Contact centers work with large amounts of customer data. When done manually, this can wreak havoc. Overloaded employees can ask customers to repeat their details multiple times, causing frustration and slowing down the resolution of the issue at hand. It’s easy to see that situations like these are likely to irritate customers, and thereby downgrade the credibility of the company. Robotic process automation can cancel out the risk of error when extracting, analyzing and cross-referencing data across multiple sources. The employees can then invest all their resources in actually attending customers. In the end, both the customers and the employees are more satisfied and less stressed.

1. (RPA) in
Contact Centers
Abhinav Sabharwal Principal
Consultant & Trainer
@ School of RPA

3. Data management
• Contact centers work with large amounts of
customer data. When done manually, this can
wreak havoc. Overloaded employees can ask
customers to repeat their details multiple times,
causing frustration and slowing down the
resolution of the issue at hand.
• It’s easy to see that situations like these are likely
to irritate customers, and thereby downgrade the
credibility of the company.
• Robotic process automation can cancel out the
risk of error when extracting, analyzing and cross-
referencing data across multiple sources.
• The employees can then invest all their resources
in actually attending customers. In the end, both
the customers and the employees are more
satisfied and less stressed.

4. Creation of summary scripts
• The versatility of software robots when it
comes to data formats allows them to collect
and analyze input data from phone calls.
• This streamlines note-taking activities and
allows employees to focus their attention on
actually listening to (not just hearing)
customers’ requests, and thus address their
requests more efficiently.
• The automatic production of summary
scripts also reduces the average handling
time (AHT). Consequently, employees can
take more calls and be more customer-
oriented, both of which contribute to
boosting productivity..

5. Updating client profiles
• This is another example of a rule-based
process to be performed upon
successive instances of data collection
(similar to point 2 from above).
• When the currently available data no
longer fits with the client profile, the
updating activity can be understood as
applying a rule of the kind “maintain
consistency”, where “consistency” is
defined by equal values of specific
parameters across multiple databases
(e.g., email addresses, phone numbers,
names).
• This is a typical task that software
robots ‘love’ to perform, warranting
error-free updates and thereby
smoothening customer interactions and
avoiding discontent customers.

6. Prediction of customer needs and behavior
• Automating this process provides a very
good illustration of technology integration.
Building on data management capacities,
software robots can scan, analyze, and
cross-examine customers’ queries.
• Based on this, in collaboration with
machine learning devices, they can predict
with high accuracy what customers will
want or do next.
• Such predictions can be leveraged for
user-friendly conversational interfaces,
which allow more genuine human - robot
interaction.
• The effect is a better understanding of
customers, and a more personalized way
of addressing their needs.

7. Responding to customer complaints and inquiries
• Customers’ statements of discontent, on
top of the inherent call to be addressed
that any kind of criticism poses, also bring
about the challenge of dealing with
unstructured data.
• Optical character recognition (OCR) can be
used to scan customer letters, emails, or
web forms and forward the structured
input to software robots.
• They can do a data check and, if necessary,
add additional relevant details. The
outcome, i.e. a recommended customer
response, is automatically delivered to the
case management system.
• An employee can then refine the content
and ultimately send it to the customer. .

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