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April 2017
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.
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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| Recoding the Customer Experience
3Recoding the Customer Experience |
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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
based on cognitive computing.2
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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4 | Recoding the Customer Experience
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
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 using pattern recognition and image processing
• Disease identification and risk stratification
• Proactive health management
• Healthcare provider sentiment analysis
• Wearables/ personal medical devices
• Smartphones
• Biometric sensors
Financial Services & Insurance
• 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
Retail & Consumer Goods
• Personalized suggestions, offers, and alerts
• Recommendation engines
• Real-time knowledge of customer’s context (location, preferences, etc.)
• Customer segmentation by analyzing usage patterns
• Cross-selling and up-selling
• Predicting customer ROI and lifetime value
• Predictive inventory planning
• Smartphones
• Wearables
• Location sensors, RFID
• Robots with sensors
• Specialized devices
• Cameras
Energy & Utilities
• Power usage analytics using real-time data
• Seismic data processing
• Dynamic tariff generation
• Smart grid management
• Demand and supply prediction
• Energy, water, gas meters
• Sensors
Transportation • Real-time vehicle tracking and optimization for logistics and public transport systems
• Asset management and tracking
• Autonomous cars
• On-board vehicle gateway devices
• RFID tags
• Sensors
Figure 1
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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.
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.
Artificial Intelligence for Enterprise Applications
SALESFORCE EINSTEIN MICROSOFT AZURE MACHINE LEARNING
• The latest addition to Salesforce’s Customer 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.
• Microsoft’s Cortana Intelligence will 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
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.
RiminderThis 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.
TamrUses ML in traditional enterprise areas, such as procurement, for predictive procurement and media analytics.
DarktraceEmploys ML to monitor network traffic for anomalies/attacks/suspicious activities, and better respond to potential cyber threats.
Figure 3
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6 | Recoding the Customer Experience
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:
• 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.
• In most sales activities, lead qualification 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 lead-qualification 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.
Figure 4
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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 Churn & Predictable Buying Behaviors
ML uses data from previous customers to target others that are most likely to generate churn. ML-based customer segmentation may thus be used to target 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.
7Recoding the Customer Experience |
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.
8 | Recoding the Customer Experience
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9Recoding the Customer Experience |
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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, high-
quality 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
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.
Predict Customer Behavior & Satisfaction in Real Time
• Predicts customer purchase patterns and positively impacts satisfaction indices in
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.
Empower Customers through Self- Service
• Using ML search algorithms, most relevant content can be made available at the
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.
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
A Chatbot with Natural Language Processing for Corporate Banking Customers
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,
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.
10 | Recoding the Customer Experience
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11Recoding the Customer Experience |
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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.
Vyoma MurariSenior Marketing Consultant
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.
She can be reached at Vyoma.Murari@cognizant.com | linkedin.
com/in/vyoma-murari-94a5a846.
ABOUT THE AUTHOR
FOOTNOTES
1 http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/2 http://www.businesswire.com/news/home/20141211005981/en/IDC-Reveals-Worldwide-Big-Data-Analytics-Predictions
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.
ABOUT ENTERPRISE APPLICATIONS SERVICES - CUSTOMER EXPERIENCE MANAGEMENT
Cognizant’s Customer Experience Practice is a global industry leader that offers end-to-end consulting and implementation across customer -experience and CRM digital transformation technologies spanning sales, services, and marketing functions. Cognizant has large teams of Salesforce, Microsoft Dynamics CRM, Adobe, Pegasystems and Oracle Cloud professionals that cater to a wide range of customer requirements across different industries. For more details, please visit www.cognizant.com/customer-relationship-management.
ABOUT COGNIZANT
Cognizant (NASDAQ-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 230 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant.
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