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Once we have identiied less-used products, the next analysis question is
whether we can isolate the cause of customer disinterest. By analyzing usage
patterns, we can differentiate between successful products and unsuccessful ones.
Were the unsuccessful ones never launched? Did many users get stuck with the
initial security screen? Maybe the identiication process was too cumbersome.
How many users could use the product to perform basic functions offered by
the product? What were the highest frequency functions?
The next level of analysis is to understand component failures. How many
times did the product fail to perform? Where were the failures most likely? What
led to the failure? What did the user do after the failure? Can we isolate the
component, replace it, and repair the product online?
These analysis capabilities can now be combined with product changes to
create a sophisticated test-marketing framework. We can make changes to the
product, try the modiied product on a test market, observe the impact, and, after
repeated adjustments, offer the altered product to the marketplace.
Let us illustrate how Big Data is shaping improved product engineering
and operations at the communications service providers. Major CSPs collect
enormous amounts of data about the network, including network transport
information coming from the routers and the switches, as well as usage infor-
mation, popularly known as call detail records (CDRs), which are recorded
each time we use telephones to connect with one another. As the CSP networks
grew in sophistication, the CDRs were extended to data and video signals using
IPDRs. Most CSPs refer to this usage information as xDRs (where
x
is now a
variable that can be substituted for “any” usage information). For larger CSPs,
the usage statistics not only are high volume (in billions of transactions a day)
but also require low-latency analytics for a number of applications. For example,
detecting a fraudulent transaction or abusive network user in the middle of a
video download or call may be more valuable than inding out this information
the next day. In addition, it is always a strategic driver for CSPs to lay out all the
network and usage information on their network topology and geography and use
a variety of automated analytics and manual visualization techniques to connect
the dots between network trouble or ineficiencies and usage. The analytics
provides CSP with a valuable capability to improve the quality of the communi-
cation. If every user call is dropping in a particular area that is a popular location
for premier customers, it could lead to churn of those customers to competitors.
The information about xDRs, network events, customer trouble tickets,
blogs, and tweets in the social media can be correlated for a variety of business
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