Page 374 - Using MIS

342 Chapter 9 Business Intelligence Systems
What Are Typical BI Applications?

This section summarizes three BI applications that will give you a flavor of what is possible.
Because business intelligence and the related term BigData are hot topics today, a Web search
will produce dozens of similar examples. After you read this chapter, search for more applica-
tions that appeal to your particular interests.

Identifying Changes in Purchasing Patterns

Most students are aware that business intelligence is used to predict purchasing patterns.
Amazon made the phrase “Customers who bought . . . also bought” famous; when we buy
something today, we expect the e-commerce application to suggest what else we might
want. Later in this chapter, you’ll learn some of the techniques that are used to produce such
recommendations.
More interesting, however, is identifying changes in purchasing patterns. Retailers know
that important life events cause customers to change what they buy and, for a short interval, to
form new loyalties to new store brands. Thus, when people start their first professional job, get
married, have a baby, or retire, retailers want to know. Before BI, stores would watch the local
newspapers for graduation, marriage, and baby announcements and send ads in response. That
is a slow, labor-intensive, and expensive process.
Target wanted to get ahead of the newspapers and in 2002 began a project to use
purchasing patterns to determine that someone was pregnant. By applying business intel-
ligence techniques to its sales data, Target was able to identify a purchasing pattern of lo-
tions, vitamins, and other products that reliably predicts pregnancy. When Target observed
that purchasing pattern, it sent ads for diapers and other baby-related products to those
customers.
Its program worked—too well for one teenager who had told no one she was pregnant.
When she began receiving ads for baby items, her father complained to the manager of the lo-
cal Target store, who apologized. It was the father’s turn to apologize when he learned that his
daughter was, indeed, pregnant. 3

BI for Entertainment

Amazon, Netflix, Pandora, Spotify, and other media-delivery organizations generate billions of
bytes of data on consumer media preferences. Using that data, Amazon has begun to produce
its own video and TV, basing plots and characters and selecting actors on the results of its BI
analysis. 4
Netflix decided to buy House of Cards, starring Kevin Spacey, based on its analysis of cus-
tomers’ viewing patterns. Similarly, Spotify processes data on customers’ listening habits to
determine locations where particular bands’ songs are heard most often. Using that data, it then
recommends the best cities for popular bands and other musical groups to perform. 5
A popular adage among marketing professionals is that “buyers are liars,” meaning they’ll
say they want one thing but purchase something else. That characteristic reduces the efficacy of
marketing focus groups. BI produced from data on watching, listening, and rental habits, how-
ever, determines what people actually want, not what they say. Will this enable data miners like
Amazon to become the new Hollywood? We will see.

3 Charles Duhigg, “How Companies Learn Your Secrets,” The New York Times, last modified February 16, 2012,
www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=2&hp=&pagewanted=all&.
4 Alistair Barr, “Crowdsourcing Goes to Hollywood as Amazon Makes Movies,” Reuters, last modified October 10,
2012, www.reuters.com/article/2012/10/10/us-amazon-hollywood-crowd-idUSBRE8990JH20121010.
5 Martin U. Müller, Marcel Rosenbach, and Thomas Schulz, “Living by the Numbers: Big Data Knows What Your
Future Holds,” Der Spiegel, accessed July 31, 2013, www.spiegel.de/international/business/big-data-enables-
companies-and-researchers-to-look-into-the-future-a-899964.html.