Katie Sweet & Karl Wirth 
One-to-One Personalization in the Age of Machine Learning [EPUB ebook] 
Harnessing Data to Power Great Customer Experiences

Підтримка

In a world cluttered with messages competing for people’s attention all of the time, marketers must surface relevant information if they want to capture the attention of their consumers or business buyers. And as consumers experience personalized experiences from other companies like Amazon, Netflix and Spotify, they grow to expect it from all the other companies they interact with, regardless of industry. One-to-one personalization is about tailoring an experience to a visitor or customer at the individual level. The experience could be on a website, mobile app, email, in-person, or any other channel where a person interacts with your brand or company. In contrast to a one-to-all experience (one that is the same for everyone) or a one-to-many experience (one that is targeted to a segment or group of people), a one-to-one experience is truly unique for each person. While marketers have dreamed of delivering one-to-one experiences for over 25 years, it has not been possible without machine learning. Machine learning can combine many different sources of data, draw insights about what that data says about an individual, and determine the most relevant experience to deliver – in a far more scalable way than has ever been possible in the past In One-to-One Personalization in the Age of Machine Learning, discover what one-to-one personalization is all about, how it has evolved and what the future entails. Learn how it’s driven by machine learning, delivered across channels and powered by in-depth customer data. Get inspired by the potential for your business and gain insights on how to develop your own personalization strategy and program. Discover how to turn the one-to-one dream into a reality.

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Мова Англійська ● Формат EPUB ● ISBN 9780999369425 ● Видавець BookBaby ● Опубліковано 2017 ● Завантажувані 3 разів ● Валюта EUR ● Посвідчення особи 5535596 ● Захист від копіювання Adobe DRM
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