Thomas W. Miller [Thomas W. Miller] - Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python

As marketing data scientists, we must speak the language of businessaccounting, finance, marketing, and management. We need to know about information technology, including data structures, algorithms, and object-oriented programming. We must understand statistical modeling, machine learning, mathematical programming, and simulation methods. These are the things that we do:

Information search. We begin by learning what others have done before, learning from the literature. We draw on the work of academics and practitioners in many fields of study, contributors to predictive analytics and data science.

Preparing text and data. Text is unstructured or partially structured data that must be prepared for analysis. We extract features from text. We define measures. Quantitative data are often messy or missing. They may require transformation prior to analysis. Data preparation consumes much of a data scientists time.

Looking at data. We do exploratory data analysis, data visualization for the purpose of discovery. We look for groups in data. We find outliers. We identify common dimensions, patterns, and trends.

Predicting how much. We are often asked to predict how many units or dollars of product will be sold, the price of financial securities or real estate. Regression techniques are useful for making these predictions.

Predicting yes or no. Many business problems are classification problems. We use classification methods to predict whether or not a person will buy a product, default on a loan, or access a web page.

Testing it out. We examine models with diagnostic graphics. We see how well a model developed on one data set works on other data sets. We employ a training-and-test regimen with data partitioning, cross-validation, or bootstrap methods.

Playing what-if. We manipulate key variables to see what happens to our predictions. We play what-if games in simulated marketplaces. We employ sensitivity or stress testing of mathematical programming models. We see how values of input variables affect outcomes, payoffs, and predictions. We assess uncertainty about forecasts.

Explaining it all. Data and models help us understand the world. We turn what we have learned into an explanation that others can understand. We present project results in a clear and concise manner.

Prediction is distinct from explanation. We may not know why models work, but we need to know when they work and when to show others how they work. We identify the most critical components of models and focus on the things that make a difference.

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Data scientists are methodological eclectics, drawing from many scientific disciplines and translating the results of empirical research into words and pictures that management can understand. These presentations benefit from well-constructed data visualizations. In communicating with management, data scientists need to go beyond formulas, numbers, definitions of terms, and the magic of algorithms. Data scientists convert the results of predictive models into simple, straightforward language that others can understand.

Data scientists are knowledge workers par excellence. They are communicators playing a critical role in todays data-intensive world. Data scientists turn data into models and models into plans for action.

The approach we have taken in this and other books in the Modeling Techniques series has been to employ both classical and Bayesian methods. And sometimes we dispense with traditional statistics entirely and rely on machine learning algorithms.

Within the statistical literature, Seymour Geisser introduced an approach best described as Bayesian predictive inference (. In emphasizing the success of predictions in marketing data science, we are in agreement with Geisser. But our approach is purely empirical and in no way dependent on classical or Bayesian thinking. We do what works, following a simple premise:

The value of a model lies in the quality of its predictions.

We learn from statistics that we should quantify our uncertainty. On the one hand, we have confidence intervals, point estimates with associated standard errors, significance tests, and p-valuesthat is the classical way. On the other hand, we have posterior probability distributions, probability intervals, prediction intervals, Bayes factors, and subjective (perhaps diffuse) priorsthe path of Bayesian statistics.

The role of data science in business has been discussed by many (.

Doing data science means implementing flexible, scalable, extensible systems for data preparation, analysis, visualization, and modeling. We are empowered by the growth of open source. Whatever the modeling technique or application, there is likely a relevant package, module, or library that someone has written or is thinking of writing. Doing data science with open-source tools is discussed in .

This appendix identifies classes of methods and reviews selected methods in databases and data preparation, statistics, machine learning, data visualization, and text analytics. We provide an overview of these methods and cite relevant sources for further reading.