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

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