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thousands of components. More integrated data
platforms now allow companies and their supply
chain partners to collaborate during the design
phase—a crucial determinant of fnal manufacturing
costs.
2 If you could test all of your decisions,
how would that change the way you
compete?
Big data ushers in the possibility of a fundamentally
diferent type of decision-making. Using controlled
experiments, companies can test hypotheses and
analyze results to guide investment decisions and
operational changes. In efect, experimentation can
help managers distinguish causation from mere
correlation, thus reducing the variability of outcomes
while improving fnancial and product performance.
Robust experimentation can take many forms.
Leading online companies, for example, are
continuous testers. In some cases, they allocate
a set portion of their webpage views to conduct
experiments that reveal what factors drive higher
user engagement or promote sales. Companies
selling physical goods also use experiments to aid
decisions, but big data can push this approach to a
new level. McDonald’s, for example, has equipped
some stores with devices that gather
operational data as they track
customer interactions, trafc in stores,
and ordering patterns. Researchers
can model the impact of variations
in menus, restaurant designs, and
training, among other things, on
productivity and sales.
Where such controlled experiments
aren’t feasible, companies can use
natural experiments to identify the
sources of variability in performance.
One government organization, for
instance, collected data on multiple
groups of employees doing similar
work at diferent sites. Simply making
the data available spurred lagging workers to improve
their performance.
Leading retailers, meanwhile, are monitoring the
in-store movements of customers, as well as how
they interact with products. These retailers combine
such rich data feeds with transaction records and
conduct experiments to guide choices about which
products to carry, where to place them, and how and
when to adjust prices. Methods such as these helped
one leading retailer to reduce the number of items it
stocked by 17 percent, while raising the mix of higher-
margin private-label goods—with no loss of market
share.
3 How would your business change if you
used big data for widespread, real-time
customization?
Customer-facing companies have long used data
to segment and target customers. Big data permits
a major step beyond what, until recently, was
considered state of the art by making real-time
personalization possible. A next-generation retailer
will be able to track the behavior of individual
customers from Internet click streams, update their
preferences, and model their likely behavior in real
time. They will then be able to recognize when
customers are nearing a purchase decision and nudge
the transaction to completion by bundling preferred
products, ofered with reward program savings. This
real-time targeting, which would also leverage data
from the retailer’s multitier membership rewards
program, will increase purchases of higher-margin
products by its most valuable customers.
Retailing is an obvious place for data-
driven customization because the
volume and quality of data available
from Internet purchases, social-network
conversations, and, more recently,
location-specifc smartphone interactions
have mushroomed. But other sectors, too,
can beneft from new applications of data,
along with the growing sophistication of
analytical tools for dividing customers into
more revealing microsegments.
One personal-line insurer, for example,
tailors insurance policies for each
customer, using fne-grained, constantly-
updated profles of customer risk, changes
in wealth, home asset value, and other
data inputs. Utilities that harvest and analyze data on
customer segments can markedly change patterns of
power usage. Finally, HR departments that more fnely
segment employees by task and performance are
beginning to change work conditions and implement
incentives that improve both satisfaction and
productivity.