Using Data Science Tools for Email Audience Analysis: A Research Guide

Abstract

Email is a crucial vehicle for media companies to generate reader revenue, yet the ways we talk about and measure email have not changed for almost two decades. Flawed, static measures can distract from success and lead to misguided strategies, crippling the development of new products. Using open source techniques from other fields, data scientists are able to provide a more complete picture of an organization’s readers and viewers. The Shorenstein Center Notebooks (written in Python and available on GitHub as a free, open-source tool) take a first step at demonstrating new ways to analyze list composition and performance in order to help editors and publishers ask and answer more nuanced questions. The open source community has a lot in common with journalism: transparency, collaboration, etc. Although journalism often uses data science tools, very little has been published about how to use data science to analyze audience and grow reach. Most single source newsrooms are not large enough to support a dedicated data science team, but all face similar challenges of figuring out how to sort through their mounds of data to gain crucial audience insights. Data analysis holds the key to building revenue sustainability—the bedrock issue for any enterprise—in our increasingly digital world. The Shorenstein Center Notebooks represent a change in mindset toward creating a freely available, shared knowledge base.