Название: Marketing Measurement and Analytics: An Introduction
Автор: Greg Kihlstrom
Издательство: Mercury Learning and Information
Год: 2025
Страниц: 270
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

This book is a comprehensive guide to aligning your marketing strategies with business objectives while embracing the latest innovations in data and AI-driven analytics. You’ll cover topics such as: aligning measurement to business goals by distinguishing between business and marketing KPIs and choosing the right metrics to guide your strategy; building adaptable systems that link organizational goals to actionable marketing tactics; exploring AI-based tools, predictive analytics, and Generative AI to refine your data strategies; and analyzing results, run multi-channel tests, and create a culture of experimentation and optimization. Through a unique blend of expert insights, practical frameworks, and a recurring case study, this book brings theoretical concepts to life, making them actionable and relatable for any organization.

Features:

Integrates cutting-edge AI technologies into your measurement processes
Uses a recurring case study to demonstrate real-world applications of measurement concepts
Analyzes results and runs multi-channel tests to create a culture of experimentation and optimization

Because Generative AI systems are trained on large datasets to recognize patterns and predict outcomes, it enables them to generate insights that would be nearly impossible for human analysts to derive due to the volume and complexity of the data. Generative AI models go beyond simple analysis; they can simulate potential future scenarios by generating data that mimics real-world behaviors. This predictive capability allows marketers to see not only what has happened or what is happening but also what could happen under different strategies. For instance, AI can simulate the impact of a new marketing campaign on customer purchasing behaviors before it is launched, providing a valuable “test” of potential outcomes.

Traditional data analysis typically focuses on descriptive analytics, which explains what happened, and diagnostic analytics, which explains why it happened. Generative AI extends these capabilities to predictive and prescriptive analytics, not only forecasting future trends but also recommending actions to achieve desired outcomes. This is achieved through sophisticated models such as neural networks, which can extrapolate future outcomes based on historical data. While traditional methods rely on static models that need human intervention for updates, Generative AI continuously learns and improves its algorithms based on new data. This self-learning capability reduces the likelihood of bias and error over time, leading to more accurate analyses. Traditional methods, however, can become outdated quickly and may retain biases from initial model assumptions.

Generative AI can automate the analysis process while bringing a level of depth and precision that was previously unattainable. By leveraging predictive analytics and Machine Learning, this technology can forecast trends, anticipate market changes, and provide recommendations that are both actionable and forward-thinking. It enables marketers to move beyond traditional descriptive analytics to embrace a more dynamic approach to understanding consumer behavior and campaign performance.

Contents:





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