Название: Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques, 3rd Edition
Автор: Mike Grigsby
Издательство: Kogan Page
Год: 2023
Страниц: 337
Язык: английский
Формат: pdf (true), epub
Размер: 22.4 MB

Who is most likely to buy and what is the best way to target them? How can I use both consumer analytics and modelling to improve the impact of marketing campaigns? Marketing Analytics takes you step-by-step through these areas and more.

Marketing Analytics enables you to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, it offers a complete resource for how statistics, consumer analytics and modelling can be put to optimal use.

This revised and updated third edition of Marketing Analytics contains new material on forecasting, customer touchpoints modelling, and a new focus on customer loyalty. With accessible language throughout, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Supported by a glossary of key terms and supporting resources consisting of datasets, presentation slides for each chapter and a test bank of self-test question, this book supplies a concrete foundation for optimizing marketing analytics for day-to-day business advantage.

Big Data has an element of different degrees of structure. I’m talking about the very common structured data through semi-structured and all the way to unstructured data. Structured data includes the traditional codes that are expected by type and length – it is uniform. Unstructured data is everything but that. It can include text mining from, say, call records and free-form comments; it can also include video, audio and graphics, etc. Big Data allows us to structure this unstructured data.

Just to be obvious: data that is not valuable can barely be called data. It can be called clutter or noise. But it’s true that what is clutter to me might be gold to you. To the marketing analyst, what is typically of value is the page the visitor came from and is going to, how long they were there, what they clicked on, etc. What web browser they used or whether it is an active server page, or the time to load the wire frame (all probably critically important to some data scientist somewhere), is of little to no value to the marketer. So Big Data can generate a lot of stuff but there has to be a, say, text-mining technique/technology to put it in a form that can be consumed. This is what makes it valuable – not the quantity but the quality.