What this book covers

Chapter 1, What is Marketing Analytics?, delves into what we mean by marketing analytics, breaking down the types of analytics, from descriptive to prescriptive, what value they add to the business, and what questions each of them answers.

Chapter 2, Extracting and Exploring Data with Singer and pandas, gives you a brief introduction to ETL and how to extract and handle marketing data, ingestion, and Exploratory Data Analysis (EDA). We will cover the fundamentals of descriptive statistics and go through common data transformations to ensure data normality.

Chapter 3, Design Principles and Presenting Results with Streamlit, takes us through how to properly design a dashboard for marketing data, from design principles to actual implementation. This is instrumental in displaying our results in a presentable way to non-technical audiences.

Chapter 4, Econometrics and Causal Inference with Statsmodels and PyMC, deals with the fact that, as a marketing analyst, you usually do not have the luxury of big data to feed into machine learning models. The data will be sparse or low-frequency time series or panel data, which will prevent you from brute-forcing your way through. You need a solid understanding of econometrics and the principles of causality to answer common questions your stakeholders will have.

Chapter 5, Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast, digs deeper into forecasting. Forecasting is one of the fundamental tasks of a marketing data analyst. It is also one of the most complex fields in statistics. You should understand which models to apply, when to apply them, and what to avoid. We will review the most common models, from ARIMA to ETS, and what are the common pitfalls in forecasting time series.

Chapter 6, Anomaly Detection with StatsForecast and PyMC, describes how to perform anomaly detection. In the daily life of an analyst, you will be tasked, more often than not, with finding anomalies before they create business impact. You will also have to understand how to deal with low-frequency data and derive anomalies while avoiding false positives.

Chapter 7, Customer Insights – Segmentation and RFM, helps us discover how to segment customers and create valuable profiles for better marketing. We’ll explore customer segmentation and RFM scoring.

Chapter 8, Customer Lifetime Value with PyMC Marketing, builds upon the previous chapter by showing how to assign a value to our customers and segments to optimize our marketing efforts, and to evaluate the ROI of our activities by estimating how much customers are worth.

Chapter 9, Customer Survey Analysis, describes customer satisfaction analysis through surveys. Analyzing customer satisfaction is an integral part of customer satisfaction management, which is an important part of CRM. We’ll go through how to analyze survey data to derive insights, how to calculate samples, and the pitfalls of NPS.

Chapter 10, Conjoint Analysis with pandas and Statsmodels, starts with a description of what conjoint analysis is and what it is used for. We’ll cover some of the techniques used to derive useful insights, customize your product offering with conjoint analysis, and explain how to build the analysis from the ground up.

Chapter 11, Multi-Touch Digital Attribution, explains in detail what digital attribution is. Marketing attribution is a fundamental problem in marketing analytics. How to attribute outcomes to marketing channels will change the conclusions you derive from channel evaluation. This chapter will describe the most common attribution methods and how to build them.

Chapter 12, Media Mix Modeling with PyMC Marketing, describes the fundamental issue of understanding how to use Media Mix Modeling to optimize your marketing activities. Understanding a marketing channel’s performance is important, but of critical importance in modern marketing analytics is understanding how channels interact with each other. The answer to this question allows us, as analysts, to advise marketing teams on optimal budget allocation.

Chapter 13, Running Experiments with PyMC, starts by explaining the fundamentals of what an experiment is. Running experiments in marketing is a fundamental technique for optimization and efficiency. We’ll go through the fundamentals of how to run experiments and how to analyze the outcome, while avoiding the most common pitfalls.