As payments providers around the globe cope with increasing pressure
on revenues and margins, customer service is increasingly becoming an
important asset for driving top- and bottom-line performance, and improving
the customer experience. While most banks, card companies, and other
payments providers have implemented various degrees of customer service
transformation by using advanced analytics, the discipline has yet to be
fully leveraged in this regard. To realize the full potential of today’s analytical
capabilities, financial institutions will need to possess, acquire, or develop the
relevant capabilities and use them to customize and enhance a wide range of
customer interactions.
Payments providers that adopt advanced
analytics to develop broad integrated approaches
are seeing significant improvement:
customer satisfaction scores rose 5 to 10
percent and operating costs declined 15 to 20
percent when they used analytics to eliminate
cross-channel leakage and migrate more
customer interactions into self-serve channels.
Analytics also enabled these firms to improve
customer retention and revenues by 10
percent or more, by enhancing the customer
journey and improving cross-selling.
The future of customer service
Customer service is shifting dramatically,
from phone and branch-centric models to an
omnichannel interaction dynamic in which
customers move seamlessly among service
channels, including mobile, phone, chat, and
online. A McKinsey survey in 2015 showed
digital channels accounted for 30 percent of
customer interactions. We expect this share
will approach 50 percent by 2020. And of this,
26 percent will be exclusively digital with no
branch interaction.
Payments customers expect high-quality
service across channels, similar to what
they enjoy at other financial institutions
and leading service providers, like Amazon
and Zappos. To deliver this level of service,
payments firms need to optimize customer
and prospect telecommunications and deliver
seamless omnichannel interactions.
Building an omnichannel customer service model
Traditionally, financial institutions have
tried to optimize customer service within
channel silos, including call centers, online,
and mobile. The key to delivering a high-quality
omnichannel experience is adopting
a broad customer journey approach that
integrates customer interactions across
digital and traditional channels. Several
institutions have already embarked on such
a model. A global life insurer, for example,
recently developed a five-year plan to migrate
nearly half of its customer journeys into
self-serve channels. However, too often
such changes are viewed as one-time efforts
rather than as a large-scale transformation.
Designing a comprehensive, ongoing program
is key to sustaining omnichannel service
improvements.
Investing in the talent to transform
A key part of transforming the customer experience
is migrating basic transactions to
self-service channels, and complex transactions
to agent-assisted channels. While most
organizations invest in ongoing agent training
and capability building, transforming the
customer experience demands a more substantial
investment in talent. It requires investing
in technology that enables customer
service professionals to have more effective
interactions with customers. For example:
Real-time coaching software, such as
Cogito, provides live feedback about customers
to agents during customer calls,
so agents can tailor the discussion to customer
needs.
Applications such as Verint use speech
analytics that foster more personalized interactions
with customers.
To provide more personalized customer service ,
financial institutions must rethink how
they interact with customers and prospects.
Analytics can personalize customer experience
by, for example, identifying the next-best
action or product offering. (See “Using
data to unlock the potential of an SME and
mid-corporate franchise .”)
Investments in technology are, of course,
critical to transforming the customer experience.
Two investment types in particular are
key: developing the agility to rapidly build,
pilot, and launch a broad transformation; and
robotics or artificial intelligence (AI) to reduce
manual workloads, improve cycle times,
and minimize back-office errors. McKinsey
research shows that 65 percent of back-office
tasks at contact centers, and 30 to 50 percent
of front-line calls, can now be automated.
Six hallmarks of analytics success
Financial institutions that are successfully
using advanced analytics to enhance the
customer experience share six common hallmarks
(Exhibit 1).
1. Migrating customers to digital channels
Given customers’ preference for omnichannel
service, there are two important
questions financial institutions must address:
First, how do they create seamless
transactions for digital natives, who prefer
digital-only service? Second, in serving less
digitally inclined customers, how can financial
institutions use tools like journey analytics
to prevent the use of multiple channels for
the same query? The main challenge for customer
service organizations is to identify the
most appropriate transactions for migration
and ensuring they are completed satisfactorily
in digital channels whenever possible.
Payments leaders in digital migration are
achieving 20 to 30 percent reductions in call
volume and successfully enhancing the customer
experience. Some industry leaders are
also developing a 360-degree, multitouch,
multichannel view of customer interactions
using journey analytics; but this requires
robust integrated datasets that can capture
customer interactions across channels.
2. Improving behavioral routing and IVR containment
Financial institutions have been using interactive
voice response (IVR) technology for
several decades, but few have optimized these
capabilities. Doing so requires more than investing
in additional VR capabilities. Financial
institutions can apply advanced analytics
or AI-based technologies to improve behavioral
routing and IVR containment:
Using analytics to identify reasons for call
transfers can help increase the number
of interactions contained within the IVR
environment. Deeper analysis of calls can
classify customers into clusters based on
value, behavior, and tenure, speeding up
IVR service and streamlining unnecessary
trees.
Matching agents to callers based on personality
(using technologies like Afiniti and
Mattersight) can meaningfully improve
customer experience and call efficiency.
Directing calls from high-potential customers
to agents trained to present tailored
products (using algorithms based on the
customer’s needs) can boost productivity.
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3. Strengthen identity validation and personalize product offerings
The layering of analytics on video and audio channels can improve identity validation and
personalize the product offering. Examples include:
Replicating face-to-face interactions in a
remote environment using optimization
software enables more personalized and
secure interaction.
Identity validation can be simplified and
improved with features like facial recognition
(online identification) and voice recognition
(in app account access).
4. Optimize the workforce management model
Most financial institutions have established
internal analytics centers staffed with experts
working to capture workforce optimization
opportunities. Yet, most workforce
management practices are rooted in backward-looking general demand–supply matching,
assuming some average service level for a
day. However, customer research reveals that
assumptions of averages fall short. There are
three important challenges for each financial
institution:
How can they effectively manage the tails
that drive customer satisfaction or dissatisfaction?
How can they use machine learning to
manage resiliency and drive the next level
of predictive modeling on demand (e.g.,
impact of hurricanes)?
How can analytics centers use real-time
simulation tools to create efficiencies in
workforce management?
5. Automate to improve employee efficiency and engagement
Thus far, automation has not been systematically
applied in the customer service environment.
In customer care, AI can be used to
automate services by supporting customers
with virtual agents, and contact center agents
through real-time interaction tools (e.g., automated
knowledge management systems) and
back-end automation (e.g., robotic process
automation). Virtual agents can solve customer
requests by using natural language processing
technology, and get smarter over time through
machine learning. For example, programs
like IPSoft’s Amelia can play the role of any
customer service agent by rapidly absorbing
call logs, recognizing emotional context, and
interacting with customers, thereby saving
costs and lifting both revenue and customer experience.
With large tech players moving into
the digital assistant arena, we expect things to
evolve quickly in this area.
Mastering the digital advantage in transforming customer experience
6. Optimize frontline performance through analytics in recruiting
Recruiting processes for customer service
organizations are seldom informed by what
makes agents successful. Leading firms take
an approach called people analytics methodology,
which reverse engineers the process,
starting with the best customer service agents
and identifying common traits that makes
them successful. They then apply these insights
at the top of the recruiting funnel in
selecting candidates. By applying people analytics
in this way, financial institutions can
improve talent management in customer experience
as well as in the wider organization.
Case example I: Improving digital channel experience and digital adoption
Recently, a North American bank used journey
analytics to accelerate digital adoption
across its customer base. Using analytics and
design thinking to address digital adoption
levers across customer journeys (rapid digitization,
containment, signature moments,
customer targeting), the bank achieved a gain of more than 20 percentage points in digital
engagement. The initiative included the following
elements:
Journey level scan: Using interaction data
and analytics from all channels (digital,
call, branch, email/text, ATM), the bank
prioritized about 15 core customer journeys
and more than 40 sub-journeys for
digitization.
Quantified journey redesign: The bank
then redesigned each core journey using
analytics-based Quantified Experience
Design (QED),
Real-time customer nudging: The bank
introduced a customer targeting process
based on customer behaviors and journeys
to accelerate digital adoption, which generated
a 5- to 10-percentage-point increase
in product adoption.
Journey tracking: The bank transitioned
from an overall customer experience-based performance measurement
system to one based on operating drivers
for each journey and channel, to track improvements
and re-orient program.
Capability building: Using journey analytics
and QED, the bank designed and
launched a capability-building program for
more than 800 contact center agents.
Case example II: Enhanced contact management
A credit card company was struggling to migrate
customers to its self-serve channels
despite having invested in natural-language
speech IVR. Consequently, it devised a three-pronged
approach to accelerate migration,
which focused on resolving (and containing)
a higher percentage of calls within their IVR,
and delivered a differentiated experience
along the customer journey:
To better understand its customers’ behavior,
the company analyzed five million
customer calls. With these findings, they
classified customers into eight archetypes
based their value, behavior, and length of
time as customers.
Management also used brainstorming
techniques to develop and refine several
initiatives based on feasibility, potential
economic impact, and customer experience
improvement. This generated 48 prioritized
initiatives that spanned VR (e.g.,
capture additional information and make
it less easy for customers to “rep out”),
routing (e.g., adapt service standards to
match expectations of different customers),
and post-VR (e.g., focus on education
and self-service awareness for disengaged
customers).
The company also surveyed 1,500 employees,
conducted focus groups that engaged
managers, and surveyed more than 1,000
customers to explore tactics for increasing
IVR containment and digital engagement.
Through these efforts, the credit card provider
identified 200 to 500 bps in potential
improvement in the containment rate (Exhibit
2). The VR enhancements and post-VR
agent initiatives also led to a 5 to 10 percent
reduction in costs or incremental annualized
savings.
Case example III: Demand forecasting
The call center head of a large UK-based bank turned to analytics to optimize agent utilization
by automating demand forecasting, as
part of a larger analytics-driven transformation
at the institution. The approach incorporated
the following elements:
creation of a robust integrated dataset
that is foundational for the analytics exercise,
by combining five different data
sources—data for more than ten million
customers, call data, agent data, bank data
related to IT outages, and other external
data (e.g., weather)
development of two sets of random forest
machine learning models to continuously
learn thresholds and forecast both
number of calls and average handling time,
on a monthly basis (4 to 16 months ahead
and updated monthly) and a 30-minute
level basis (8 to 10 weeks ahead and updated
daily)
Bayesian techniques to capture most recent
dynamics for extrapolation, non-linear
regression models for forecasting, and
more than 100 features to capture
different levels of seasonality
The bank achieved a 20 to 40 percent error
reduction in forecasting for a subset of population
and are rolling it out across all FTEs.
Starting the journey on analytics to customer service
When introducing advanced analytics, a critical
first step is clearly understanding the organization’s
current position in terms of one
of three horizons (Exhibit 3):
Those on Horizon 1 generally have low levels
of awareness regarding recent developments
in advanced analytics for customer service.
These organizations need to begin their
transformation by building a business case,
educating their leadership, and obtaining
organizational buy-in. Once these initiatives
are underway, quick, tangible wins should
be pursued to reinforce the organization’s
commitment to a full transformation. Additionally,
another challenge faced by these
organizations is lack of in-house knowledge on relevant frameworks and solutions, to diagnose
and prioritize initiatives.
Enterprises at Horizon 2 have a better understanding
of recent advances in the field,
and have started to experiment with or
adopt them. However, they have done so
largely on an ad hoc, unstructured basis. Unfortunately,
informal approaches are likely
to leave significant value on the table. The
key challenge for Horizon 2 organizations
is to identify the most efficient path for delivering
the desired results. This might be
accomplished, for instance, by shaping their
perspectives through a sharing of external
best practices, and then setting challenging
timelines.
Horizon 3 firms are well ahead of the curve,
applying next-generation analytics solutions
to transform the customer service model. At
this stage, the key challenge is finding ways
to advance to even higher levels, and to continue
to invest in next-generation solutions.
The use of new analytical tools and capabilities
are transforming customer service in financial
services. The following questions can
help firms shape their strategy discussions:
Where do we stand currently in terms of
the three advanced analytics/customer
service horizons?
What challenges are preventing us from
advancing to the next horizon?
What immediate steps can we take to address
these challenges?
About the author(s)
Krishna Bhattacharya is a partner, Greg Phalin is a senior partner, and Abhilash Sridharan is a consultant, all in McKinsey’s New York office.