Zingg.AI

?? Customer Identity Resolution—Beyond Rules, Powered by ML Matching customer records across systems isn’t easy—typos, missing data, and format variations make rule-based approaches unreliable. This Databricks Solution Accelerator shows how Zingg.AI, an open-source ML-based tool, simplifies entity resolution at scale. ? ML-driven matching, no manual rules ? Scales with Apache Spark ? Powers a real Customer 360 Check out how Zingg.AI + Databricks solve this challenge: https://lnkd.in/dvXmE4WJ #EntityResolution #Customer360 #Databricks #MachineLearning

?? Unlock Hidden Customer Data in Travel & Hospitality Customer data is scattered across bookings, loyalty programs, ancillaries, and credit cards, making personalization and engagement a challenge. A unified customer view turns disconnected data into actionable insights, but achieving this at scale isn’t easy. By resolving duplicate and fragmented customer profiles, brands can: ?? Boost Direct Bookings – Identify OTA customers and target them with personalised offers ?? Increase Loyalty & Credit Card Sign-Ups – Find high-value travelers who aren’t yet enrolled. ?? Target Smartly – Move beyond batch campaigns and offer distressed inventory to the right audience. ?? Enhance Personalisation – Understand full travel behavior and tailor upgrades, ancillaries, and communications accordingly. ?? AI-powered identity resolution helps travel & hospitality brands connect the dots across systems, ensuring every interaction is personalised and meaningful. Zingg.AI makes this possible. Is your brand making the most of its customer data? Let’s discuss. ?? #IdentityResolution #Customer360 #SingleViewOfCustomer

?? Zingg AI Hits 1,000 Stars on GitHub! ???? We’ve just reached a major milestone—1,000 stars on GitHub! ?? A huge THANK YOU to our incredible community of users, coxntributors, and supporters who made this possible. From day one, our mission with Zingg AI has been to make entity resolution smarter, scalable, and AI-driven—helping businesses clean and unify their data effortlessly. Seeing the community grow and embrace Zingg motivates us to push even further! ?? What’s next? More features, better integrations, and an even stronger commitment to making AI-powered identity resolution accessible for everyone. ?? Haven’t checked out Zingg yet? Join us on GitHub ?? https://lnkd.in/dPjk5XTF Drop a ? if you love open-source AI! ?? #EntityResolution #OpenSource #Customer360?

Identity Resolution in Databricks with Zingg: A Step-by-Step Guide Matching and linking records across datasets is a challenge, but it doesn’t have to be. Zingg AI, an open-source entity resolution tool, leverages Machine Learning to automate this process—no manual rules, no hassle. Paired with Databricks, it scales seamlessly to millions of records. Our step-by-step guide walks you through setting up Zingg in Databricks to clean, match, and resolve your data with ease. Check it out here:?https://lnkd.in/dauMkRcn #EntityResolution #Databricks #Customer360 #MachineLearning #OpenSource #DataEngineering

The Hidden Challenge in Sports Data: Who Really Are Your Fans? ???? In sports, fans are more than just customers. They paint their faces, wear jerseys, and travel miles to support their teams. But from a data perspective, they’re scattered across ticketing, e-commerce, email lists, and digital platforms—often under multiple identities. For leagues like the Canadian Football League (CFL), this creates a fundamental challenge: ? How do you build a Single Customer View when one fan has different emails across platforms? ? How do you personalize engagement when data is siloed across nine teams? ? How do you trust your data when a single fan can appear as multiple fragmented profiles? This is the problem CFL tackled with their Customer 360 initiative—not just collecting data but stitching it together to make it actionable. One surprising insight? 10-15% of fan profiles were actually the same person—but went unresolved due to inconsistencies like different emails or name variations. The challenge isn’t just about having data. It’s about making sense of it. For any organisation looking to understand its audience—whether in sports, retail, or beyond—the question is the same: Watch how CFL tackled this challenge: ?? https://lnkd.in/dUj25jYG #Customer360 #IdentityResolution #SingleSourceOfTruth

See how Orthodox Union uncovered hidden relationships using Entity Resolution.

1,000 stars are within reach—just 13 to go! ? Let’s make it happen!

Two people. Same name. Same address. Different IDs. Are they the same person? A family? A duplicate? Or just lost in the data? For a nonprofit like Orthodox Union—which supports Jewish communities through youth programs, advocacy, religious study, and kosher certification—understanding relationships is key. But with data spread across 40+ websites, 5 mobile apps, and multiple CRMs, donors, households, and connections were getting lost.? Centralisation didn’t work. Manual matching wasn’t scalable. By rethinking the problem, entity resolution helped uncover hidden relationships—bringing clarity to data and transforming how OU engages with its community. What once took days now happens in minutes.? Data isn’t just rows and columns—it’s people, families, and impact.? ?? Watch how it’s done: https://lnkd.in/dVX36few #EntityResolution #Customer360 #ComposableCDP #SingleSourceOfTruth

Zingg.AI Office hours at 10 AM PST today. Join us to talk entity resolution, identity resolution using Databricks/Snowflake/Spark/BigQuery/RedShift..you name it. See you soon! https://lnkd.in/gsCHEPK8

Struggling with identity resolution and messy data? Join us today for the Zingg Community Meetup - Office Hours. Where we discuss all things Zingg, answer your questions, and connect with fellow users.