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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 

Here are specific business use cases for BigQuery that highlight how it can empower business analysts to deliver actionable insights and add value to an organization: 1. Customer Segmentation and Personalization Use Case: Analyze customer demographics, purchase behavior, and engagement metrics to segment customers. Example: A retail company uses BigQuery to identify high-value customers based on purchase frequency and spending patterns. These insights enable targeted marketing campaigns, personalized offers, and loyalty programs. 2. Sales Performance Analysis Use Case: Track and optimize sales performance across regions, products, or teams. Example: A global company uses BigQuery to analyze sales data from multiple territories. They identify underperforming regions and adjust strategies to boost sales, such as reallocating marketing budgets or offering localized promotions. 3. Financial Reporting and Forecasting Use Case: Automate financial reports and create predictive models for revenue and expense forecasting. Example: A SaaS company leverages BigQuery to generate monthly recurring revenue (MRR) reports. Using historical data, they predict cash flow trends and allocate resources more effectively. 4. Real-Time Inventory Management Use Case: Monitor and predict inventory levels to avoid stockouts or overstock situations. Example: An e-commerce company streams inventory data into BigQuery and integrates it with sales data. The system provides real-time alerts for low-stock items and recommends restocking based on demand patterns. 5. Website Traffic and User Behavior Analysis Use Case: Understand website performance and visitor behavior to optimize user experience. Example: A media company uses BigQuery to analyze web traffic data from Google Analytics. They track bounce rates, user journeys, and content engagement to improve website layout and content strategy. 6. Marketing Campaign Effectiveness Use Case: Evaluate the ROI of marketing campaigns across multiple channels. Example: A business imports ad spend and performance data from Google Ads, Facebook, and other platforms into BigQuery. They identify the most cost-effective channels and adjust their ad budgets accordingly. 7. Fraud Detection and Risk Management Use Case: Detect anomalies in transaction data to prevent fraud or financial risk. Example: A payment processor uses BigQuery to analyze millions of daily transactions. Advanced queries flag unusual patterns, such as sudden spikes in refunds, enabling timely investigation. 8. Operational Efficiency Analysis Use Case: Optimize business processes to reduce costs and improve efficiency. Example: A manufacturing company analyzes machine performance and maintenance logs using BigQuery. Insights help schedule predictive maintenance, minimizing downtime and improving productivity.

𝗛𝗼𝘄 𝘁𝗼 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗲𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗙𝘂𝗻𝗻𝗲𝗹 𝗶𝗻 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 Creating a sequential funnel works like this: users must go through all the steps in a specific order. For example, if someone lands on your site directly on a product page, they won’t be counted because they need to start at step one. This is the hallmark of a closed funnel. Here’s how to do it: 1. Define Your Funnel Steps: In an eCommerce funnel, typical stages include product view, adding to cart, starting checkout, and completing the purchase. It’s crucial that all these steps are mandatory because a sequential funnel requires users to pass through them in order. A closed funnel counts only those who start from the very first step, like viewing a product. This focus helps you concentrate on users who genuinely go through the entire journey. 2. Extract Data: Start by pulling the necessary data from your Google Analytics 4 (#GA4) events table. You’ll need events related to key user actions to track their movement through the funnel. 3. Create Joins: This is where it gets interesting! Combine the data for each step to count how many users make it to each stage. Make sure your joins only include users who completed previous steps in the right order. For example, step two will count only those who viewed a product and added it to the cart; step three will include only those who added an item to the cart and started checkout, and so on. 4. Don’t forget to check event timestamps: you want to ensure that users performed previous actions before moving on to the next steps. It’s also important that all actions happen on the same day to keep your data accurate. 5. Visualize Your Data: Once everything is set, save the results in a new BigQuery table and create visualizations in Looker Studio. And that’s it—your sequential funnel is ready! If you’re interested in diving deeper into attribution and learning how to build funnels effectively for your business, I’m launching a course soon, and there are just three days left for early bird pricing for only $199! Don’t miss out on this opportunity - drop me a message or put "course" into the comment section! More details here - https://lnkd.in/gz8zsjAe

👉 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

📊 Unlock Scalable Growth with a Marketing Data Warehouse 🚀 In today’s competitive landscape, a centralized marketing data warehouse is your secret weapon for smarter strategies and faster scaling. 💡 From streamlining data management to driving personalization and optimizing campaigns, the benefits are endless. ✨ Here’s what a data warehouse can do for you: - Centralize all your marketing data for instant insights. - Optimize campaigns with real-time, data-driven decisions. - Personalize customer experiences to boost loyalty and engagement. - Align marketing efforts with business goals for maximum ROI. Ready to supercharge your growth? Dive into our latest blog to discover how a marketing data warehouse can transform your business. Read more here 👉 https://lnkd.in/gZkkvvBi #MarketingAnalytics #MarTech #AdTech #DataTools #DataWarehouse #SmartMarketing #AdTools #MarketingTools #MarketingData #AI

All GA4 Updates of 2024: Simplified key event tracking with streamlined recommendations Allowed manual ad content parameters in custom channel groups Added product disapproval recommendations Integrated Customer Match with Analytics audiences Enhanced cost data import Introduced granular Enhanced Ecommerce dimensions. Improved data deletion controls Launched benchmarking Added session-level traffic source fields to BigQuery exports Introduced shared saved segments for consistent team-wide data analysis. Increased Analytics 360 unsampled data quotas Added diagnostics to identify configuration issues in Google Tags. Introduced real-time pages report to monitor active users and popular content. Raised data export limits for more extensive external analysis. Fixed user-provided data over-attribution to “Direct” traffic. Allowed multiple data import sources for GA4 while preserving existing data. Improved traffic attribution accuracy for multi-channel reporting. Introduced "Plot Rows" to visualize multiple rows in reports. Automated GA4 property setup to match Universal Analytics configurations. Simplified Data Studio report creation with direct linking. Increased BigQuery export frequency for faster data analysis. Released new app metrics like session start rate and average duration. Updated audience definitions for lifecycle alignment. Added AI-based key event tracking in custom reports. Improved ecommerce tracking with automatic product categorization. Expanded user-provided data collection flexibility. Enabled property-wide data deletion requests for privacy compliance. Launched real-time BigQuery reporting. Added enhanced user pathing for customer journey tracking. Unified session tracking for better cross-platform attribution. Streamlined UI for easier navigation and reporting. Expanded API support for external data integrations. Released predictive ecommerce metrics like purchase likelihood. Simplified linking Analytics to third-party platforms. Enhanced explorations with customizable visualization tools. Improved multi-step funnel analysis. Introduced anomaly detection in real-time reports. Enabled direct export of unsampled data to spreadsheets. Improved app campaign attribution models. Enhanced event debugging for GA4 admins. Added Display & Video 360 linking recommendations. Unified conversion terminology with Google Ads. Removed user ID requirement for user-provided data collection. Introduced Admin API for property upgrades and integrations. Deprecated Universal Analytics 360 features in EEA. Introduced "Primary Channel Group" for better traffic source categorization. Improved user consent management tools. Added real-time data dashboards for enterprises. Enhanced API for advanced data queries. Enabled deeper Google Cloud integration for data analysis. Added advanced filtering in exploration reports. Updated data retention settings for longer storage. Improved predictive analytics for churn and lifetime value. #GA4

𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 size was valued at $41.05 billion in 2022 and is projected to grow from $51.55 billion in 2023 to $279.31 billion by 2030. The Digital Analytics Software Market refers to tools and platforms that collect, process, and analyze digital data to help businesses optimize their online presence, customer experiences, and marketing strategies. This market is fueled by the increasing reliance on data-driven decision-making across industries to enhance business operations and customer engagement. 𝐂𝐥𝐢𝐜𝐤 𝐇𝐞𝐫𝐞, 𝐓𝐨 𝐆𝐞𝐭 𝐅𝐫𝐞𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐑𝐞𝐩𝐨𝐫𝐭 https://lnkd.in/gMdNibfv Market #Drivers Digital Transformation: The shift towards digital platforms for business operations and marketing increases the need for analytics tools. Increased Online Engagement: Growing internet and mobile penetration is driving traffic to digital platforms, making analytics essential for understanding user behavior. Demand for ROI Optimization: Businesses aim to maximize ROI on marketing and advertising spends, relying on analytics to measure performance. 📈 𝐌𝐚𝐫𝐤𝐞𝐭 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬: Global Digital Analytics Software Market: By #Company Google Adobe Siteimprove IBM Amplitude Looker Pendo Statcounter Funnel Mixpanel GoSquared Global Digital Analytics Software Market: By #Type On-Premises Cloud Base Global Digital Analytics Software Market: By #Application SMEs Large Enterprises 𝐂𝐥𝐢𝐜𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨 𝐆𝐞𝐭 𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐑𝐞𝐩𝐨𝐫𝐭 https://lnkd.in/gJBEDZK4 ✅ 𝐅𝐨𝐥𝐥𝐨𝐰-Stringent Datalytics - Information Technology #DigitalAnalytics #DataDrivenDecisions #CustomerInsights #MarketingOptimization #RealTimeAnalytics #AIinAnalytics

💡 Building sequential funnels in BigQuery provides an essential view of the customer journey for eCommerce businesses. Alex Ignatenko's steps show how BigQuery can be used not only to capture each stage but also to ensure users progress through the sequence in the intended order. I especially appreciate the focus on event timestamps and precise joins—two key elements for reliable insights. Having worked with BigQuery, I know how crucial these sequential funnels are for identifying where users drop off, allowing businesses to target the stages that need attention.

What a week for Customer Data Platforms. With Q4 upon us, several of the CDPs we work most closely with have made significant announcements. There are 4 big things I’d like to highlight among the CDP providers we work closely with: 1) CDP World was the best in person martech conference I’ve attended in some time. With great partners like Treasure Data, MetaRouter, and a host of clients and former clients, this event rocked. Congrats to Treasure Data on this touchstone event.   The conversation focused heavily on AI – with interesting demos of LLM powered marketing automation and insights. 2) Lytics has announced its xCDP positioning. Lytics is bucking the CDP Institute's categorization of CDPs and saying there are two types of CDPs…data CDPs and experience CDPs. With its robust integrations to common CMS providers such as #WordPress and #Drupal, Lytics has lowered the barrier to rapid time to value for a new constituent in the CDP Center of Excellence – front end engineers and web developers. Of course, the data still needs expertise like Actable can provide, but activations on the website are even more seamless.   3) BlueConic. Long a holdout in the composable approach, BlueConic announced its foray into the hybrid game of traditional CDPs with composable capabilities on the #Snowflake platform. 4) Simon Data also recently announced new capabilities to extend the reach of addressable audiences to The Trade Desk. Simon clients have additional optionality for addressing audiences on an additional scale ad platform. Each have said that the CDP category is both real-time and composable depending of course on your use cases – with the ability to support all of them. It will be interesting to see how composable players respond, as they have dependencies on the cloud data warehouse. Yet, for brands with well-organized data, careful governance, and/or stringent data privacy requirements, the composable CDP space remains attractive for ease of implementation and data privacy. With all the innovation in the customer data space, I'm excited for what's ahead for the CDP category.

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