2014-04-04 06:49:01 - Big Data Applications in the Contact Center: Opportunities and Challenges - a new market research report on companiesandmarkets.com
Big Data can be aggregated from a plethora of sources, including:
Social networks, such as Facebook, Google, LinkedIn, YouTube, and others;
Sensors, such as Radio Frequency Identification (RFID) tags on physical assets, or motion
sensors to detect movement on toll bridges or turnstiles, or GPS on smartphones, or
Population Census data;
Web interactions, such as clickstream data, web page hits, and search indices;
Customer data within vertical markets, such as financial or medical records;
Video, such as surveillance footage or in-store cameras; and
Geographic, psychographic, or demographic data.
In essence, these categories represent just some of the possible sources of data currently being
tapped to gain information about
customers. And while businesses such as Communications
Services Providers (CSPs) and large retailers are using Big Data to gain more intelligence
about customers -- and honing supply chains and marketing programs as a result -- the contact
center also is paying attention to the benefits that Big Data can bring when it comes to the
Big Data projects cut across all industries and are being used to do myriad things including
enhance forecasting models, contribute to product design, or determine consumer buying
patterns, and it´s now being looked at as a way to enhance the customer service industry as
well. Big Data -- in all its variety of sources, volume, and velocity at which it arrives and
must be acted on -- can augment the transaction history of a customer journey.
But handling Big Data also demands a different set of rules than more traditional contact
center data sets. Some data, such as trending topics on social networks, has fleeting value.
Dynamic and unpredictable occurrences, such as a flare-up of activity about a company,
product or service, means that running structured, planned reports as with other contact
center data requires a different level of data storage and tools. However, when combined
with more traditional data sources used in the contact center (such as CRM, WFM, IVR, and
ACD statistics) Big Data can bring a new level of customer insight, and help drive real-time
decisions on customer handling and workflow.
Over the past decade the focus has been on the customer journey and how to improve it.
Traditional contact center metrics provide visibility from a macro customer journey level
down to individual input on a single channel (events). Contact center analysts and supervisors
can drill down and see which DTMF inputs are done in a single transaction in an IVR
system, or go a level higher to see at which point the customer got frustrated and "zeroed
out" to an agent (transaction). They can see what happened between agents and the
customer (interaction) and tie those interactions together to see how the whole issue was
resolved (engagement). This type of data allows companies to gain intelligence on a number
Understanding what the customer wants;
Finding out where there are breakpoints between systems and contact channels (for
instance, did the customer opt out to an agent because the IVR is poorly implemented, or
because they needed something more than self-service could provide?);
Understanding which agents are the best performing and which need training; and
Determining which contacts are being handled the first time, and if not, what needs to be
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