A major car rental
agency couldn’t process a refund at the counter for a customer, and told the
customer that the reason was because the customer had booked his reservation
online and that the e-commerce channel of the company was “a separate
organization, and you’ll have to deal with them directly.”
In another case, a call
center agent in a bank advised a customer to pursue getting a home equity loan
for a building project – when the customer had already been turned down for a
similar loan by the bank’s lending department.
Welcome to the world
of “disconnects” that retailers face at the same time that their customers
expect them to be fully cognizant of every interaction they have ever had with
the company. Retailers have a word for understanding customer expectations.
They call it “omnichannel”
because it represent a 360-degree understanding of each customer, based on
every single channel (e.g., e-commerce, brick and mortar, etc.) through which
the customer has ever done business with the company.
The pursuit of “omnichannel”
awareness of every customer is challenging retailers and the financial services
industry – because most enterprises have disparate systems in their call centers,
e-commerce, and brick and mortar channels that don’t talk to each other. On top
of this, there is little “big data” intelligence coming in from the Web
on customers, their purchases and their buying habits – which retailers ideally
want in real time.
X+1 is a cloud services company that entered
into this space in 1999-2000, with an initial goal of focusing its solutions on
what it calls “data-driven marketing decisions.”
“What we focus
on is a digital market hub,’ said Leon Zemel, x+1’s Chief Analytics Officer. In
other words, we gather information from any channel that carries real time customer
Of course, Web-based
data analytics in itself isn’t enough to get companies to an “omnichannel”
understanding of their customers – because these customers still also patronize
brick and mortar stores.
To include brick and
mortar customer information, x+1 obtains offline files from its clients’
systems, and then blends data from these files with what has been gathered
about customers online. End goals for retailers are to not only get the traditional
demographics about customers (e.g., where they live, what their ages are, etc.),
but to also deduce psychographic
information about customers that can tell a retailer more about what customers
are likely to buy next. Omnichannel customer information can also be grouped
through analytics into profiles of customers with similar buying patterns. This
allows companies to better pinpoint products and promotions to more highly
defined customer buying segments.
Retailers are building
out their big data analytics staffs to include statisticians, programmers, and
other skillsets, so they can better capitalize on big data analytics. In the
meantime, they can lean on cloud services companies that already have the big
data science teams and methods in place.
Equally important for
retailers is being able to take action on big data as it flows in about consumers
from the Web so they can anticipate what consumers are going to buy at any
given moment. “We can tell a retailer that a client has just gone to its
Website looking for information about a specific item, and this information can
automatically trigger a process that pings the customer with an email about the
item,” said Zemel. “We can even ping the call center.”
Real time awareness
cultivated from big data flowing in from the Web and then married to off-line
brick and mortar data will definitely bring retailers closer to their customers
– and will avert embarrassing episodes like trying to pitch a loan in the call
center to someone who’s already been refused that loan at a brick and mortar
we are now seeing solid use cases emerge for companies that are able to
successfully merge big data with traditional data from their internal systems
of record for new insights into consumer behavior.