Unifying email, web, and ad data for marketing analysis

𝐇𝐨𝐰 𝐈 𝐁𝐮𝐢𝐥𝐭 𝐚 𝐌𝐮𝐥𝐭𝐢-𝐂𝐡𝐚𝐧𝐧𝐞𝐥 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐔𝐬𝐢𝐧𝐠 𝐎𝐧𝐥𝐲 𝐒𝐐𝐋 That’s how I built a Multi-Channel Attribution Dashboard that tracks revenue back to the real drivers, across Google Ads, Facebook Ads, email, and organic traffic — without a single BI tool. Here’s how it happened - The Problem: The client was spending across multiple channels, but GA4’s last-touch model wasn’t giving the full picture. Marketing teams were flying blind, and every channel wanted credit. So, I set out to answer: "Which channels actually drive conversions, and how do they work together?" The Solution: SQL-Only Attribution in BigQuery Using only SQL in BigQuery, I built a dashboard that: ✅ Tracks first-touch, last-touch, and linear attribution ✅ Allocates revenue proportionally to every user journey ✅ Connects ad spend to conversions across sessions and sources ✅ Supports flexible filters by date, campaign, device, and region How I Did It (Simplified) 1. Unified All Touchpoints • Pulled raw GA4 events data, including session_source, user_pseudo_id, and event_name. • Mapped all key user interactions — from ad clicks to checkout completions. 2. Created a Conversion Timeline • Used LAG() and ARRAY_AGG() to reconstruct user journeys. • Tracked each session leading up to a conversion event. 3. Applied Attribution Logic - Wrote modular SQL views for: • First-touch • Last-touch • Linear Each logic had its own SQL CTE, allowing a quick switch for comparison. 4. Joined Spend Data • Brought in Google Ads + Facebook Ads costs from external tables. • Linked spend to sessions via gclid and UTMs. 5. Final Output - A single BigQuery table showing attributed revenue by: • Channel • Campaign • Source/Medium • Attribution model Bonus: I connected it to Looker Studio later for visualization, but the real power? It’s all SQL. Why This Matters • Marketing teams don’t just need numbers; they need trust in their data. • When you eliminate the black-box tools and own your logic in SQL, you unlock freedom and transparency. Curious to see the SQL behind it? Drop a “SQL” in the comments and I’ll share a simplified version. #SQL #BigQuery #Attribution #MarketingAnalytics #DigitalAnalytics #GA4 #DataEngineering #MarketingOps #LookerStudio

Rethinking the “T” in ETL/ELT: Why Transformation is Not a Single Step: Many organizations approach data pipeline transformations the wrong way, treating “T” in ETL (or ELT) as a single step when, in reality, it consists of three distinct processes that should be managed separately. 1. Standardizing Raw Data for Storage & Querying The first transformation is about converting raw data into a structured, usable format. This means taking API responses from sources like Google Ads, Shopify, or Meta and transforming them into a standardized schema that can be stored and queried efficiently. Each data source has unique structures, but the process for transforming them remains consistent across all clients using that source. TapClicks handles this step automatically, ensuring that marketing teams get clean, structured data out of the box. 2. Organizing Data for Business Use Cases The second transformation is about making data actionable for marketing analysts—without altering the raw data. Since marketing teams have domain expertise, they need control over how data is structured for insights. This includes: • Defining metrics & dimensions across channels (ensuring consistency in how conversions, revenue, or engagement are calculated). • Pacing rules & budget tracking (aligning real-time spend with forecasted budgets). • Cleaning and normalizing naming conventions (standardizing campaign names, creative tags, and more). • Building audience and product segmentations (grouping users or SKUs into meaningful categories for insights and activation). Unlike the first transformation, which is a technical standardization process, this step requires deep business context and varies by organization. TapClicks enables marketing analysts to perform this transformation in an intuitive, flexible way. 3. Connecting Transformed Marketing Data to the AI & Analytics Ecosystem The third transformation is about integrating marketing data with broader business datasets like revenue, CRM, and customer lifetime value. This is where AI-driven insights, predictive analytics, and deeper attribution models come into play. Typically, this transformation happens in a cloud data warehouse (CDW), where centralized analytics teams build cross-functional models. TapClicks facilitates this third step by pushing the marketing teams’ transformed data into the CDW, ensuring that marketing data is seamlessly integrated into enterprise AI pipelines, financial models, and business intelligence systems. Companies that separate these transformation layers can move faster, improve data governance, and ensure real-time decision-making across teams. TapClicks enables a structured yet flexible approach—handling the first transformation automatically, empowering marketing teams to own the second, and seamlessly integrating with CDWs for the third. Would love to hear - how is your team approaching data transformation? 🚀 #MarketingAnalytics #MarTech #ETL #ELT

For years, marketers have been forced to analyze performance in silos—evaluating Facebook in Ads Manager, Google in GA, TV through post-campaign lift reports. Each platform tells a different story, leaving teams to stitch together a fragmented view of performance. The problem? Siloed measurement doesn’t reflect how consumers actually move through the funnel. A purchase isn’t usually the result of a single channel—it’s the product of multiple touchpoints working together. Relying on platform-specific attribution ignores this complexity, leading to misallocated budgets and missed opportunities. This is where unified measurement comes in. By combining methodologies like Multi-Touch Attribution (MTA), Marketing Mix Modeling (MMM), and incrementality testing, marketers can move beyond siloed analysis and see the full picture. A unified approach ensures: -More accurate decision-making—by accounting for both granular, user-level data and broader, market-level trends. -Better budget allocation—understanding the true impact of each channel instead of over-relying on the last-click or individual platform metrics. -More trust in marketing data—giving finance and leadership a clear, consistent framework for investment decisions. The days of optimizing channels in isolation are over. Marketers who embrace unified measurement gain the clarity and confidence needed to drive real business outcomes. How is your team thinking about breaking down silos in measurement?

Many marketing managers default to Google Analytics for tracking performance. But here’s the thing: Google Analytics only covers what happens on the website. It doesn’t tell you what happens after someone signs up, fills out a form, or makes a call from your site. That crucial data is sitting in your CRM or backend system. To really understand which channels, campaigns, and ads are bringing in new customers and driving revenue, you need to link your website traffic data with your CRM data. That’s where Elly Analytics💛 helps. It pulls together data from ad platforms, Google Analytics (or whatever web/app analytics tools you’re using), and your CRM. This gives you the whole picture of what’s happening across your marketing channels — so you can see what’s really working. Elly also helps clean up your data. It can merge duplicate customer profiles, group contacts from the same company into one entity, and add all sorts of interactions to the customer profile. We’re talking app events, calls, chats, emails, texts, push notifications, webinars, discount codes, referrals — you name it. With a complete view of each customer’s journey, you can figure out what’s driving conversions and get a clear read on your marketing performance. This helps you fine-tune your strategies, boost your results, and bring in leads that turn into high-value customers.

Many of you DM'd me about how to get your marketing data in one place. Tools, teams, cost, etc. Here's a breakdown. Last week, I mentioned that most brands often can't get to more sophisticated analysis (measurement, reporting, forecasteing, etc) because their data is messy. Note, this will follow the basic "modern data stack" playbook. I'll also warn you that this is NOT a marketing ops project, it's a data engineering project. I'll talk about resourcing below. The simple goal: Get all of your marketing data in one place. Clean, accurate, and automated. 1. Inventory your data - All paid media platforms - Analytics tools - CRM/CDP systems - Transaction/revenue data - Assign a data owner to each - Make sure your data teams have access 2. Get the data in one place It's difficult to work w/ data if it's all spread out in different platforms, spreadsheets, tools. So, we want to get the data all loaded in a single place, a warehouse. You'll need an ETL tool and a warehouse. Yes, spreadsheets can work initially. But they're manual and will break at scale. Warehouse: Bigquery, Snowflake, AWS/Redshift, etc. All are pretty quick to set up w/ a credit card. ETL: Synchs data from the source to your warehouse on a schedule (daily, hourly, etc) We use a mix of Fivetran, Funnel, and native APIs (cloud functions), but there are plenty of other great tools depending on your need. Portable, Sititch (Talend), Airbyte. 3.) Transform the data So far we have the raw data loaded, but we need to get it in usable shape. For this we need to build a "data pipeline" which, on a schedule, will clean and blend the data into usable datsets. dbt is the major player here, but there are plenty of others...airflow, dataform, sqlmesh, etc. depending on your team's preference. Examples of transform layers: - Making "impressions" consistent across platforms (Meta, Google, TikTok) - Excluding draft/wholesale/employee orders - Building 1P segments - Currency conversions - etc. 4.) The People You'll need some combination of: - Data/Analytics Engineer (ETL/Warehouse/Transformations) - Analytics (QA/Validation) Options: - Internal team (any technical marketers?) - Borrow from IT/Analytics - Partner with vendor (like us, Power) - Hybrid approach In summary, the Basic Process - ETL pulls raw data daily - Warehouse stores historical data (raw and transformed) - Transformation standardizes metrics Price and timeline 100% depends on the number of sources, team skillset, and use cases. But I recommend starting small (single ETL, priority data sources, fewer use cases) Lmk if you have questions or share tips that work. DM me if you have specific questions about your current stack. #moderndatastack #etl #dbt ♻️ Marketers, share this with your data team 🔔 Follow me for more rants on data + marketing

I have seen so many CMOs struggling with a patchwork of channel metrics and platform reports. Every tool speaks its own language, leaving them with an an incomplete picture, missed opportunities and underperforming campaigns. ---------------------------------- How do you fix this? → Start with a unified measurement program. Pull data from all sources: - Search - Social - Offline channels Integrate it into a cohesive view that derives incremental insights. We worked with a national electronics brand and retailer who thought their paid search and TV campaigns were siloed efforts. Until they discovered both worked in tandem to drive store visits. We enabled marketing mix modeling (MMM) and incrementality testing that told them: - How each channel contributed to revenue - Tracked customer lifetime value (CLV) - Unlocked real business growth Lesson? No more isolated KPIs. ---------------------------------- How are you connecting your marketing data today? Let me know in the comments.

A growing e-commerce logistics platform 4x'd their ROAS after fixing one fundamental data problem: Their attribution was broken. 🛑 THE PROBLEM(S): 1. Marketing couldn't see clear touchpoint-to-conversion paths 2. Customer data was fragmented across multiple tools 3. Teams made decisions based on partial or outdated metrics 4. Ad spend effectiveness remained a mystery They thought this was a strategy problem. It wasn't. It was a DATA problem. Their marketing team knew which campaigns they were running, but couldn't tell which ones actually drove conversions. ✅ THE SOLUTION: They built a single pipeline for capturing all user events - website behavior, product usage, marketing interactions - and delivered them directly to their data warehouse. This let them: 1. Stitch together complete user journeys from first click to final action 2. Create accurate customer profiles unified across channels 3. Calculate precise CPA and ROAS metrics in real-time 4. Stop the guesswork on campaign effectiveness 🏆 THE RESULTS: → 4× increase in return on ad spend → Marketing teams shifted budget to truly effective channels → Less time wrangling data, more time optimizing campaigns → Data and marketing FINALLY speaking the same language Most marketing teams aren't failing because of bad creative or targeting. They're failing because they can't accurately measure what's working.