| Managing Marketing Information to Gain Customer Insights 115

Chapter 4 | Managing Marketing Information to Gain Customer Insights 115

116 Part Two | Understanding the Marketplace and Consumers

cates, following consumers online and stalking through myriad computer networks, where it’s

a rainy day in January. All that data streams

This can stretch a marketing campaign that

them with ads feels more than just a little sorted, cataloged, analyzed, and then used to

would have reached one million prospects into

creepy. Behavioral targeting, for example, has deliver ads aimed squarely at you, potentially

one that reaches eight million or ten million

already been the subject of congressional and anywhere you travel on the Web. It’s called

prospects, most of them new.

regulatory hearings. behavioral targeting—tracking consumers’ on-

Despite such concerns, however, online line behavior and using it to target ads to

Online listening. Behavioral targeting. So-

listening will continue to grow. And, with ap- them. So, for example, if you place a cell

cial targeting. All of these are great for mar-

propriate safeguards, it promises benefits for phone in your Amazon.com shopping cart but

keters as they work to mine customer insights

both companies and customers. Tapping into don’t buy it, you might expect to see some ads

from the massive amounts of consumer infor-

online conversations and behavior lets compa- for that very type of phone the next time you

mation swirling around the Web. The biggest

nies “get the unprompted voice of the con- visit your favorite ESPN site to catch up on the

question? You’ve probably already guessed it.

latest sports scores.

As marketers get more adept at trolling blogs,

sumer, the real sentiments, the real values, and

the real points of view that they have of our That’s amazing enough, but the newest

social networks, and other Web domains,

products and services,” says P&G’s Bush. wave of Web analytics and targeting take online

what happens to consumer privacy? Yup,

“Companies that figure out how to listen and eavesdropping even further—from behavioral

that’s the downside. At what point does so-

respond . . . in a meaningful, valuable way are targeting to social targeting. Whereas behav-

phisticated Web research cross the line into

going to win in the marketplace.” After all, ioral targeting tracks consumer movements

consumer stalking? Proponents claim that be-

knowing what customers really want is an es- across Web sites, social targeting also mines in-

havioral and social targeting benefit more than

sential first step in creating customer value. dividual online social connections.

abuse consumers by feeding back ads and

And, as one online information expert puts it, “It’s getting back to the old adage that birds of

products that are more relevant to their inter-

“The Web knows what you want.” a feather flock together,” says a social target-

ests. But to many consumers and public advo-

ing expert. Research shows that consumers shop a lot like their friends and are five times more likely to respond to ads from brands

Sources: Adapted excerpts, quotes, and other information from Stephen Baker, “The Web Knows What You friends use. So identifying and targeting

Want,” BusinessWeek, July 27, 2009, p. 48; Brian Morrissey, “Connect the Thoughts,” Adweek, June 29, 2009, friends of current prospects makes sense. So-

pp. 10–11; Paul Sloan, “The Quest for the Perfect Online Ad,” Business 2.0, March 2007, p. 88; Abbey Klaassen, cial targeting links customer data to social in-

“Forget Twitter; Your Best Marketing Tool Is the Humble Product Review,” Advertising Age, June 29, 2009, pp. 1, teraction data from social networking sites. In

17; David Wiesenfeld, Kristin Bush, and Ronjan Sikdar, “Listen Up: Online Yields New Research Pathway,” Nielsen effect, it matches a prospect with his or her

Consumer Insights, August 2009, http://en-us.nielsen.com/; and Elizabeth A. Sullivan, “10 Minutes with Kristin closest connections and targets them as well.

Bush,” Marketing News, September 30, 2009, pp. 26–28.

TABLE | 4.4 Types of Samples

Probability Sample Simple random sample

Every member of the population has a known and equal chance of selection.

Stratified random sample The population is divided into mutually exclusive groups (such as age groups), and random samples are drawn from each group.

Cluster (area) sample The population is divided into mutually exclusive groups (such as blocks), and the researcher draws a sample of the groups to interview.