(Page 3 of 3)
practices they’ve already got covered?
The answer: it depends. Data mining and new analytics technology can
be viewed as a speedier, more powerful way to feed the kind of database
marketing direct merchants have always done. It’s already delivering bottom
line results for companies that use it to manage growing amounts of customer
information across channels. Fingerhut and others are effectively using
data mining that focuses largely on purchase history to zero in even closer
to what customers want, and to target outreach accordingly.
But industry observers say buying behavior is just the start, and an
even more robust use of the tons of data being gathered online and across
channels is yet to come. “It’s now possible, for example, to measure customers’
viral propensity,” says David Daniels, an analyst with Jupiter Media Metrix.
“If I’m a loyal customer, I’m influencing other people to buy from the
brand. There’s software that has the ability to track whether customers
send e-mails from the merchant to someone else. You can also measure behavior
that indicates the customer isn’t satisfied and work that into a retention
strategy. AOL, for example, monitors how much time people spend online
with them. When they see a customer’s use going down, the customer will
receive incentives to come back, like $10 off at one of their affiliate
merchants.”
In other words, if strip mining the data is already
producing results, digging deeper into databases could yield even more
for merchants, as they gain new ways of defining value among customer
segments and target marketing approaches to match. For many, for now,
that will wait for a day when staff and budgets catch up with what technology
makes possible. And if increasingly sophisticated data mining and analysis
can deliver the formula retailers have been looking for, it’s a day that
will likely arrive soon. “The object here is to find the right buyer,
match them with the right products and right price and the right channel.
That’s the ultimate problem of retailing, and technology today, if used
in the right way, can make huge advances in that direction,” Xchange’s
Green says. “We’re just beginning to learn how powerful it can be.”
It’s
not what they buy, it’s how they buy it
While
most retailers segment their customers based on what they buy-if they
bought a $1,000 lamp maybe they’d be interested in an oriental rug-Eddie
Bauer is segmenting customers based on how they shop. “Our merchandise
assortment is so much more narrow that we might only be promoting three
men’s sweaters at one time. We don’t need a complex algorithm to recommend
one of the three sweaters,” says Michael Boyd, director of customer relationship
management for Eddie Bauer.
To come up with segments based on shopping style, Eddie Bauer conducted
an exploratory data mining and analysis exercise, looking at behavioral
data that defined shopping preferences. It considered such factors as
what percentage of a shopper’s total purchases were made on mark-down,
what colors a given customer buys, and what time of day a customer is
most likely to do a transaction. All provided clues linked to larger predictive
patterns. Eddie Bauer also mined its database of attitudinal research
looking for more ways to classify customers in terms of how they approached
shopping.
Two segments emerged from the data. One, dubbed Too Busy to Shop, doesn’t
particularly enjoy shopping for apparel, is willing to pay more for a
speedy shopping experience, and likes to buy outfit solutions rather than
apparel pieces. The Professional Shopper, on the other hand, loves to
shop, is more price-aware and prides herself on the ability to assemble
a look from a variety of different sources.
“We’ve changed our focus to using all of the data we have to draw only
one conclusion: which type of shopper is this?” Boyd says. “And we develop
different versions of that set of communications.” Before the holidays,
the company tested three versions of an e-mail, including a version targeting
Too Busy to Shop, one targeting The Professional Shopper and a control
group. Tests on and off target resulted in sales 7% to 8% higher when
customers got communications targeted for their type. When they received
communications targeting the group they didn’t belong to, the message
actually depressed sales, making them even less likely to buy than the
control group.
The two-segment approach is already in use at Eddie Bauer in the form
of segmented catalog mailings. A roll-out of segmented e-mail campaigns
is likely later in the year-not, however, without additional testing and
validation of the concept.
Comments
Sign In to Make a Comment
Comments are moderated by Internet Retailer and can be removed.