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DIGITAL BUSINESS Recoding the Customer Experience By embedding artificial Intelligence and the Internet of Things into

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


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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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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Next-Generation Business Models





Remote patient monitoring/doctor consultation

Wearables/ personal medical devices


Alerts and diagnostics from real-time patient data using pattern recognition and image processing

Disease identification and risk stratification

Biometric sensors

Proactive health management

Healthcare provider sentiment analysis


Risk analytics and regulation

Mobile phones

Services &


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).

Retail &

Personalized suggestions, offers, and alerts




Recommendation engines


Real-time knowledge of customer’s context (location, preferences, etc.)

Location sensors, RFID

Customer segmentation by analyzing usage patterns

Robots with sensors

Cross-selling and up-selling

Specialized devices

Predicting customer ROI and lifetime value


Predictive inventory planning

Energy &

Power usage analytics using real-time data

Energy, water, gas meters


Seismic data processing

Dynamic tariff generation


Smart grid management

Demand and supply prediction


Real-time vehicle tracking and optimization for logistics and public transport systems

On-board vehicle

gateway devices

Asset management and tracking

RFID tags

Autonomous cars


Figure 1


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



The latest addition to Salesforce’s Customer Success Cloud Platform, powered by machine learning.

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.

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.

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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Machine learning is already delivering consistent, gratifying customer experiences across digital channels in key areas such as sales, marketing, and customer service.


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 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.

Improving sales forecasting (predicting credit risk, customer churn, win/loss rate, etc.).

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.

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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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



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.


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

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


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


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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.

up with an e-mail or ad prompting members to complete the booking process. 8 | Recoding

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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


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.


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.


Routing &



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.


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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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.

transactions faster, and agents can work more efficiently. QUICK TAKE Informing Next-Gen Shopping Experiences By


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,





Recoding the Customer Experience

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.


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.

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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.





ABOUT THE AUTHOR Vyoma Murari Senior Marketing Consultant Vyoma Murari is a

Vyoma Murari

Senior 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 | linkedin.



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



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