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handheld devices, giving publishers an opportunity to understand what we read,
how many times we read it, and which parts we look at. We watch television
using a two-way set-top box that can record each channel click and correlate
it to analyze whether the channel was switched right before, during, or after a
commercial break. Even mechanical products such as automobiles are increasing
electronic interactions. We make all of our ordering transactions electronically,
giving third parties opportunities to analyze our spending habits by month, by
season, by ZIP+4, and by tens of thousands of micro-segments. Usage data can
be synthesized to study the quality of customer experience and can be mined for
component defects, successes, or extensions. Marketing analysts can identify
micro-segmentations using this data. For example, in a wireless company, we
isolated problems in the use of cell phones to defective device antenna by analyz-
ing call quality and comparing it across devices.
Products can be test marketed and changed based on feedback. They can
also be customized and personalized for every consumer or micro-segment based
on their needs. Analytics plays a major role in customizing, personalizing, and
changing products based on customer feedback. Product engineering combines
a set of independent components into a product in response to a customer need.
Component quality impacts overall product performance. Can we use analytics
to isolate poorly performing components and replace them with good ones? In
addition, can we simplify the overall product by removing components that are
rarely used and offer no real value to the customer? A lot of product engineering
analytics using customer experience data can lead to building simpliied products
that best meet customer requirements.
To conduct this analysis and predictive modeling, we need a good under-
standing of the components used and how they participate in the customer
experience. Once a good amount of data is collected, the model can be used to
isolate badly performing components by isolating the observations from custom-
er experience and tracing them to the poorly performing component. Complex
products, such as automobiles, telecommunications networks, and engineering
goods, beneit from this type of analytics around product engineering.
The irst level of analysis is in identifying a product portfolio mix and its
success with the customers. For example, if a marketer has a large number of
products, these products can be aligned to customer segments and their usage.
We may ind a number of products that were purchased and hardly used, leading
to their discontinuation in six months, while other products were heavily used
and sparingly discontinued.
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