Privacy and eReading Part 1: Overview
Based on the Wall Street Journal article, I thought I would explore the extent of companies’ spying on digital readers. Data mining and predictive analysis has become an important component of marketing. In The Power off Habit Charles Duhigg shares the story of how Target built a data model that predicted when a woman was pregnant based upon her buying choices. The expecting parent demographic is the holy grail of consumers. They are price insensitive, tend to buy everything at the same place, and are willing to buy almost anything.
Target statistical expert was able to gather the data and determine based upon purchases like vitamins, scent free lotions and soaps, washcloths, and the like (about 25 items in total) that you might be pregnant. Target would then start sending you baby coupons. Target then clued into the idea that shoppers don’t want to feel spied on so they mixed the baby coupons in with other stuff so it made it look like you were getting a general mailer when, in fact, Target is sending you a mailer that is likely quite different from your neighbor’s mailer (particularly if your neighbor is a bachelor with no kids).
How can you avoid this? Pay cash. Don’t use your debit card. Stay disconnected.
There are companies out there that build profiles based on your message board postings, your facebook likes, etc. In other words, ebook companies aren’t the only ones that are spying on you. Everyone does.
Today, however, we’ll discuss just some general data points of information that digital publishers are acquiring from readers. Michael Tamblyn of Kobo Books gives several talks a year about the data derived from Kobo readers. One of his presentations is here entitled “What do eBook Customers Really, Really Want?” Tamblyn is a great speaker and the information that he shares is fascinating. The presentation begins with “What happens when a group of reading obsessed technophiles get their hands on the richest data set in the history of the book industry?”
From this slide we know that Kobo can tell the reader is using a dedicated e ink device rather than an app. Kobo knows how much she spends per month and whether her purchases are accelerating or decelerating. They can tell what types of books she is buying (fiction) and whether she reads mostly free or mostly paid. Kobo makes a game of the data they are mining and encourage you to share it.
Kobo is tracking how long you spend reading; how long it takes you to complete a book; how many pages per hour you read; how many pages you read per session; and when you read the most. Then Kobo encourages you to share the information on your social networks
Companies are recording your ynnotations, bookmarks, highlights, and notes. Every time you sync your content, companies know where you stopped reading, when you stopped reading and based on your notes and highlights know what is moving you to interact with the story. They can track the samples you download and how many times a sample turns into a purchase. They know where you are from (geographic location based on IP address); what app you are using or whether you use a dedicated device; how much you read while you travel based on varying IP addresses; what type of method of payment; likely if you are female or male.
Kobo isn’t the only one collecting this data and trying to use it to sell more books to its customers. Every retailer does this. The convenience and features that are offered to you in exchange for the data mining might be a worthwhile exchange. Everyone has a different comfort level. The goal of the series is to just make the reader aware.