Data, Insight, Action: Machine Learning & AI for Marketing Analytics

Published by Adobe

It’s easy to see the potential in artificial intelligence (AI) and machine learning (ML) for data analysis. Your team can use these tools to surface deeper insights, process more data in less time, and automate the repetitive manual work of data cleansing and preparation.

Despite the possibilities, organisations are still struggling to successfully adopt AI and ML. A 2018 Gartner report predicted that 85% of AI projects would eventually fail. Five years later, the prediction has proven accurate.

In order to succeed with a data project, it’s important to start with your business goals in mind, and use technology as a means to these ends. It’s easy to get caught up in pure technology and pure data—but the business value has to be the primary driver.

We worked with leading data and analytics experts to create this guide to implementing AI and ML in marketing analytics. With the right program in place, you can:

• Increase efficiency for analytics teams
• Successfully complete the data initiatives you’re accountable for
• Automate lower-value tasks
• Reach the right people, in the right channel at the right time
• Increase customer retention
• Drive more revenue

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---------- Data Governance Omnichannel Data GDPR Compliance Connecting data from multiple sources Activating Customer Segments in Real time to digital channels Currently have no challenges

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---------- Yes, in the next 6-12 months Yes, but not for another 12 months No

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Related Categories Marketing Analytics, Cross Channel, Customer Data, Customer Relationship, Digital Marketing, Collaboration, Technology, Segmentation, Customer retention, Web analytics, Digital Marketing