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Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques
Who is most likely to buy and what is the best way to target them? How can businesses improve strategy without identifying the key influencing factors?
The second edition of Marketing Analytics enables marketers and business analysts 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, this book offers a welcome handbook on how statistics, consumer analytics and modelling can be put to optimal use.
The fully revised second edition of Marketing Analytics includes three new chapters on big data analytics, insights and panel regression, including how to collect, separate and analyze big data. All of the advanced tools and techniques for predictive analytics have been updated, translating models such as tobit analysis for customer lifetime value into everyday use. Whether an experienced practitioner or having no prior knowledge, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Complete with downloadable data sets and test bank resources, this book supplies a concrete foundation to optimize marketing analytics for day-to-day business advantage.
The second edition of Marketing Analytics enables marketers and business analysts 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, this book offers a welcome handbook on how statistics, consumer analytics and modelling can be put to optimal use.
The fully revised second edition of Marketing Analytics includes three new chapters on big data analytics, insights and panel regression, including how to collect, separate and analyze big data. All of the advanced tools and techniques for predictive analytics have been updated, translating models such as tobit analysis for customer lifetime value into everyday use. Whether an experienced practitioner or having no prior knowledge, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Complete with downloadable data sets and test bank resources, this book supplies a concrete foundation to optimize marketing analytics for day-to-day business advantage.
- GenresBusiness
352 pages, Kindle Edition
Published April 3, 2018
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Displaying 1 - 5 of 5 reviews
March 26, 2019
Microanalytics
I came from a mathematical/engineering background into management, and I was challenged with the material in this book – it is not for the faint-hearted. There is a requirement to understand statistical techniques so you can grasp how analytics and microeconomics are used in marketing science. This book is an important element to assist management and marketing insights that go unused or ignored in many companies. The advice here is that while experience and anecdotal insights are important and will always be major factors in management there is a need in well-established companies to balance strategy and decision making with hard real-world data metrics.
The only caveat I’ll add is that for a new start or early-stage company this is probably a huge overhead requiring specialist capability and historical business data. This cannot be provided when new-start companies are operating in an uncertain environment of agile customer feedback, funding issues, and the search for the initial growth and repeatable customer base.
For its heavy mathematical content, Mike Grigsby makes it as light and explanatory as possible. Each chapter is very well structured, starting with its contents, an overview/introduction, detailed content and a conclusion with a very useful checklist. The checklist bullet points outline what would make you “The smartest person in the room” – not unique but well delivered here. There are also various examples used where appropriate in the book, which is a valuable feature.
Essentially this is a book designed for a marketing scientist working with marketing analytics and dare I say big data. The big data issue is addressed in the book and quite rightly it manages the expectation of what can be achieved from big data presuming we really know what big data is.
I would recommend this book to marketing analysts, and Sales and Marketing Directors, and VPs so they can add science to their real-world experiences, approaches and gut feelings.
Many thanks to Kogan Page Ltd Publishing and NetGalley for an ARC version of the book in return for an honest review.
I came from a mathematical/engineering background into management, and I was challenged with the material in this book – it is not for the faint-hearted. There is a requirement to understand statistical techniques so you can grasp how analytics and microeconomics are used in marketing science. This book is an important element to assist management and marketing insights that go unused or ignored in many companies. The advice here is that while experience and anecdotal insights are important and will always be major factors in management there is a need in well-established companies to balance strategy and decision making with hard real-world data metrics.
The only caveat I’ll add is that for a new start or early-stage company this is probably a huge overhead requiring specialist capability and historical business data. This cannot be provided when new-start companies are operating in an uncertain environment of agile customer feedback, funding issues, and the search for the initial growth and repeatable customer base.
For its heavy mathematical content, Mike Grigsby makes it as light and explanatory as possible. Each chapter is very well structured, starting with its contents, an overview/introduction, detailed content and a conclusion with a very useful checklist. The checklist bullet points outline what would make you “The smartest person in the room” – not unique but well delivered here. There are also various examples used where appropriate in the book, which is a valuable feature.
Essentially this is a book designed for a marketing scientist working with marketing analytics and dare I say big data. The big data issue is addressed in the book and quite rightly it manages the expectation of what can be achieved from big data presuming we really know what big data is.
I would recommend this book to marketing analysts, and Sales and Marketing Directors, and VPs so they can add science to their real-world experiences, approaches and gut feelings.
Many thanks to Kogan Page Ltd Publishing and NetGalley for an ARC version of the book in return for an honest review.
May 9, 2023
Laudable philosophy unmatched by the content
The stated aim of the book is a good one - a marketing focussed book explaining which analytic techniques would be best to solve a particular marketing problem, written in a way that is direct, accessible and easy to understand. While the concept is a good one that many marketeers would welcome, sadly the book did not deliver for me.
Mike Grigsby has 25 years of experience including a degree and masters degree in finance and a PhD in marketing science. It’s fair to say that he knows his subject inside out but the book is written in a way that I didn’t find engaging. The information is presented using a statistics first approach rather than providing some clear case studies and then showing what analytics would be most useful and why in each case. The focus on the maths first makes it a dense read and this may be a barrier for some readers. I struggled to work out what stats to use and when and felt that I didn’t gain the knowledge and insight I was hoping to.
I was given this book from the author via netgalley only for the pleasure of reading and leaving an honest review should I choose to.
The stated aim of the book is a good one - a marketing focussed book explaining which analytic techniques would be best to solve a particular marketing problem, written in a way that is direct, accessible and easy to understand. While the concept is a good one that many marketeers would welcome, sadly the book did not deliver for me.
Mike Grigsby has 25 years of experience including a degree and masters degree in finance and a PhD in marketing science. It’s fair to say that he knows his subject inside out but the book is written in a way that I didn’t find engaging. The information is presented using a statistics first approach rather than providing some clear case studies and then showing what analytics would be most useful and why in each case. The focus on the maths first makes it a dense read and this may be a barrier for some readers. I struggled to work out what stats to use and when and felt that I didn’t gain the knowledge and insight I was hoping to.
I was given this book from the author via netgalley only for the pleasure of reading and leaving an honest review should I choose to.
January 6, 2022
A bit of a strange book, confused as to its audience. Maybe.
It's a bit like that college professor that covers the basic material just cause the department makes it so, then proceeds to hammer you with terse explanations and somewhat unhelpful generalizations.
Listen to this, the author spends ten pages covering all of statistics from the basic definition of means all the way to probability. Yeah, and that's fine but why waste that space when you then spend 6 pages explaining what panel regression is and how to use it? And by the way, half of those pages are just tables of statistical output...And so on for most of the chapters.
You do get the sense that the author is overly concerned with breadth rather than depth, and it shows in how shallow some of his chapter read. It's basically a survey of methods rather than a textbook approach to marketing analytics.
The last chapter where he recounts personal experiences in his career all the way from shoe salesman to Ph.D. in tensor analysis is pretty darn good and great practical advice.
It's a bit like that college professor that covers the basic material just cause the department makes it so, then proceeds to hammer you with terse explanations and somewhat unhelpful generalizations.
Listen to this, the author spends ten pages covering all of statistics from the basic definition of means all the way to probability. Yeah, and that's fine but why waste that space when you then spend 6 pages explaining what panel regression is and how to use it? And by the way, half of those pages are just tables of statistical output...And so on for most of the chapters.
You do get the sense that the author is overly concerned with breadth rather than depth, and it shows in how shallow some of his chapter read. It's basically a survey of methods rather than a textbook approach to marketing analytics.
The last chapter where he recounts personal experiences in his career all the way from shoe salesman to Ph.D. in tensor analysis is pretty darn good and great practical advice.
October 24, 2019
This is my analytics bible
I discover web analytics in 2012, while I was in college studying business marketing. I have been doing web analytics since then. I have always wanted to explore predictive analytics with some python libraries. This book gave me a better view of statistics. It’s implied marketing analytics for me and there is some wonderful carrier advice as well.
I discover web analytics in 2012, while I was in college studying business marketing. I have been doing web analytics since then. I have always wanted to explore predictive analytics with some python libraries. This book gave me a better view of statistics. It’s implied marketing analytics for me and there is some wonderful carrier advice as well.
April 4, 2020
Highly recommend for data-driven marketer, but domain knowledge especially in stats is deeply required.
Displaying 1 - 5 of 5 reviews