SAS Predictive Analytics for Marketing Checklist cover image

TDWI Checklist Report | Marketing Analytics Meets Artificial Intelligence: Six Strategies for Success

October 10, 2016

Marketing analytics is about leveraging insight from data analysis to make marketing more efficient and effective. It involves analyzing, measuring, and optimizing marketing efforts so that marketing dollars are not wasted and adjustments to campaigns can be made more quickly. In order to accomplish this, marketing departments are using advanced analytics that focus on customer behavior, customer intelligence, and marketing optimization. In fact, we see in TDWI research that marketing is often one of the first departments in an organization to utilize advanced analytics such as next best action recommendations or churn analytics.

Vendors are helping them along by providing tools and solutions that often include sophisticated analytics under the hood. Some of these newer tools and technologies use components of artificial intelligence (AI). AI has been around for decades, but has seen a recent resurgence in interest as data size and diversity continue to grow and the cloud becomes a popular option for quickly and economically scaling compute power and data storage. AI and its subcomponents (machine learning, cognitive computing, and even deep learning) are being woven into the analytics arsenal of various departments at organizations across industries.

This Checklist explores how AI can be used to enhance marketing analytics and to help companies both better understand their customers and deliver a great customer experience. It also provides practical advice on how organizations can use what they may already be doing to become more effective in marketing.

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