From programming to statistics to machine learning for marketing
With artificial intelligence (AI) and machine learning (ML) taking the spotlight recently, it is imperative for all marketing professionals to cultivate an understanding of what these entail from a practical point of view. It is valuable to know how AI and ML arose from computer programming, some of their strengths, and some of their risks. It is useful to have a game plan for how to approach this powerful, new technology. This paper describes ML through the lens of prior programming methods and describes what it can and cannot do, while introducing some of the risks associated with using ML for marketing.
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Document Type: Research Article
Publication date: March 1, 2018
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- Applied Marketing Analytics is the major new professional journal publishing in-depth, peer-reviewed articles on all aspects of marketing analytics. Guided by an expert Editorial Board each quarterly 100-page issue - published both in print and online - features detailed, practical articles written by and for marketing analytics professionals on innovative thinking, strategies, techniques, software and applied research showing how major brands are collecting, interpreting and acting on marketing analytics, both around the world and across varied digital and non-digital marketing channels. Learn how to measure the effectiveness of your marketing initiatives more accurately, how this compares to your competitors, identify gaps in your marketing analytics program and what metrics that support sound marketing decision making - and add to the bottom line.
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