historical data will remain under the original event name. This means you will have two sets of data: one for the original event name and another for the renamed event, starting from the time of the change. You will end up with two data sets for the same user interaction but under different event names. It will become harder to track the performance of a specific event over time when the data is split between two or more names, esp. if you are not aware of the name change. You will have to merge the old and new event names manually. GA4 does not automatically merge data from renamed events. Your reports could become misleading to users unaware of the name change. If you rename a GA4 event with a new event name exceeding 40 characters, it will no longer be processed. Instead of renaming an event, create a new event. Note: Renaming a key event does not automatically remove its status as a key event. You would need to manually remove it from the list of key events if that's your intention.

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Why is GA4 pushing me to install a gtag? If you are just setting up Google Analytics 4 (GA4), you will be prompted to install a script like the one you see below during the onboarding process. But why is that? Shouldn’t you use Google Tag Manager (GTM)? Honestly, I have no idea why Google pushes such methods (even for Google Ads) instead of GTM. What I can tell you is that in most cases, you will probably be better off using GTM. I think this prompt does more harm than good, as I see some GA4 properties with the gtag and GA4 running through GTM at the SAME TIME. Your gtag will overwrite whatever settings you have through GTM if you keep both configurations running, wreaking havoc on your tracking. What to do if you suspect you have a gtag on your website? 1️⃣ Open the inspection tool and look for ‘gtag’. You will notice a script like this: <​script async src="https://www[.]googletagmanager[.]com/gtag/js?id=G-XXXXXX"><​/script> If you don’t like this method, install this extension: https://lnkd.in/eBHrHUeD Once you pin it, click on it and look for ‘gtag[.]config’. You will see all your hard-coded scripts. 2️⃣ Tell your developer to remove it. This is the easy part if you have access to a developer. If not, then you probably used some sort of plugin to add the ID. Look for the plugin and just remove the ID. #ga4 #digitalanalytics — - — — 📕 Download the server-side tracking guide for agencies: https://lnkd.in/e-7V4q3T

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On Saturday, I was headed to work to play a spirited game of foosball with my wonderful grandkid Sydney. She was eating a popsicle and, as we walked, part of it fell off and landed on the ground. Oof. We all know how painful that is. The dropped popsicle. The dropped ice-cream cone. What a missed opportunity - the pleasure we could have enjoyed! That's the feeling I get when I open up a GA4 property and see it's not connected to BigQuery. What an opportunity wasted. That's why I wanted my next two CXL Feeds to be about connecting GA4 to BQ. I suspect there are two reasons people haven't connected GA4 to BQ. 1. They're scared of the cost. 2. They're scared of creating a BQ account (or don't know how to do it). In this first CXL Feed, I'll address the cost issue, and show why for most SMB's, BigQuery will be free - or very very inexpensive. In the second (upcoming) CXL Feed, I'll show how to set up a free BQ sandbox - no cost, no worries, no credit card. These two CXL Feeds may not prevent your popsicle from falling on the ground, but they will get you going on the road to BQ/GA4 integration - one of the sweetest pleasures in the digital marketing world.

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A bit late to the party, but let's see the growth of Calendly-Alternatives.org. Many talked about how an exact domain beat the SERPs (rankings). 👁 Looking at the graphs of Traffic vs. Links in parallel... ... you see a little bit of correlation. We know that links are not immediately discovered by Google. ⏰ It takes some time. When the people behind Calendly Alternatives bought the domain, they were not after ONLY the exact domain. What they did was: ✔ Get an exact domain ✔ Build links* ✔ Publish some content ✔ Take some time & patience** *They spent more than 1 year building links. **And probably benefited from an algorithm change or re-evaluation. ❌ There is not an exact correlation between links & traffic, though. There is some relevance, seeing both growing together. The reality is... ...A combination of tactics achieved the results. My point is, they didn't just buy an exact domain and hope to rank. They invested in link-building for a long time. They wrote some supportive content to improve average site quality. They might have used other tactics as well. ⛰ The latest peak After a peak and fall, there was a "resurgence." That's probably when everyone started talking about the domain. Direct & brand searches spiked, giving it another boost. 🎯 Results? I am pretty sure the creators got some nice incoming traffic & leads.

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Unlike Universal Analytics, #GA4 does not have the ‘Full referrer‘ dimension. So, it becomes difficult to see the full referrer in GA4. But there is a workaround. You can create a new exploration report and apply the ‘Landing Page+Query String’ and ‘Page Referrer’ dimensions to it. The ‘Page referrer’ dimension shows the full referral URL. However, it can also show your website domain if that was the user’s previous URL. The ‘Page referrer’ dimension can also show referrers from search engines like Google and Bing. It can also show blank values when the referrer is not recorded (maybe because of adblocker, privacy extension, etc). So, you will need to apply several filters to make your report usable.

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Dreamdata + G2: A Game-Changing Partnership! 🏆 We’re buzzing with excitement at Dreamdata because we just became a G2 Partner as part of their new attribution and portfolio visibility launch. 🙌 Why should you care? With this partnership, B2B marketers that love reviews (like we do) can finally measure the true impact of G2 Buyer Intent data on pipeline and revenue. No more gut feelings - just cold, hard data. Here’s what’s in it for you: 👉 Full journey visibility: See exactly where G2 touchpoints fit in your customer journey. No more blind spots. 👉 Track ROI: Know how G2 stacks up against your other channels. Optimize your budget like a pro. 👉 Uncover hidden demand: Identify those high-intent accounts lurking outside your CRM. Engage them before your competitors do. 👉 Build Better Audiences: Sync G2 insights with your ad platforms to target the right people at the right time. This is a big deal for anyone who’s tired of debating G2’s value. Now, you can see it, track it, and act on it. #B2BMarketing #RevenueAttribution #Dreamdata #G2Partner #DataDrivenDecisions #SaaS

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How the smart bidding algorithm learns… and what that means for our choice of match types. Extract from the upcoming course unit How to Use Match Types Well. Coming next week in Google Ads Level Up https://lnkd.in/eQ5Ne67K

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I exclude brand from pMax 99% of the time. Here's why (3 reasons): 1. It inflates my data. 2. The goal is to scale my account efficiently, not only Performance max. 3. I'm probably overpaying for branded clicks on pMax. I use SMEC's Brand Traffic Analyzer script to analyze my share of branded conversions over time. You can find it in this article I wrote (script number 1): https://lnkd.in/eRsEqafJ p.s. there are also 4 other useful pMax scripts in there! Have you already used this script by SMEC?

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0.3kb vs 7.5kb It's fun looking in the tags of other AB Testing tools to see how they do things. The above is a comparison in size of Webtrends Optimize sending data into GA4 vs. how one of our competitors does it. Just one tiny, teeny little action that our tags both do, and the size difference is insane. Most other AB Testing tools I've seen have very bloated tags. They're written in ES6 and run through a transpiler to output "friendly code" that's twice the size. When in reality, if you'd have just written it in the friendly format yourself, it might be 5% bigger. People also over-engineer problems, writing 1000 lines of code to deal with scenarios that aren't relevant or won't happen but "just in case it does". A smaller tag, adding features if/when you need them, and solid masking principles are a few of the reasons our tag scores so well for performance in Lighthouse.

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SHOULD YOU TRUST IN-PLATFORM ATTRIBUTION MODELS? TL;DR: probly not. Attribution is tricky. Last-click attribution is also, uh, sketchy. Why? 1) It often confuses correlation with causation. 2) It rarely accounts for 10 or 12 other factors that are likely involved. 3) It’s got conflict-of-interest built in: of COURSE a platform wants to claim brand lift or sales impact or whatnot. Econometric models are better (the good ones are). They factor in LOADS of things to tease apart effects. Is it using correlation to assume causation? Mostly yes. But it’s miles better than naïve attribution models. magic numbers compared 5 years of Google attribution model data to their own econometric model. According to Google, ~33% of sales could be attributed to someone clicking a google ad. Woop! According to magic numbers, only ~9% was rillly incremental. Oopsie. The rest was people who were about to buy anyway, but The Goog was taking credit for them. SOME LESSONS: 🔸 Be wary of what a platform claims it delivers: Mandy Rice-Davis Applies 🔸 Use a good econometrics model to get a better picture of incrementality & attribution. 🔸 Look up Mandy Rice-Davis. #marketing #marketresearch #dataliteracy

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Need an easy way to export your UA data before July 1st? I'm sure you've seen 454654 posts and banners about this.. but Universal Analytics is getting deleted on July 1st, so you will need to export your data. I have made a Google Data Studio report where you can select your UA property and export the data quickly. This should fit the needs of most websites. Don't worry, no one can see which properties you have in this view. The data control is specific to what you have access via the email you access this Google Data Studio report. You may need to change the goals on page 8! You can export this as an Excel file or Google Sheets. . . . . . . . . #googledatastudio #googleanalytics #analytics

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⏰ Final days to export your Universal Analytics data... See below for my template for a basic export of UA data. These csv or Google Sheets files can then be uploaded into a database or to Google Data Studio.

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