a practical guide to improving consumer insights using data techniques
- ISBN: 9781398608191
- Editorial: Kogan Page Ltd.
- Fecha de la edición: 2022
- Lugar de la edición: London. Reino Unido
- Edición número: 3rd ed.
- Encuadernación: Rústica
- Medidas: 24 cm
- Nº Pág.: 256
- Idiomas: Inglés
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.
Section - 00: Introduction;Section - PART ONE: How can marketing analytics help you?;
Chapter - 01: Overview of statistics
Chapter - 02: Consumer behaviour and marketing strategy
Chapter - 03: What is an insight?
Section - PART TWO: Dependent variable techniques;
Chapter - 04: Modelling demand and elasticity
Chapter - 05: Polynomial distributed lags
Chapter - 06: Using Poisson regression
Chapter - 07: Logistic regression and market basket analysis
Chapter - 08: Survival modelling and lifetime value
Chapter - 09: Panel regression and same store sales
Chapter - 10: Introduction to forecasting;
Section - PART THREE: Interrelationship techniques;
Chapter - 11: Simultaneous equations
Chapter - 12: Principal components and factor analysis
Chapter - 13: Segmentation overview
Chapter - 14: Tools of segmentation;
Section - PART FOUR: Focus on media and loyalty;
Chapter - 15: Modelling marcom value
Chapter - 16: Media mix modelling
Chapter - 17: Overview of loyalty
Chapter - 18: Loyalty with SEM
Chapter - 19: The customer loyalty journey;
Section - PART FIVE: More important topics for everyday marketing;
Chapter - 20: Statistical testing
Chapter - 21: Introduction to Big Data
Chapter - 22: Conclusion - The finale
Chapter - 23: References
Chapter - 24: Further reading