Database Reference
In-Depth Information
with the same information or problem yields a different result, depending on
the CSR's attitude and training.) Many of us have been conditioned to expect
mediocre customer experiences as the rule. It doesn't have to be that way,
however, and as consumers become more vocal about not wanting to be pun-
ished because the companies they do business with have been cutting service
to improve their profitability in tough economic times, something is going to
have to change. We've been busy helping firms with this—and the good
news is that in our experience, companies
want
to provide good service, and
Big Data technologies can both help improve service and lower cost at the
same time.
The key is to re-image how the whole process works by making use of all
the available information; to help the business do the right thing, at the right
time, and in the right context. This is not a new goal, of course, but the ability
to actually do it, and to do it well, is greatly facilitated by Big Data.
Most customer engagement data is ignored. Little, if any, context from call
center interactions is captured. The same can be said about clickstream data
that passes through your company's web site. Isn't that a form of communi-
cation? In this case, customers are saying that you've done something that's
interested them enough to learn more about your company or product. This
type of data is usually used at an aggregate level—we look at what customers
are doing as a whole; for example, what products are your customers viewing,
what's been added to a shopping cart, and what carts are being abandoned.
Why not instead use this data at a more personal level to discover what the
customer is actually doing? For example, are carts consistently being abandoned
at the shipping calculation phase of the order process, or after searches that
don't seem to yield results? This type of granular data isn't typically kept and
analyzed because it requires too much storage, perhaps its considered to
have too short of a shelf life to invest in, or can't be processed quickly enough
to be useful at the individual customer level. As another example, consider
the last time you emailed a service provider and it actually changed how
they interacted with you. Shouldn't it? You bet it should! In the Big Data era,
storage and processing concerns that forced a trade-off to lower service lev-
els can start to be addressed.
Now consider a scenario in which you can combine what the warehouse
knows about a customer with these communication events: you'd end up with a
much richer, informed, and timely understanding of what we call the c
ustomer
state
. A great example of customer state mismatch was recently experienced by
