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

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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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HOW NEXT-GENERATION TECHNOLOGY INFLUENCES CUSTOMER EXPERIENCES

Artificial intelligence is already supporting enterprises in areas such as deep learning, natural

language processing (NLP) 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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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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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.

Recoding 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.

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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.

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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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