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12. Carrier Corp. is using data mining to profile online customers and offer
them cool deals on air conditioners and related products. By using services
from WebMiner, Inc., the air-conditioning, heating, and refrigeration
equipment maker has turned more Web visitors into buyers, increasing
per-visitor revenue from $1.47 to $37.42.
Carrier, part of $26 billion United Technologies Corp., began selling air
conditioners, air purifiers, and other products to consumers via the Web in
1999. However, it sold only about 3,500 units that year, says Paul Berman,
global e-business manager at the Farmington, Connecticut, company. Not
knowing just who its customers were and what they wanted was a big part of
the problem. “We were looking for ways to raise awareness [of Carrier’s
Web store] and convert Internet traffic to sales,” Berman says.
Last year, Carrier gave WebMiner a year’s worth of online sales data, plus a
database of Web surfers who had signed up for an online sweepstakes the
company ran in 1999. WebMiner combined that with third-party
demo-graphic data to develop profiles of Carrier’s online customers. The
typical customer is young (30 to 37), Hispanic, and lives in an apartment in
an East Coast urban area.
WebMiner matched the profiles to ZIP codes and developed predictive
models. Since May, Carrier has enticed visitors to its Web site
(www.buy.carrier.com) with discounts. When they type in their ZIP codes,
WebMiner establishes a customer profile and pops up a window that offers
appropriate products, such as multi-room air conditioners for suburbanites
or compact models for apartment dwellers. “It’s the first time we’ve
intelligently delivered data-driven promotions,” Berman says.
Online sales have exceeded 7,000 units this year, Berman says, compared
with 10,000 units for all of last year. Carrier chose the WebMiner service
because it was quick to implement and is relatively inexpensive—$10,000 for
installation and a $5 fee to WebMiner for each unit sold, compared with
6-figure alternatives.
a. The DM application used by Carrier was one that was predictive in
nature. Could a descriptive model also be used? How would you use it, and
what outputs would you expect? Would they be of any use to Carrier?
b. What other data-driven promotions could Carrier come up with using
other data mining techniques?
c. What manufacturing-driven applications can Carrier implement using
data mining? Hint: How can it be used to forecast manufacturing defects?
d. What finance-driven applications can Carrier implement using data
mining?
Hint: How can Carrier use DM to distinguish on-time paying customers
from doubtful ones?
SOURCE: Whiting 2001.
a. The only descriptive model that can be used is the multiple regression,
where we can develop a formula to determine the relationship between the
online sales on one hand and various variables on the other. "Y = a + bX1
+ cX2 +…" This model can predict the dependent variable (sales volume)
using the independent variables. The limitation of this model is that all
independent variables must be quantified (Average income, family
members, etc.). So it will not be helpful for Carrier, as some important
attributes cannot be quantified (place of living, nationality, etc.).
b. By realizing from a DM clustering model that their customer-base is
located in the east coast, they can install manufacturing facilities in the
proper facilities that can cover the largest possible area, and reduce
shipping costs).
c. One of the many applications for DM is quality inspection. Certain quality
parameters can be entered in the application, and whenever the pattern
changes, the defects can be identified immediately.
d. By entering historical data that includes customers who paid on time, and
others who defaulted, a DM model can be developed to assign the
attributes of each type and to make predictions about the payment habits of
new customers.