Download Recoding the Customer Experience

DIGITAL BUSINESS
Recoding the Customer
Experience
By embedding artificial Intelligence and the Internet of
Things into their enterprise applications, consumer-facing
organizations can cultivate lasting and profitable customer
relationships with hyper-personalized offers and services
that deliver on the promise of digital.
April 2017
Digital Business
EXECUTIVE SUMMARY
In the age of digital consumerism, customers are “always on” – active on the Web, e-mail,
social media, mobile devices, and tablets. To keep up with this trend and compete effectively
in an increasingly connected marketplace, brands must continually fine-tune their customer
strategies. This requires investing in technology platforms and applications that anticipate
customers’ needs and understand their behaviors early in the buying cycle. By taking a
proactive stance, companies can spare customers from having to start over when
researching or purchasing a product or service.
This white paper outlines three disruptive technology trends and their applications –
artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT) – that are
gaining momentum across enterprise environments. We will discuss how AI and ML
strategies are enabling leading businesses to gain customer mindshare, and reshaping the
customer experience as we know it with informed, hyper-personalized information and
services across touchpoints. Last, we highlight how we are helping companies apply these
advancements to retool the customer experience and achieve long-term, profitable growth.
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Digital Business
HOW NEXT-GENERATION TECHNOLOGY
INFLUENCES CUSTOMER EXPERIENCES
Artificial intelligence is already supporting enterprises in areas such as deep learning, natural
language processing (NPL) and neural networks. Machine intelligence, a subset of artificial intelligence,
equips computers with self-learning capabilities without the need for explicit programming when
exposed to new data. According to Gartner research, AI, ML and the IoT are among the top technologies
that will be applied to transform the customer experience across channels, devices, and touchpoints.1
Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook
prompts friends to be tagged in photos, how Alexa, Siri and Cortana assist us in answering questions
or going about our day-to-day activities? Or how Amazon and Airbnb make personalized product or
lodging recommendations? These are all real-life examples of machine learning in action.
Whether we are searching the Web, driving a car, purchasing a product, automating a task, ordering
dinner from a robot at a restaurant, or using speech recognition on our smartphones – we are
benefiting from machine learning. ML uses a customer’s historic data and behavioral patterns to
create high-quality predictive intelligence concerning their future behavior. IDC reports that
applications with advanced predictive analytics will grow 65% faster than those that do not have this
functionality “built in.” In fact, IDC predicts that by 2018, most consumers will interact with services
2
based on cognitive computing.
The IoT refers to everyday “things”
equipped with sensors that generate
enormous amounts of data based on use
and environmental conditions.
Similarly, the Internet of Things (IoT) is disrupting many industrial business processes. The IoT refers
to everyday “things” equipped with sensors that generate enormous amounts of data based on use
and environmental conditions. Enterprises across industries are deploying next-generation business
models around the convergence of two or more disruptions, such as artificial and machine intelligence
and the Internet of Things, to segment and analyze the billions of bits of data they generate to
determine what is meaningful. (See Figure 1).
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Next-Generation Business Models
INDUSTRY
MACHINE LEARNING APPLICATION
IoT DEVICES/SENSORS
Healthcare
• Remote patient monitoring/doctor consultation
• Alerts and diagnostics from real-time patient data
• Wearables/ personal
Financial
Services &
Insurance
•
•
•
•
•
•
•
•
•
Retail &
Consumer
Goods
Energy &
Utilities
Transportation
medical devices
using pattern recognition and image processing
Disease identification and risk stratification
Proactive health management
Healthcare provider sentiment analysis
• Smartphones
• Biometric sensors
Risk analytics and regulation
Customer segmentation
Predictive personalized communications/offers
Cross-selling and up-selling
Credit worthiness assessment and risk prevention
(automated assessments)
Insurance management (e.g., Japanese companies
using AI to calculate insurance payouts).
• Mobile phones
• Wearables
• Sensors
• Personalized suggestions, offers, and alerts
• Smartphones
Recommendation
engines
•
• Wearables
Real-time
knowledge
of
customer’s
context
(location,
•
• Location sensors,
•
•
•
•
•
•
•
•
•
•
•
•
preferences, etc.)
Customer segmentation by analyzing usage patterns
Cross-selling and up-selling
Predicting customer ROI and lifetime value
Predictive inventory planning
•
•
•
RFID
Robots with sensors
Specialized devices
Cameras
Power usage analytics using real-time data
Seismic data processing
Dynamic tariff generation
Smart grid management
Demand and supply prediction
• Energy, water, gas
Real-time vehicle tracking and optimization for
logistics and public transport systems
Asset management and tracking
Autonomous cars
• On-board vehicle
•
meters
Sensors
gateway devices
• RFID tags
• Sensors
Figure 1
ARTIFICIAL INTELLIGENCE
Cloud-based applications for artificial intelligence have already staked their claim. Salesforce
(Einstein), Microsoft (Azure), SAP (HANA), Adobe and Oracle use machine learning as a core
component of their offerings, along with natural language processing, deep learning, predictive
analytics and smart data discovery, to help their clients transform CRM.
Tech giants such as Apple, Amazon, Google, IBM, and Facebook are on an acquisition mission to beef
up their AI and ML capabilities. For instance, Google has upgraded its image search and recognition
capabilities to identify individuals or objects in photos on the Web. Meanwhile, Apple is investing
heavily in artificial intelligence in areas such as self-driving autonomous vehicles, deep learning,
image recognition and processing, and voice control. It is also enhancing its mapping technology with
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a full-featured AI navigation system. It may not be long before Siri and other iOS apps find a place in
autonomous vehicles.
Figure 2 highlights the latest developments available to forward-thinking businesses.
Artificial Intelligence for Enterprise Applications
SALESFORCE EINSTEIN
MICROSOFT AZURE MACHINE LEARNING
• The latest addition to Salesforce’s Customer
• Microsoft’s Cortana Intelligence will
•
Success Cloud Platform, powered by machine
learning.
Leverages data from different sources to
transform CRM.
»» Sales: Predictive lead scoring, opportunity
insights, and automated sales activities.
»» Services: Predictive routing of cases.
»» Marketing: Predictive customer
segmentation; analysis of website visits; social
media posts; e-mail; personalized product
recommendations.
•
soon add powerful AI and ML
capabilities to MS Office productivity
applications and Microsoft Dynamics.
The cloud-based software enables
natural and contextual interaction
using machine intelligence and AI
algorithms for vision, speech,
language, and knowledge.
Figure 2
The Rise of AI & ML Startups
As shown in Figure 3 below, numerous startups are using artificial intelligence and machine learning
to transform the customer experience in a variety of ways.
Future-Forward Innovators
Real Life Analytics
Sends targeted advertising using plug-and-play
AI dongles on digital screens in real time at
shopping centers and subway stations.
Real-time facial recognition customizes
content according to viewers’ levels of
engagement and demographics.
Riminder
This service is disrupting the HR industry and
the job-seeking process by applying deep
learning to internal and external data to help
talent managers and recruiters attract relevant
talent and pinpoint top potential candidates.
FinTecs Ltd.
This fraud-detection and surveillance company
is developing new business models for lending,
fraud detection and robo advisers using AI/ML.
DataRobot
Automates data science processes by
integrating with ML algorithms (R, Python,
Spark, H2O) for predictive decision making.
Tamr
Uses ML in traditional enterprise areas, such as
procurement, for predictive procurement and
media analytics.
Darktrace
Employs ML to monitor network traffic for
anomalies/attacks/suspicious activities, and
better respond to potential cyber threats.
Figure 3
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HOW MACHINE LEARNING IS REFURBISHING
THE CUSTOMER EXPERIENCE
Machine learning is already delivering consistent, gratifying customer experiences across digital
channels in key areas such as sales, marketing, and customer service.
Sales
Sales people are constantly on the move, and rely on their mobile devices to stay connected to their
company and their customers. Given the enormous amount of data (structured, unstructured, and
semi-structured) generated by various systems across different channels (social media, e-mail and sales
CRM tools, for example) sales leaders are challenged to qualify leads and identify the right opportunities
to engage and win. So how does machine learning help improve sales productivity and efficiency?
Figure 4 offers some insight.
Improving Sales Productivity
Enhance Sales Operations
Improve Lead Qualification
Machine learning offers powerful capabilities in
predictive analytics – enabling enterprises to
analyze data from every point in the sales process
(apps, e-mail, CRM systems and social media) and
develop actionable recommendations such as:
• In most sales activities, lead qualification
• Improving sales forecasting (predicting credit
•
risk, customer churn, win/loss rate, etc.).
• Automating account management and lead•
•
•
identification activities, such as closing deals
and enhancing sales operations.
Pin-pointing missed revenue opportunities.
Applying ML algorithms to the sales pipeline to
increase win rates and further improve sales
productivity.
Uncovering new up-sell and cross-sell
opportunities.
Figure 4
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Recoding the Customer Experience
is a major area that can drive up
customer acquisition costs. Sales teams
often spend a lot of time and effort
contacting the wrong prospects.
With natural language processing,
companies can add real-time virtual
assistance to their initial leadqualification efforts and correctly
classify leads as interested, not
interested, or needing nurturing. This
frees sales teams to focus on the most
promising opportunities while lowering
acquisition costs.
Digital Business
Marketing
Marketing organizations’ key role is to establish, sustain and extend customer relationships. As more
customer information becomes available through big data, machine learning will become an essential
element of customer-focused marketing campaigns. Among the top challenges marketers face include
lead generation, ROI measurement, and generating personalized offers/messages in real time by
utilizing customers’ personal data, demographics, historical purchase patterns, and social sentiments,
for example. (See Figure 5).
Applying Machine Learning to Marketing
Customer
Segmentation
ML algorithms analyze customer behaviors in real time and use personas
(fictional groups that reflect the buyers associated with a company’s marketing
and sales efforts) to create effective, highly personalized interactions.
Personalized
Recommendations
Leading e-commerce companies such as Netflix and Amazon use ML
algorithms to recommend TV programs, movies, and other products based on
individual customers’ preferences.
Optimized User
Channels
Marketing organizations can use ML techniques to determine where and how to
reach customer segments and tailor ads and marketing communications
accordingly.
Reduced Customer
ML uses data from previous customers to target others that are most likely to
Churn & Predictable generate churn. ML-based customer segmentation may thus be used to target
Buying Behaviors
customers for retention offers.
Figure 5
As more customer information becomes
available through big data, machine learning
will become an essential element of
customer-focused marketing campaigns.
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QUICK TAKE
Practicing the “Art of the Possible”
Today’s digital frontrunners are using artificial and machine intelligence to predict customer behaviors and revamp
customers’ experiences. For example:
• Facebook uses machine learning to post relevant content and ads based on the things you liked, groups you joined
and pages you follow. Have you ever wondered why only certain people appear in your News Feeds? ML algorithms
run on your News Feeds and analyze the types of content you prefer.
• Google uses a machine-learning artificial intelligence system, “RankBrain” to help process, organize, and refine its
search results.
• Amazon sends periodic e-mails when a product you searched for is available at a lower price, as well as product
recommendations that may be of interest to you. All of this is the result of machine learning.
• TripAdvisor uses ML through different phases of the customer experience to track member habits, build segments
based on behavioral patterns, and push suggestions/offers at the most likely time of purchase. The company
follows up with an e-mail or ad prompting members to complete the booking process.
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Customer Service
Regarding customer service, we see human-assisted virtual agents like chatbots taking over traditional
call center IVRs to route customers to the right agent or queue and improve the overall quality of
customer service. AI technologies such as natural language processing and speech recognition support
contact center agents during live customer-service interactions by looking up relevant information and
suggesting how best to respond. Another AI technology, conversational voice interfaces such as
Amazon’s Alexa and Apple’s Siri, provides the ability to conduct a natural conversation with the user and
suggest the next best action. Additionally, intelligent/predictive ML algorithms and AI analytics are being
integrated with high-volume customer data and transactions enhance enterprise areas such as the call
center, technical support, interactive troubleshooting, self-help, and interactive sales and customer
activities to serve customers better.
One of the biggest service-related challenges that enterprises face today is to maintain consistent, highquality customer service across channels and devices. Figure 6 highlights how ML can make this happen.
How Machine Learning Applications Enhance Customer Service
Automate
Routine
Tasks
• Uses virtual assistants, or chatbots, to automate routine tasks that would
•
otherwise require a live agent to reset passwords, address account issues, and
provide sales support.
Frees the live agent to focus on handling more complex and revenue-generating
tasks, which helps to improve top and bottom-line performance.
Improve
Ticket
Routing &
Response
Times
• Employs ML to classify and route incoming tickets to the agent team queue with
Predict
Customer
Behavior &
Satisfaction
in Real Time
• Predicts customer purchase patterns and positively impacts satisfaction indices in
Empower
Customers
through
SelfService
• Using ML search algorithms, most relevant content can be made available at the
the best capabilities to respond to the customer’s questions/issues.
• Uses natural language processing to clearly understand what the customer is
saying and route the call to the appropriate agent. This reduces call durations and
increases the likelihood of first-call resolutions.
•
•
real time across service channels, such as phone, chat, e-mail, IVR or social media.
Enables agents to look at the CSAT meter and fine-tune the conversation instantly,
or escalate to another team.
customer’s fingertips across different channels, enhancing self-service and
improving omnichannel experiences.
Reduces the number of calls made to the IVR or any other channel. By integrating
this capability with predictive ML analytics, companies can uncover new insights
into customer behavior and send out targeted. personalized offers through
various channels.
Figure 6
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QUICK TAKE
Today, we are helping enterprises in various industries apply machine
learning and the Internet of Things to dramatically improve the
customer experience.
A Chatbot with Natural Language Processing
for Corporate Banking Customers
We developed a proof of concept that demonstrates how
corporate banking customers can ask a question and
receive an answer via the bank’s instant messenger app
connected to a chatbot. A customer can also query an
AI-equipped conversational voice interface such as Alexa
or Siri to access their account summary, transaction
history, or payments.
The bot processes the query by passing it through natural
language processing (NLP) and AI engines, then suggests
an intelligent response (the next best action, self-help).
In some cases, the bot hands the question over to a
human service rep. Customers can close transactions
faster, and agents can work more efficiently.
QUICK TAKE
Informing Next-Gen Shopping Experiences
By capitalizing on machine learning, the Internet of
Things, and beacons (small devices that broadcast signals
to nearby smart devices), consumer-facing companies
can create offers that expose customers to next-gen
shopping experiences.
We created a proof of concept that recommends
“next-best” offers using ML algorithms on large data sets
(consumer spend habits, historical purchasing behavior,
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Recoding the Customer Experience
demographics, and social footprint). For example, beacons
can sense/recognize a customer in a store through the
customer’s smartphone and pass on a message to the ML
algorithm, which then pushes contextual information –
personalized store offers, notifications, location details,
and a call to action – to the shopper’s device. This drives
higher footfalls and enhances the customer’s experience
at every touchpoint – both physical and digital.
Digital Business
LOOKING AHEAD: NEXT STEPS
Today’s enterprise systems generate enormous
volumes of data that can be fed into AI, ML, and IoT
technologies to analyze meaningful trends and
generate actionable insights. C-level decision
makers must understand the important role of this
treasure trove of enterprise and customer data in
building and maintaining stronger customer
relationships, providing hyper-personalized offers,
and increasing client engagements.
So how should organizations embark on this
journey? A good way to start is to look deep into
business functions, operations, and processes,
and evaluate where these emerging technologies
can be best applied. For example:
• Typical
enterprise functions include
repetitive business processes that require a
lot of manual intervention – often leading to
mistakes in order fulfillment, inventory
management, shipping, purchasing, and
billing. When automated, these tasks are
predictable and manageable – freeing human
resources to focus on more critical tasks.
• IT back office systems/night time data
center operations and batch processing are
good candidates for intelligent automation,
which can reduce reliance on IT operations
staff.
• Customer service functions for inquiries or
technical support can be automated with
virtual assistants (bots) to encourage
customer self-help.
• Business processes can be further enhanced
with machine learning algorithms to predict
employee/customer churn, track equipment
conditions, and resolve tickets faster by
intelligently routing to the right agent.
Machine learning remains a work in progress. As
solutions mature, their impact will be felt in more
profound ways across the enterprise. The time is
now for companies to weave artificial intelligence
and machine learning into their strategic agendas
to enrich the customer experience, streamline
processes, drive profitable business growth, and
transform the way they operate and serve
customers.
Note: All company names, trade names, trademarks, trade dress, designs/logos, copyrights, images and products
referenced in this white paper are the property of their respective owners. No company referenced in this white
paper sponsored this white paper or the contents thereof.
FOOTNOTES
1
2
http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/
http://www.businesswire.com/news/home/20141211005981/en/IDC-Reveals-Worldwide-Big-Data-Analytics-Predictions
ABOUT THE AUTHOR
Vyoma Murari is a Senior Marketing Consultant with Cognizant
Enterprise Applications Services’ Customer Experience
Management Practice. She has eight years of experience
in marketing, branding, market and customer intelligence,
analyst relations and business analysis across CRM, customer
experience and digital platforms. She holds an MBA from
Symbiosis International University, Pune. Vyoma is a certified
Adobe Campaign Business Practitioner and Salesforce Business
Administrator.
Vyoma Murari
Senior Marketing Consultant
She can be reached at [email protected] | linkedin.
com/in/vyoma-murari-94a5a846.
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ABOUT ENTERPRISE APPLICATIONS SERVICES - CUSTOMER EXPERIENCE MANAGEMENT
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