Myntelligence Optimizes Digital Marketing Campaigns and Unlocks Real-Time Business Insights with TigerGraph

Myntelligence Optimizes Digital Marketing Campaigns and Unlocks Real-Time Business Insights with TigerGraph

TigerGraph, the only scalable graph database for the enterprise, announced that Myntelligence has chosen TigerGraph to power advanced analytics within the Myntelligence Media Automation platform to deliver integrated, real-time insights to marketers. Myntelligence, with TigerGraph, is addressing a key business challenge: Marketers often struggle to manage multiple campaigns across various digital channels while analyzing integrated results in real-time – especially when working with data repositories such as Hadoop-based data lakes.

“Today’s businesses must harness the power of interconnected data to gain real-time, meaningful insights – and this data is especially critical for marketers,” said Dr. Yu Xu, founder and CEO, TigerGraph. “In fact, almost 80 percent of today’s marketers describe their customer engagement as ‘data-driven,’ with this data being essential to understanding a customer’s engagement journey before, during, and after a purchase. TigerGraph’s advanced graph analytics bolsters the Myntelligence platform, arming marketers with the real-time campaign information they need to strengthen customer relationships, resulting in higher ROI.”

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Myntelligence’s Media Automation and Optimization SaaS platform enables marketers to utilize all ad-tech and mar-tech platforms available in the marketing ecosystem from a single web interface and run campaigns across the entire acquisition funnel. It gives marketers a single customer view across multiple digital channels, facilitating the real-time analysis and optimization of campaign results. TigerGraph serves as the advanced graph analytics foundation for the Media Automation and Optimization system and enables the interactive, fine-grained analysis of campaign performance across multiple ad and marketing touchpoints. Marketers can then aggregate events linking to individual consumers, run flexible audience analytics queries, and drill down into target demographics. Brands can use the resulting contextualized insights to design additional customer journeys through AI-powered campaign mapping, improving campaign efficiency and increasing ROI in the process.

“Myntelligence’s prime objective is to help companies build digital communications strategies and omnichannel campaigns in-house. We realized we needed to add advanced graph analytics capabilities to our platform to bridge the ‘business value’ gap,” said Carlo De Matteo, co-founder, and managing partner, Myntelligence. “We were looking for a high performing graph analytics platform that would be easy to build our solution on top of. We evaluated all the players, including open-sourced solutions, and TigerGraph emerged as the best fit. TigerGraph offers the performance, scalability, and ease-of-use we needed and allows us to connect and transform the data in our Hadoop-based data lake so that we can deliver contextualized insights to our customers.”

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Data lakes are built for storing large volumes of raw data in its native format. However, these storage repositories are not designed for rapid data analysis, leaving companies stranded when it comes to deriving real-time actionable insights from marketing campaigns. Companies need to do more than connect multiple digital channels – they need to unlock the business value within these data lakes. Only then can marketers optimize their ad-tech and mar-tech platforms to drive customer engagement.

“Many organizations have invested heavily in building data lakes. Meanwhile, they’ve learned that extracting insights from this ‘data overload’ is a slow, difficult process,” said Tony Baer, principal, dbInsight. “Graph databases can actually complement data warehouses, picking up where data warehouses leave off. Graph databases highlight specific relationships and connections within the interconnected data, shedding light on how various factors drive the customer journey. These newly discovered connections within massive datasets provide businesses with valuable competitive information.”

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