{"id":755,"date":"2022-01-13T17:41:37","date_gmt":"2022-01-13T17:41:37","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/?page_id=755"},"modified":"2022-01-13T17:47:08","modified_gmt":"2022-01-13T17:47:08","slug":"heather-johnston","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/speakers\/heather-johnston\/","title":{"rendered":"Heather Johnston"},"content":{"rendered":"\n
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\n Heather Johnston <\/h3>\n <\/div>\n \n <\/div>\n <\/div>\n
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Heather Johnston<\/h1>\n

Manager, Data Science and AI at IBM Marketing<\/h5>\n

Heather Johnston leads the Data Science & AI team for IBM Marketing. Her team builds scaled production-level AI capabilities to bring IBM’s marketing demand generation platform to life to listen and respond to client needs intelligently and automatically.<\/p>\n

Heather\u2019s nearly fifteen years of experience in data science includes six years in a manager role and three years in digital marketing, where she worked on A\/B testing and web personalization. Her past work includes Customer Lifetime Value, Cross Sell Recommendations, Lead Touch Optimization, Marketing Effectiveness, and Client Experience Analytics.<\/p>\n

Before joining IBM, Heather was a mathematics professor at Rutgers University, University of Massachusetts, and Vassar College. She holds a PhD in Mathematics from the University of Chicago, an MBA from Rensselaer Polytechnic Institute, and a BS in Mathematics from Massachusetts Institute of Technology.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Professional<\/p> <\/div>\n<\/div>\n\n

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Track: Machine Learning Applications in Marketing<\/h6> <\/div>\n<\/div>\n\n
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Machine Learning in Business-to-business Marketing<\/h2>\n

We will provide an overview of machine learning applications to B2B marketing, detailing business problems that can benefit from data science solutions, providing sample machine learning methodologies, and quantifying the benefits of integrating machine learning into marketing automation and performance measurement systems.<\/p>\n

Applications to early stages of the marketing funnel include audience segmentation, paid media and web personalization, next best action nurture models, and product recommender systems that integrate with marketing automation tools to deliver the right content to the right customer at the right time. Additional machine learning tools can be leveraged to streamline B2B processes farther in the journey with a direct impact on revenue, including prioritizing leads for digital sales, routing leads to the right sellers, and optimizing marketing touches for lead progression. Finally, a complete marketing data science program includes performance measurement machine learning models such as customer lifetime value, multi-touch attribution, and marketing mix models that improve loyalty, increase revenue and optimize marketing spend.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

Heather Johnston Heather Johnston Manager, Data Science and AI at IBM Marketing Heather Johnston leads the Data Science & AI team for IBM Marketing. Her team builds scaled production-level AI capabilities to bring IBM’s marketing demand generation platform to life to listen and respond to client needs intelligently and automatically. 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Her team builds scaled production-level AI capabilities to bring IBM's marketing demand generation platform to life to listen and respond to client needs intelligently and automatically.<\/p> <p>Heather\u2019s nearly fifteen years of experience in data science includes six years in a manager role and three years in digital marketing, where she worked on A\/B testing and web personalization. Her past work includes Customer Lifetime Value, Cross Sell Recommendations, Lead Touch Optimization, Marketing Effectiveness, and Client Experience Analytics.<\/p> <p>Before joining IBM, Heather was a mathematics professor at Rutgers University, University of Massachusetts, and Vassar College. She holds a PhD in Mathematics from the University of Chicago, an MBA from Rensselaer Polytechnic Institute, and a BS in Mathematics from Massachusetts Institute of Technology.<\/p>\n<p>Professional<\/p>\n<h6>Track: Machine Learning Applications in Marketing<\/h6>\n<h2>Machine Learning in Business-to-business Marketing<\/h2> <p>We will provide an overview of machine learning applications to B2B marketing, detailing business problems that can benefit from data science solutions, providing sample machine learning methodologies, and quantifying the benefits of integrating machine learning into marketing automation and performance measurement systems.<\/p> <p>Applications to early stages of the marketing funnel include audience segmentation, paid media and web personalization, next best action nurture models, and product recommender systems that integrate with marketing automation tools to deliver the right content to the right customer at the right time. Additional machine learning tools can be leveraged to streamline B2B processes farther in the journey with a direct impact on revenue, including prioritizing leads for digital sales, routing leads to the right sellers, and optimizing marketing touches for lead progression. Finally, a complete marketing data science program includes performance measurement machine learning models such as customer lifetime value, multi-touch attribution, and marketing mix models that improve loyalty, increase revenue and optimize marketing spend.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/pages\/755","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/users\/1001077"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/comments?post=755"}],"version-history":[{"count":2,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/pages\/755\/revisions"}],"predecessor-version":[{"id":758,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/pages\/755\/revisions\/758"}],"up":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/pages\/256"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/media\/693"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/wp-json\/wp\/v2\/media?parent=755"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}