ML-driven Multi-touch Attribution: Delivering next-level marketing insights

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Presented by

Dilyan Kovachev, Manager, Machine Learning Engineering at Treasure Data

About this talk

In an era where consumers are bombarded with many different messages, offers, and content, how do you know which touchpoints worked to close a sale or build enduring customer loyalty? There are many different “ multi-touch attribution models ” that provide insights into what works. But which models are best for your business, and when and how should you apply them? These questions have only become harder to answer with the growing complexity of consumer journeys across many digital and physical touchpoints, from brand websites, online ads and social media to brick-and-mortar stores. To tackle this challenge, Treasure Data has developed a multi-touch attribution (MTA) model that combines a Long Short-Term Memory (LSTM) deep learning ML-model with Shapley values, a concept developed by Nobel Prize-winning economist Lloyd Shapley. With multi-touch attribution models, marketers are able to: · Use real-world customer behavior data and ML to calculate and update attribution values · Unveil correlations between touchpoints during the customer journey · Provides granular channel attribution % at each step of the customer journey to conversion · Allows for a data-driven budget allocation across channels and funnel stages

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Treasure Data Customer Data Cloud helps enterprises use all of their customer data to improve campaign performance, achieve operational efficiency, and drive business value with connected customer experiences. Our suite of customer data platform solutions integrates customer data, connects identities in unified customer profiles, applies privacy, and makes insights and predictions available for Marketing, Service, Sales, and Operations to drive personalized engagement and improve customer acquisition, sales, and retention. To learn more, visit www.treasuredata.com.