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🛒 Predictive Supermarket Magic: Forecasting New Product Adoption 📈 --------------------------------------------------------------------------------- Check out the project here: 🚀https://lnkd.in/gmvj2Y9j 📊 Business Problem Description : This project addresses the challenge of predicting whether 90% of supermarket customers will purchase a new product based on data from the initial 10% who have already interacted with it. 🛒📊 Accurate predictions help the supermarket optimize inventory, marketing, and supply chain management, reducing waste and boosting sales. 📈 By leveraging early adopter behavior, businesses can make informed decisions to enhance customer satisfaction and stay competitive. 🏆 Code Explanation : The code preprocesses customer data, including cleaning and feature engineering, to prepare it for analysis. 🧹📊 Logistic Regression, a machine learning algorithm, is employed to build the predictive model. 🤖📈 This model accurately predicts the purchasing behavior of the remaining 90% of customers. 🛍️ Visualization tools help interpret the results and identify key factors influencing customer decisions. 📊🔍

Is your data costing you millions? 🚨   Incorrect addresses, inaccurate phone numbers, and impossible purchase dates – the list of faulty data is long. These issues not only cause operational problems but can also lead to significant costs. In fact, Gartner estimates that poor data quality costs companies an average of $15 million annually (Gartner's Data Quality Market Study).   But why is data quality so important?   High quality data is essential for effective demand forecasting and efficient supply chain management. Experian Marketing Services found that 73% of companies in Germany believe inaccurate data prevents them from delivering an outstanding customer experience. Precise demand forecasting relies on high quality data to form the basis for accurate analyses, reliable predictions, and optimized supply chain operations. Without it, companies risk inefficiencies, increased costs, and missed opportunities. Ensure your data is of the highest quality to excel in demand forecasting and supply chain optimization. Discover how pacemaker.ai's Data Thinking Workshop can be the start of your journey to transform your data management strategy and enhance your forecasting accuracy. 🎯   Read more in our blog: https://lnkd.in/eJsUedRE Learn more about Data Quality: https://lnkd.in/e9uHzgyR   #DataQuality #AI #pacemakerai #SaaS #DemandForecasting #SupplyChain

🚀 Unlock the Potential of AI Data Integration Solutions in B2B Data Exchange! 🌐✨ Dive into our latest insights on optimizing data integration for business growth. Don't miss out! #AI #DataIntegration #B2B #Tech #BusinessGrowth 📚 Read more on our blog: https://hubs.ly/Q02BS3dt0 #ArtificialIntelligence #DataExchange #Innovation

Measure what matters.....because if you don't measure it, you can't improve it. It's not a new concept - but it's just not done in the supply chain. Most organizations don't have data to measure even the most basic supply chain metrics like OTIF and lead times. Measurement is built on data, and supply chain data sucks. It's all over the place, and quite honestly, isn't timely or accurate enough to really drive results. Years of managing supply chains with spreadsheets, emails, portals and phone calls has led to a massive data gap in supply chains, which leaves companies in the difficult position of not even being able to mesure what matters. It becomes death by spreadsheet trying to piece incomplete, disconnected information together into something usuable. So, what do you do? 👉 Connect all of your data sources to a single platform of truth, taking out manual data entry into spreadsheet reports. 👉 Automate supplier data collection. Without shipment dates, completion dates and other key information, you're missing a huge chunk of data that is required to drive OTIF and lead time metrics. 👉 Be patient and build out accurate and timely data and please, do not try to use AI until you have this accureate and timely data. It's amazing what you can learn and improve with a couple of simple reports that have been very difficult (if not impossible) to pull together in the past. SourceHub changes the game by 1) centralizing all of your supply chain data and 2) providing actionable measurements that drive your supply chain forward.

The retail industry has evolved rapidly, with changing consumer expectations and global regulations, making it more challenging than ever to stay ahead. Add AI to the mix, and it's a whole new ball game. That's where Informatica comes in. Our Chief Strategist for Retail & CPG, Scott Jennings, has outlined the top 10 data & AI use cases for retailers. Check out the blog below to learn how Informatica can help you solve your business challenges. #WhereAIandDataComesToLife #AIandDataInRetail

#8 🔥 Mastering Data Collection for Real-Time Business Success 🔥 🚀 Data Collection: The foundation of any successful AI project, involving the systematic gathering of relevant data from various sources to inform and drive business decisions. 🚀 Steps and Real-Time Business Application: Customer Insights in Retail Identify Data Sources: In a retail business, data can come from sales transactions, customer loyalty programs, social media interactions, and online reviews. By leveraging internal databases, external APIs, and third-party providers, retailers can gather comprehensive data. 🚀 Define Data Requirements: For improving customer insights, required data might include purchase history, customer demographics, website behavior, and feedback ratings. Clearly specifying what data is needed based on business objectives ensures relevant and actionable insights. 🚀 Data Acquisition: Implement systems to automatically collect data from point-of-sale systems, customer relationship management (CRM) software, and social media platforms. Continuous data collection processes and tools are essential for maintaining up-to-date information. 🚀 Ensure Data Quality: Regularly check for and correct errors in customer profiles, transaction records, and feedback entries to ensure data reliability. Validating the accuracy, completeness, and consistency of the collected data is crucial for making informed decisions. 🚀 Data Integration: Merge online and offline sales data with customer interaction data to get a comprehensive view of customer behavior. Combining data from multiple sources creates a unified dataset that provides deeper insights. 🚀 Privacy and Compliance: Adhere to GDPR, CCPA, and other regulations, ensuring customer data is collected and stored securely, with proper consent. Ensuring data collection complies with relevant privacy laws and regulations builds customer trust and protects the business from legal issues. 🎯 Real-Time Business Impact: By mastering data collection, businesses can: 🚀 Enhance Customer Insights: Gain a deeper understanding of customer preferences and behaviors, enabling personalized marketing strategies. 🚀 Improve Decision-Making: Use accurate and comprehensive data to make informed business decisions, from inventory management to targeted promotions. 🚀 Drive Innovation: Leverage collected data to identify new business opportunities and areas for growth. 🚀 Increase Efficiency: Streamline operations by automating data collection and ensuring data accuracy, reducing manual errors and saving time. #DataCollection #AI #MachineLearning #BusinessIntelligence #DataScience #AIinBusiness #DataAnalytics #TechInnovation #CustomerInsights #RetailSuccess #DataDriven #TechStrategy

🚀 𝟓 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐑𝐞𝐭𝐚𝐢𝐥 🚀 In today’s fast-paced retail environment, a solid data architecture can be the difference between leading the market and lagging behind. Drawing from my past experiences, here are 5 key strategies that I think every retail enterprise should consider: 1️⃣ 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐬: 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Break down data silos and store all your data in one accessible place. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: A centralized data lake enables better data analytics and real-time insights. 2️⃣ 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Retail decisions need to be made in real-time. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Implementing real-time data processing tools can significantly boost operational efficiency and customer satisfaction. 3️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Protecting customer data is paramount. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Adopt robust security protocols and ensure compliance with regulations like GDPR and CCPA. 4️⃣ 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: As your business grows, so does your data. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Ensure your data architecture can scale efficiently to handle increased data loads without performance drops. 5️⃣ 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: 💡 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Predictive analytics can revolutionize inventory management and customer personalization. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: AI and ML can turn raw data into actionable insights, driving better decision-making. 👉 Which of these strategies do you think is the most critical for retail success? #DataArchitecture #RetailSuccess #EnterpriseArchitecture #ITStrategy #TechTrends #GoYallo

Then vs. Now: Tackling Product Data Challenges 🚀 The Nightmare Project: Eight years ago, I inherited a digital selling platform project that was a nightmare. Product data was a mess; setting up a PIM system took a herculean effort, and we struggled with ERP integration, resource constraints, and unit-of-measure issues. ❌ Dirty Data  ❌ No ERP Integration  ❌ Resource Constraints  ❌ Unit of Measure Issues  ❌ Data Accuracy & Image Quality 🚀 Contrast this with my team's current approach, and the transformation is striking. We deploy Azure Cognitive and other AI Services to mine data through document cracking and other unstructured data approaches. This allows us to seamlessly extract and normalize product data from various sources. ✅ Automated Data Mining & Taxonomy Building  ✅ AI-Driven Recommendations  ✅ Seamless ERP Integration  ✅ Enhanced Data Accuracy & Consistency 🤙 What strategies have you used to tackle product data challenges? Share your thoughts below! 👇 James Dorn, John Gunderson, Jason Hein #b2b #ai #distribution #productData #digitalSelling #data #dataEngineering

Navigating the maze of data complexity can be daunting, especially in sectors like B2B distribution and manufacturing. But don't worry, you're not alone. The proliferation of data across multiple channels presents a unique opportunity to streamline operations and enhance decision-making. To turn data complexity into strategic advantage, consider implementing these three steps: 📊 Centralize Your Data: Implement data warehousing solutions to consolidate information. 📊 Enhance Data Quality: Regularly clean and validate your data for accuracy and reliability. 📊 Leverage Advanced Analytics: Utilize AI and machine learning to unearth insights and drive informed decisions. Have you found effective strategies or tools to manage data complexity? Share your insights in the comments below. John Gunderson James Dorn J Schneider. Joe Caruso - thanks for the graphics. #DataIsGold #DigitalTransformation #DataComplexity #TechSolutions #DornGroup #B2B #IndustrialDistributors #Consulting

🚀 Revolutionizing Data Analytics: 5 Key Trends Shaping 2024 As an experienced data analytics expert, I've witnessed the evolution of analytics tools over two decades. Here are 5 game-changing trends in how companies are deploying analytics in 2024: 1. Democratization of AI-Powered Analytics Companies are rapidly adopting augmented analytics platforms that leverage AI and machine learning to make advanced insights accessible to non-technical users. For instance, Dynamic Yield uses machine learning to personalize customer experiences, driving stronger relationships and higher ROI. This shift enables true self-service analytics across organizations, empowering employees at all levels to make data-driven decisions. 2. Cloud-Native Analytics Architectures Both SMEs and large enterprises are shifting to cloud-based, modular analytics stacks that offer greater flexibility and scalability. Walmart’s AI-optimized supply chain is a prime example, utilizing cloud-native architectures to enhance inventory management and delivery optimization. This approach allows companies to quickly deploy new capabilities and integrate best-of-breed tools, ensuring they remain agile and competitive. 3. Embedded Analytics for Operational Intelligence There's a growing trend of embedding analytics directly into business applications and workflows. For example, Delta Airlines uses dynamic pricing strategies powered by embedded AI analytics to optimize ticket prices in real-time. This integration puts insights in context, enabling more data-driven decision-making at the point of action and improving operational efficiency. 4. DataOps and MLOps for Analytics at Scale Organizations are implementing DataOps and MLOps practices to streamline analytics pipelines, improve data quality, and operationalize machine learning models more efficiently. Salesforce’s use of AI for sales projection analysis exemplifies this trend, enhancing the accuracy and reliability of sales forecasts. These practices ensure that analytics processes are robust, scalable, and continuously improving. 5. Augmented Data Governance With the proliferation of data sources and users, companies are leveraging AI-assisted data cataloging, lineage tracking, and access controls to maintain data integrity and compliance. PayPal’s security enhancements through AI analytics highlight the importance of robust data governance in protecting sensitive information and ensuring compliance with regulatory standards. This approach enables broader data access while safeguarding data quality and security. These trends are ushering in a new era of accessible, integrated, and automated analytics that deliver value enterprise-wide. How is your organization leveraging these trends? Let's discuss! #DataAnalytics #AITrends #BusinessIntelligence #B2B #PPSS #afcfta #au #data #analytics #SMEs #afreximbank