Your Data Infrastructure (or lack thereof) Is Costing You BIG TIME This simple adjustment led to 110% more purchases and a 33% reduction in Cost Per Purchase. What was the change? Was it new creatives? Nope. Was it new copy? Nope. Was it simplifying their offer? Nope. The real game changer? Hiring our team of specialists to optimize the D A T A infrastructure & strategy Your Data Infrastructure is the foundation of ALL your marketing and growth activities & a robust first-party data strategy is crucial for your growth efforts. Here's the issue. . . Marketing and growth teams are stretched thin— managing clients, creating content, testing creatives, developing strategies—the list goes on. And what inevitably happens is that the very foundation of all of their efforts is left unmanaged. The consequences of neglecting your data infrastructure and data strategy include: > Erratic ad performance > Rising acquisition costs > Missed growth opportunities All of which directly impact your bottom line. After the release of iOS 14.5, brands have adopted a variety of strategies to overcome tracking and data challenges, typically falling into three main categories: Bucket 1: Ignore the need for effective first-party data systems & endure rollercoaster ad performance. Bucket 2: Use standard CAPI through platforms like Shopify. Better than nothing, but still limited and will never deliver optimal performance. Bucket 3: Rely on tracking, CAPI or attribution tools just because a well known podcast or thought leader recommends it. Spoiler alert. . .most of these tools are just glorified tag managers. Most of our clients come to us from Bucket #3—using tracking, CAPI, or attribution tools recommended by influencers or podcasts. When they put these popular solutions to the test against us, we've never been outperformed. Upgrade your data strategy today with audienceOS & join our clients who experience: ✅ More consistent ad performance ✅ Lower costs per acquisition ✅ Enhanced ability to scale Bottom line, an optimized data strategy & infrastructure are no longer 'nice-to-haves'—they’re essential for consistent, predictable growth. Don’t let your data infrastructure be your Achilles’ heel. Invest in your data infrastructure today & unlock massive growth and stability for your brand or clients. Comment below or send me a DM & let's see how much more profit we can help you squeeze out of your paid acquisition efforts.
Garrett Gray’s Post
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✨ Seamless Data Migration with Onesecondbefore & ClickHouse! We're excited to share a groundbreaking update to our Composable CDP—now supporting ClickHouse! If you're into large-scale data analytics, you know that speed and efficiency can make all the difference. With Onesecondbefore’s latest enhancement, you can leverage the power of ClickHouse, a real-time analytics database trusted by industry leaders like eBay, Spotify, and Lyft for its blazing-fast querying capabilities. What’s New? Our platform now seamlessly integrates with ClickHouse, enabling automatic, reliable, and secure data migration from popular sources, including marketing applications (like DoubleClick, Google Ads, or Meta), SaaS applications, databases, ERPs, or files. With all your data present in ClickHouse, you can achieve a 360-degree view of all your marketing efforts. Why It Matters Onesecondbefore's Composable CDP leads in data management technology, providing a robust solution for businesses aiming to enhance their data operations. 📊 The adaptability and scalability of Onesecondbefore's CDP meet the data needs of businesses of all sizes, streamlining integration and improving decision-making. Embrace Onesecondbefore's CDP today and turn your data lake into a 360-degree marketing analytics platform! 🌟 To learn more about how Onesecondbefore can enhance your data operations and integrate seamlessly with ClickHouse, visit our website at https://lnkd.in/emxnZ47v #DataMigration #Analytics #ClickHouse #Onesecondbefore #ComposableCDP #RealTimeAnalytics #MarketingAnalytics #DataManagement
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In 2005, I was among the first marketers to buy Facebook ads. 19 years later, the digital marketing industry has evolved significantly, but one fundamental challenge remains: How can marketers get insights that help them run Marketing more effectively? Every few years, there is a new flavor of BI tools - Microstrategy, PowerBI, Tableau, Looker, etc., but the fundamental challenges persist. Marketers remain highly dependent on data teams to answer questions and they lack marketing data context. 🔴STATIC dashboards designed to answer predefined questions 🔴Limited data EXPLORATION - BI tools are designed for analysts, not marketers 🔴DEPENDENCIES on data teams for new dashboards, metrics, dimensions, groups, etc 🔴No access to GRANULAR data for steering (e.g., creative performance) ❓How can marketers explore data and ask follow-up questions to validate hypotheses and confidently steer performance? Here's the truth: seeing WHAT’s happening on high-level dashboards is an observation…NOT an INSIGHT. It’s not enough… (This is why most data-driven marketers build their own spreadsheets) The result? 🔁Endless cycle of dashboard requests 🔁Limited insights 🔁Missed optimization optimization opportunities Marketers sift through BI dashboards, ad platforms and spreadsheets to make “data-driven” decisions. Meanwhile, opportunities slip away and hours are wasted on manual reporting. Data teams have become "ticket takers" and "dashboard factories" With the rapid pace of change and innovation, this operating model will fail. Marketers should be empowered to go FROM QUESTIONS TO INSIGHTS IN MINUTES. PS: Time flies - it’s been nearly 20 years! 🔽What's your biggest challenge in getting marketing insights quickly?
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With just a few clicks and our brand new connector, you can combine your Google Analytics data effortlessly with other key sources like CRM, ERP, social media metrics, email campaign data, & POS data. Like everything Snowflake, it just works. Derive valuable insights for understanding your customer journey and driving personalisation by building your own enterprise grade models and apps for: 1. Customer 360 2. Multi-touch attribution (MTA) 3. Churn prevention 4. Next best action And if you really want to jazz it up check out our Composable CDP/MarTech partners Hightouch, BluSnow, Census, Segment, Braze, and Simon Data! Want to know more about how Snowflake can help your marketing/retail teams? Follow James W. Warner and Leslie Lorenz for insights or contact your friendly ❄ account team and let us do the heavy lifting for you 💪 https://lnkd.in/gJDWqhzu #MarketingDataCloud #martech #adtech #customer360 #snowflake #mlops #genai #llm
Snowflake Connector for Google Analytics
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I’ve known Aj Orbach and Maxx Blank for 15+ years. They’ve been hustling to build startups for many years & so was I which is how we bonded. In 2019 AJ joined Maxx to build Madison Braids - a growing ecommerce business Maxx was working on. The brand was doing very well, but Maxx and AJ found themselves drowning in spreadsheets trying to understand the bottom line of their daily P&L & trying to figure out which ad channels converted best. That’s when they came up with the idea of building a reporting & analytics tool for Ecommerce brands, hence Triple Whale was born in 2021. Shopify entrepreneurs on the Twitter-verse couldn’t get enough of it. Almost 4 years later… and 25,000+ brands rely on Triple Whale daily for their reporting and attribution. One day this summer I got a call from AJ, “Triple Whale is crushing it in the Ecomm space, but my internal teams want to use our own data warehouse, attribution & reporting tools. We need to build our own product for SaaS now.” That’s when I joined the Triple Whale bandwagon… What Triple Whale solved beautifully for Ecom businesses applies for any growth marketer that invests in paid channels. The caveat is that SaaS marketer’s challenge is compounded by 300%. Much of the data is in CRM stages, a very long sales cycle, endless ad channels and a marketing budget that ~50% of it gets spent offline (e.g. conferences, mailings) But what I saw the team built got me excited: a full dataware house + BI reporting connected to all ad spend channels & CRM connectors with AI baked in natively. Like AJ told me: “we built a reverse ETL and parked an LLM on top of it”. Since I’ve joined, internal teams at TW are dogfooding our own SaaS product in every way imaginable… from Marketing, Sales, CS and even Product. They can’t get enough of it. Here are the tools we parted with as a result: 1️⃣ Fivetran -> ETL 2️⃣ Big Query -> data warehouse 3️⃣ Looker -> reports 4️⃣ Databox -> marketing funnel reports 5️⃣ Posthog -> product analytics Excited to launch this new tool to growth marketers & make their marketing team REALLY efficient. DM if you’d like to take a look :)
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🔑 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐂𝐮𝐬𝐭𝐨𝐦 𝐋𝐓𝐕 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 𝐰𝐢𝐭𝐡 𝐆𝐀𝟒 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐆𝐫𝐨𝐰𝐭𝐡 Customer Lifetime Value (LTV) is vital for strategic planning, revealing a customer's total revenue potential. Predefined metrics may miss business nuances, but with GA4 data in BigQuery, you can build custom LTV models to target and retain your most valuable customers. 🤔 Why Build Custom LTV Models? Generic LTV calculations often miss the mark because they don’t: ✔️ Account for seasonal purchasing behavior. ✔️ Include channel-specific attribution for acquisition costs. ✔️ Reflect variations in customer segments or lifetime engagement trends. Custom LTV models allow businesses to: ✔️ Identify their most valuable customer segments. ✔️ Optimize marketing spend by focusing on channels driving high-LTV customers. ✔️ Predict future revenue streams for better strategic planning. ⚙️ How to Build a Custom LTV Model in BigQuery Using GA4 Data 1. Gather Key Data from GA4 in BigQuery: Extract essential metrics such as revenue, user acquisition data, and engagement events. 2. Add a Time Dimension to Measure Recency and Frequency: Incorporate the time users have been active, to analyze purchasing patterns. 3. Combine with Acquisition Costs for Net Value: Integrate data from your ad platforms (Google Ads, Facebook Ads, etc.) to calculate net customer value after deducting acquisition costs. 4. Train an LTV Prediction Model Using BigQuery ML: With historical data, use BigQuery ML to predict future LTV. A regression model can estimate the total lifetime revenue for each user. 5. Predict Future LTV for Strategic Planning: Run predictions to forecast LTV for each customer. 📊 Applications of Custom LTV Models: 1. Target High-LTV Customers: Invest in retaining customers with high predicted LTV through loyalty programs or personalized offers. 2. Channel-Specific Optimization: Allocate marketing budgets to channels that drive high-LTV acquisitions. 3. Revenue Forecasting: Use LTV predictions to anticipate future cash flows and inform financial planning. 4. Segmented Strategies: Develop tailored engagement strategies for high-, medium-, and low-LTV customers. 🚀 Real-World Impact Example: For a subscription-based business, implementing a custom LTV model revealed that customers acquired via organic search had 30% higher LTV than those acquired via paid social ads. This insight led to reallocating the budget toward SEO initiatives, increasing high-value acquisitions, and reducing overall customer acquisition costs. 💬 What’s your experience with LTV modeling? Have you tried building custom models using GA4 data? Let’s discuss how advanced analytics can drive smarter growth strategies! #CustomerLTV #BigQuery #GA4 #DataAnalytics #MarketingStrategy #SQLForAnalytics #BigQueryML #CustomerRetention #RevenueOptimization #GrowthHacking
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Meet Michał – a senior growth manager who specializes in data analytics & reporting. Recently, Michał Wilgosz was tasked with developing a dashboard to help a client answer the following business questions: 1️ ↗ Which campaigns are delivering sales qualified leads (SQLs) and signed deals? 2 ↗ How much revenue do they generate and what’s their return on investment (ROI)? What seemed like a straightforward task quickly became challenging, even for experienced analytics experts. Several of them tried and failed – here's what went wrong: – The Looker expert ended up creating a very appealing dashboard, but unfortunately, the marketing and sales data didn’t match. – Two Hubspot experts also failed, mainly because the base reports varied significantly and they couldn’t answer the questions by limiting themselves to Hubspot's functionality. === What we did maybe wasn't beautiful or technically sophisticated, but it was fast and worked! + Michał combined data from all the relevant sources in a Google Sheet, created pivot tables to answer the business questions and automated it to get new data daily with no extra work. ↗ Data from ad accounts (costs and clicks) are now collected automatically from Google Ads, Bing Ads, Meta Ads, and mobile app ads, using Supermetrics. ↗ We matched data from HubSpot (SQLs and deals) with corresponding data from ad accounts using quite complex formulas. ↗ The final report consolidated data on costs and clicks, calculated revenue and ROI, and did so for each channel and campaign. Exactly what the client needed. I often see that experts who know just a single tool can't solve the business problem the client is looking to solve because their specialization is too narrow. What usually helps is a broader perspective and thinking as an integrator of various tools.
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👉 Do you want to understand more about Optimizely Data Platform (ODP) - what it is and what you can use it for? The benefits and how it differs from Google Analytics? Then you should read our ODP expert ✨ Adrian Naderi's blog post. Enjoy! https://bit.ly/3UvlFSW #optimizely #customerdataplatform #CDP #personalization #epinova
Optimizely Data Platform
epinova.se
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Why Should Integrate Analytics with Google Tag Manager? There are several advantages to integrating Google Analytics with Google Tag Manager (GTM): Easier Management of Tracking Codes: GTM eliminates the need to manually add and update tracking codes (snippets of JavaScript) for Google Analytics and other analytics tools directly on your website. This makes it simpler to maintain your site and reduces the risk of errors. Centralized Control: From a single GTM interface, you can manage all your website's tracking tags, including those for Google Analytics, conversion tracking, and remarketing. This provides a centralized location for monitoring and updating your tags. Improved Efficiency: GTM lets you set up rules to determine when and where specific tags fire on your website. This allows for more granular control over data collection and helps ensure you're gathering the most relevant information. Faster Website Load Times: GTM can potentially improve website loading speeds by asynchronously loading tracking tags. This means the tags load in the background without slowing down the initial page load for visitors. Flexibility and Experimentation: GTM enables you to easily experiment with different tracking setups and test changes before publishing them to your live site. This allows for more data-driven decision-making. Integration with Other Tools: GTM works with a wide range of marketing and analytics tools beyond Google Analytics. This makes it a versatile solution for managing all your website's tracking needs. In summary, integrating Google Analytics with Google Tag Manager streamlines tag management improves efficiency, and offers greater control over your website's analytics data.
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Common situation: a CMO reaches out, ready to modernize their measurement and evolve beyond attribution. Budget approved. Leadership aligned. Then we look at their data 🤢 Most brands aren't ready for modern measurement, even if they think they are. Data cleanup alone can delay launch by months. Here's what we commonly find: - Historical data scattered or missing - No centralized data warehouse - Random campaign naming conventions - Basic platform access issues - Geographic data gaps Want to be ready for modern measurement in 2025? Here's your roadmap for getting your data house in order: 1. Data Consolidation You'll need 2+ years of data for proper Media Mix Modeling. - All of your campaigns across paid and organic - Spend (or impressions) This is often challenging for large/enterprise brands...multiple agencies, dozens of social/paid media accounts, online and offline channels, etc. Quick Tips: - Daily data preferred, weekly minimum - Spreadsheet ok, data warehouse preferred - ETL tools are your friends to automate API pulls 2. Campaign Classification This is the silent killer of measurement projects. The better we understand the campaigns that were run, the better the model output will be. You should tag (and name) your campaigns to cluster by: - Marketing channel (affiliate, paid search, etc) - Platform (Meta, Google, TikTok, etc.) - Funnel stage (TOF/MOF/BOF) - Audience - Product category - Promotional periods Campaign Examples: Bad: "Q1_2024_v2" Good: "GOOGADS_NB_US_TOF_HOLIDAY_ELECTRONICS_VIDEO_15S_BROAD" Ps, don't feel bad. 80% of our clients need significant data cleaning. Save us both time and start your naming convention cleanup today. Future you will be grateful. 3. Access Management The number of times we've heard "No one actually has those login credentials..." is staggering. - Assign clear data ownership - Document all platforms and accounts - Centralize credential management Be honest, is your data house in order? Your model will only be as good as it's source data. Garbage in garbage out. Let me know if you have questions (we've seen it all, you won't scare me). #measurement #mmm #marketinganalytics ♻️ Share this with marketing folks 🔔 Follow me for more rants on data + marketing
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🚀 Growth Story Time: Analytics Transformation for Smarter Decisions! Let’s talk about how we helped a platform struggling with fragmented data make confident, data-driven decisions. When we came in, their analytics setup was outdated, and key user behaviours weren’t being tracked effectively. This was limiting their ability to act on insights. Here’s how we turned things around: 1️⃣ Audit: We performed a comprehensive audit of their analytics stack (Google Analytics and GTM). We pinpointed missing events and discrepancies in data between platforms. 2️⃣ Event Schema Creation: We designed a detailed schema to capture Acquisition, Activation, and Retention events, ensuring no touchpoints were missed. 3️⃣ Implementation & Training: Working with their developers, we deployed a robust tracking setup and conducted workshops with their marketing team to ensure they could fully utilise the new dashboards. 4️⃣ Advanced Reporting: We built tailored dashboards in Google Data Studio, providing real-time visibility into campaign performance and user flows. The results? 📈 Discrepancies between platforms were reduced by over 25%. 📊 Full visibility into the user registration flow for the first time. 💡 The team now makes confident, data-backed marketing decisions. Testimonial: "Giovanni and the team provided great growth marketing training and analytics expertise. They’re highly skilled individuals I’d recommend to anyone." – Kena Amoah #Analytics #GrowthMarketing #DataDrivenDecisions
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