1、1Data,Insight,Action:Machine Learning&AI for Marketing Analytics.2The path to data maturity.Wave 1-Aspire:demonstrate quick wins.Wave 2-Mature:build a single customer view(SCV).Wave 3-Mature:implement data governance.Wave 4-Mature:ensure data quality.Wave 5-Industrialise:automate and scale.Wave 6-Re

2、alise:maximising data science.Machine learning and AI in action.Use case:increasing share of wallet and lifetime value.Use case:reducing customer churn.Use case:improved individual customer messaging.Use case:re-engage lapsed customers.Use case:customer conversion optimization.Use case:contextual re

3、cognition.How Adobe powers the switch from data-driven to AI-driven.System of data.System of insights.System of engagement.56791011121313131414151515Table of contents.3Its easy to see the potential in artificial intelligence(AI)and machine learning(ML)for data analysis.Your team can use these tools

4、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 fai

5、l.Five years later,the prediction has proven accurate.1In order to succeed with a data project,its important to start with your business goals in mind,and use technology as a means to these ends.Its easy to get caught up in pure technology and pure databut the business value has to be the primary dr

6、iver.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 youre accountable for Automate lower-value tasks Rea