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When I do this in a pivot table, it repeats the format again for every date within that month. I.e. i have 10 rows that say "April 2024" if I had data on ten different days in April of 2024. Any way to fix this?
Hi there! Unfortunately, the GA4 API doesn’t support segments (developers.google.com/analytics/devguides/migration/api/reporting-ua-to-ga4#not_supported_segments) like GA did (which is featured in this video). Since the API doesn’t support segments, we're unable to support segments on our end.
This is a weird place to ask, but I am a data scientist with a master's degree in artificial intelligence. Have 2 years of experience in MMM, MTA, and uplift modeling. I am looking for full-time positions. Please let me know if you have any opportunities.
Hey there! Feel free take a look at our careers page and apply any roles that match your skillset - good luck! supermetrics.com/careers/join-supermetrics
Thanks for flagging this. The SQL query in the report was missing the line with the time stamp (Numeric time stamp will work as well) I updated the report with these lines: ... event_timestamp as timestamp, CAST(event_timestamp AS NUMERIC) as numericTimestamp, TIMESTAMP_MICROS(event_timestamp) AS event_date_time, ...
Hey! Can you explain how you created the Link Limit Dimension in the first place in GA4? Unfortunately we cannot use it in your template, therefore we wanted to create it by ourself - Help would be very much appreciated!
Hi Adrian, The link limit is a style setting. We exposed this style setting to be accessible via a Looker Studio parameter. The value of this setting is then passed into the JavaScript code, which reduces the number of links. These are your two options, using the style setting or the parameter. On the data level, limiting the number of links will be very complicated.
Great interview, where are the LinkedIn profile link, and links to the site ,I know there is on video but please put here too o message and video description
I believe I made it work with the numeric timestamp as shown in page 2. I am curious, my ga4 is installed for a few months, but I just made the Bigquery link and created this new view. The table shows only a few users, does this mean it doesn't push all past data? Thanks a lot for the widget and explanation, it is very promising!
Great webinar! At 28:14, how do you split Brand parameters to show a breakdown of Ad Recall, Positivity, Preference and Awareness along with their respective percentages? Where are you getting this data from? Does the client send you this data to be added in the model?
Hi there, great video, thanks @Supermetrics. Could you share the source of this piece of info from the video? "Up to 70% of traffic is comprised of ad and click fraud."
Hey Charlotte! Sure, this stat is from a report published by Arkose Labs: www.arkoselabs.com/wp-content/uploads/Breaking-Bad-Bots-Bot-Abuse-Analysis-and-Other-Fraud-Benchmarks.pdf
🎯 Key Takeaways for quick navigation: 00:21 🤖 Google has been working with AI technologies, including models like Transformers and AlphaFold, for almost a decade, and these models are continually improving. 01:36 🌍 When implementing AI solutions, it's crucial to consider their impact on users and business partners, and responsible usage is key. 03:15 👁 Google is moving toward more visual search experiences, blending text and visual elements to enhance user experiences. 04:23 📊 Generative AI functionalities will be used to answer new types of questions and simplify search while clearly labeling and integrating ads. 05:05 💰 AI-based solutions have led to increased revenue and cost reduction, with human inputplaying a crucial role in enhancing performance. 07:06 📈 Google's Performance Max campaign type leverages AI to deliver better results, such as increased conversions and return on ad spend. 08:03 🖼 Creative content is a major driver of advertising performance, with features like Flip Video helping to optimize visual assets for various platforms. 09:14 🛍 Google is launching features like Product Studio to improve product image quality and offers conversational experiences for headline text and body content. 12:47 📊 Using first-party data is crucial for powering AI campaigns, with the potential for driving new customer acquisition using customer match data. 15:04 📉 AI can help extrapolate insights from first-party data and enhance conversion measurement, especially as third-party data sources shrink. 17:10 📊 Google is introducing AI in local Studios and Looker Studio Pro to simplify data analysis and reporting, saving time and minimizing the need for technical expertise. Made with HARPA AI
🎯 Key Takeaways for quick navigation: 01:02 🚀 Supermetrics' success is rooted in understanding and solving a common problem - the burden of collecting and managing large volumes of data. 03:04 💡 Start using data early in your business, as it guides decision-making, optimizes investments, and uncovers opportunities. 05:51 📊 High-quality data is crucial for adapting to changes in marketing, like privacy concerns and AI, and achieving a competitive advantage. 08:25 💰 Maximizing ROI through data requires high-quality data, the right tools, and measurement strategies tailored to your organization. 14:03 🌟 Supermetrics aims to provide added value by helping customers take ownership of their data, transform it for insights, and use data to look into the future. Made with HARPA AI
🎯 Key Takeaways for quick navigation: 01:07 📊 Technical marketers and non-technical marketers should recognize the increasing importance of technical skills in marketing, as digital marketing requires using sophisticated tools and data analysis. 05:31 🤝 Big data is vital for marketers to personalize customer experiences, target audiences effectively, and analyze ad performance. However, marketers often struggle to know where to start with big data. 12:51 🛢️ To work with big data for marketing, you should understand the concepts of data warehouses, data lakes, and data marts. These are essential for storing and managing data effectively. 15:18 📈 Data visualization tools and business intelligence tools are valuable for exploring and making sense of big data, while basic SQL knowledge can help marketers navigate and analyze data. 18:21 🔒 Ensure data privacy when connecting marketing tools with data sources and warehouses to protect customer information. Understand the data flow and use connectors to streamline data integration. 28:15 🛠️ Consider using free websites and tools to learn SQL, such as Cloud Skills Boost, for hands-on exercises. 30:10 📚 Explore educational resources and labs provided by Google, like Cloud Skills Boost, for practical learning. 32:16 💼 When should an organization look into Data Warehouse? Consider starting when you have more than two data sources for better analysis. 33:33 📖 To learn SQL and become proficient, choose a data warehouse tool first, then find specific courses related to that tool. 34:48 🏭 Data Lake and Data Warehouse work together, with Data Lake accepting both structured and unstructured data for preprocessing before transferring to the warehouse. Made with HARPA AI
🎯 Key Takeaways for quick navigation: 00:34 📊 Marketing Mix Modeling (MMM) is a statistical analysis technique used to quantify the impact of various marketing inputs, helping examine historical data and measure campaign effectiveness. 02:55 📈 MMM has evolved from a slow, artisanal process to a digitally-enabled, faster, and more automated next generation, combining statistical rigor with modern machine learning and cloud processing. 04:21 📊 Sales data and media spend data are primary inputs for MMM. Gathering data now often involves APIs, enabling faster and more accurate data collection. 06:10 📈 Validation of MMM models is crucial for ensuring accuracy and reliability. Real-world incremental experiments help minimize bias and ensure robust results. 09:15 🚀 Future trends in MMM include the broader adoption by various businesses, clear data strategies, and infrastructure development. Expect more semi-automated modeling solutions and continuous calibration. 19:22 💡 Implementing MMM depends on the goals, talent, and resources of the business. In-house development is feasible but requires expertise and resources. Collaboration with trusted MMM providers or consultants is an option, as is starting small and gradually scaling. Clean and comprehensive data is crucial for accurate modeling. 24:20 🧠 Prioritize strategy mapping with an expert to analyze weaknesses and set the foundation for successful Marketing Mix Modeling (MMM). 24:47 🔄 Stay updated with the evolving marketing landscape and revisit and recalibrate your models as needed, considering the speed at which you make decisions. 25:26 📈 Real-time data analysis may not be necessary for all businesses, and the frequency of model review should align with decision-making cycles. 25:55 💡 Integration of insights from subject matter experts can enhance MMM, as they can provide valuable context and application of model recommendations. 27:02 🤝 Collaborate with experts in the specific field being measured to ensure that the model's insights align with your strategy and goals. Made with HARPA AI
🎯 Key Takeaways for quick navigation: 12:23 📊 Using data to influence business strategy and decisions. - Shifting the mindset from historical data to people-focused data. - Tailoring data insights for different users' specific needs. 19:26 🧩 The importance of small data and decentralized approaches. - Recognizing the power of small data sets and manual data work. - Balancing centralized and decentralized data solutions for efficiency. 23:40 🔍 Dealing with Data and Tools - Marketers discuss their preference for using tools like Google Sheets for data analysis, and the importance of having control over data. 26:01 💡 Using Data for Storytelling - The panel emphasizes the significance of using data to tell compelling stories as marketers and how traditional attribution models are evolving. 32:55 🤖 AI in Marketing - The discussion shifts towards AI in marketing, its impact on jobs, and how it can enhance efficiency and productivity. 44:26 💬 Attribution Challenges in Marketing - The panel briefly touches on attribution in marketing and the complexity of measuring the full customer journey post-privacy updates. 46:02 🧩 Attribution in Marketing - Attribution models don't reflect the complexity of customer journeys. - Data should be viewed more holistically to avoid misattributing impact. 48:03 🚀 Key Takeaways - Josh: Understand that data doesn't exist in a vacuum; focus on soft skills and human factors. - Dary: Accept that data is inherently messy and can't be perfectly cleaned. - Jessica: Know your product life cycle, set appropriate KPIs, and diversify your media mix accordingly. Made with HARPA AI
Hey @jaywilner4187! We asked Zach for some recommendations on resources! One thing to note is that there's a difference in resources for those that want to perform and build MMM's, and those who want to educate themselves enough to use an MMM. Both require quite different resources so the answer depends on the purpose you have in mind. If you want to understand & build an MMM: - From a statistical starting out perspective, ISLR is the quintessential book and the one Zach used at the beginning of his journey. Once he completed that, he moved on to ESL to form a deeper understanding of statistical approaches. After all, at it's core MMM is a regression so understanding the facts of different types of regression analysis is paramount. - An Introduction to Statistical Learning: with Applications in R by James, Witten, Hastie, and Tibshirani - The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Hastie, Tibshirani, and Friedman That should give the confidence in statistical knowledge and some programming knowledge to move into the more specific spaces of marketing research and analysis. For those, Zach recommends: - Understanding Advertising Adstock Transformations by Joseph - R /Python for Marketing Analytics and Research by Chapman If we want to discuss simply how to use these methods, or how to start searching for an MMM provider or tool, Zach recommends more online methods that don't involve months of learning statistical rigor. - An Analyst's Guide to MMM published by Meta is an incredible resource - The MMM Handbook by Google is a quick 7 page read that can help you start to contextualize the conversation in your organization bout how, why, and who you should partner with - Build Trust in Marketing Mix Modeling Across your Organization published by Gartner is a great asset in how to shed some of old suppositions about MMM and show thoughtfully how the market has changed and can support the models. Hope these help!
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This ad was very well edited. I really love the smooth transition during the zoom inside her blue eye 👁️⬅️🔍sequence to express what she was thinking (stress/pressure of the deadline) before coming up with the solution! Well done!
