Marketing Intelligence

Volume 2: Developing the Models and Analysis

De: Kyle Allison

Narrado por: Virtual Voice

Unlock the full potential of data in shaping innovative and effective strategies with this second installment of a three-volume series. Transform your marketing data into confident, decision-ready models that finally show you what’s working and where to invest next.

Journey step by step from “we have a lot of data” to “we have a clear plan.” Start by nailing the real marketing problem you’re trying to solve, then move through descriptive analytics, diagnostic analytics, and data preparation so your dashboards, reports, and SQL/Excel/Python work actually line up with the questions the business is asking. Along the way, you’ll see how brands like Starbucks, Amazon, and modern e-commerce retailers use data storytelling, segmentation, and attribution to turn scattered metrics into sharp customer insights and smarter campaign decisions.

Next, the book dives into the core models every modern marketer needs: regression and correlation for understanding relationships between ad spend, pricing, and sales; clustering for customer segmentation; attribution models that give search, social, email, and offline channels proper credit; and predictive analytics that estimate churn, conversion, and lifetime value before they happen. See how to structure your datasets, engineer features, handle bias, split training and test sets, and avoid overfitting so your machine learning and marketing analytics hold up in the real world.

Finally, get a practical tour of marketing mix modeling (MMM) and time-series forecasting, showing how to quantify the impact of TV, paid search, social, retail promotions, and email on revenue and profit. Clear examples, mini-cases, and visuals illustrate how to build scenarios, reallocate budgets, forecast demand, and connect your marketing mix models to inventory planning, pricing strategy, and brand growth. Whether you work primarily in spreadsheets, BI tools like Power BI, or coding environments like Python, you’ll find concrete starting points you can lift straight into your own workflow.

By the time you’ll reach the last page, you’ll have a toolkit for segmentation, attribution, predictive modeling, marketing mix modeling, forecasting, and model evaluation that fits together into one coherent system for data-driven marketing. More importantly, you’ll know how to turn those models into decisions people actually follow, and be able to bridge the gap between marketing analytics, marketing strategy, and real business impact, setting you up perfectly for Volume 3’s focus on delivering strategic, enterprise-wide influence.