customer engagement analytics encompasses these highly intelligent capabilities.docx

8/10/2019 Customer Engagement Analytics encompasses these highly intelligent capabilities.docx

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Customer Engagement Analytics encompasses these highly intelligent capabilities:

Customer journey mapping: Provides a clear understanding of how customers are interacting

with your company

Interaction analytics: Captures and analyzes every interaction automatically, across all

interaction channels

Contact reasoning: Uses the customer interaction journey to automatically assign a reason to

each customer contact

Repeat contact sequencing: Provides an accurate understanding of which customers—whether

individual or in aggregate—contact your company more than once, and what’s driving their calls,

emails or texts

Predictive analytics: Gives an uncannily accurate, advance picture of how customers will behave

so you can work to make future contacts unnecessary

Customer Engagement Analytics

Understand the Full Customer Journey

Customers’ interactions with their service providers are a journey—an increasingly complex one at

that. This journey is shaped with every outreach via the web, email or online chat, social media

and phone, and every billing statement, payment and purchase. Despite the complexity,

customers expect providers to know their journey’s every turn. But with thousands of customers

generating millions of contacts every day, following the customer journey would require a very

sophisticated roadmap.

Customer Engagement Analytics powered by Big Data technology provides smart analytics

platform and maps the cross-channel customer journey, capturing and analyzing what was said,

written and done each step of the way, for every customer. Customer journey can be mapped,and

bottlenecks, blind alleys and dead-ends can be re-routed.

Customer Journey Mapping

Combining data from transactions, customer interactions, agents’ desktop activity, and customer

feedback, analytics generates a complete view of the customer journey, both for individual

customers and customers at large. We can follow the journey of a customer who, for example,

receives his bill, then logs into his web account, emails the company, and finally calls the contact

center. We can also see how many other customers’ journeys are the same or similar, which can

be useful in identifying broken processes.



Innovative Speech Technologies to Unveil Hidden Insights

Speech analytics is a key required capability in customer engagement. It helps identify the reasons

why customers call the company and what causes customer dissatisfaction. It also helps contact

centers uncover training opportunities to improve agent performance. Speech analytics, can help

companies gain deep insights from customer interactions that can be used to increase customer

satisfaction and loyalty, and improve operational efficiency.

Speech analytics can be used in:

Real-time speech analytics

A cutting edge technology that analyzes spoken interactions in real-time as they occur. It enables

real-time agent guidance for next-best-action, as well as supervisor alerts for call intervention.

Phonetic indexing

This powerful speech analytics technology breaks down speech into phonemes, the smallest units

of language, and creates an indexed voice database. Since phonetic indexing is a fast and highly

scalable technology, it can analyze 100% of call recordings to understand why customers are

calling. It also enables free text search for specific words or phrases.

Speech-to-text transcription

Transcription of calls from spoken to written words is a foundation of speech analytics. It enables

text and data mining models to uncover root causes and hidden insights in frequently mentioned

topics as well as the context in which these topics were mentioned.

Speaker separation

It’s not enough to know what was said during calls, but to know who—customer or agent—said it.

Speech analytics leverages speaker separation to provide valuable context for the content of

customer interactions.

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Emotion detection

Certain words spoken during an interaction may indicate high levels of emotion. However,

sometimes emotion can only be detected by analyzing the voice and tone of the speaker. Emotion

detection is a speech analytics technology that analyzes the voice of the speaker and identifies

emotion via slight variations of pitch or tone. High levels of emotion are a reliable gauge of both

customer dissatisfaction and agent skills.

Talk-over analysis

This useful speech analytics capability identifies portions of calls in which the customer and agent

are talking simultaneously—a common indicator of customer dissatisfaction. In addition, talk-over

analysis identifies periods of silence during calls that may be related to agent knowledge gaps.



Text Analytics

The Power to Read Between the Lines

Text analytics, a key technology, allows organizations to derive high-quality insights from

customer interactions to better understand why customers contact the company and correct root-

cause issues. It captures and analyzes speech and text interactions, and the vocal and emotional

cues within them. Its innovative speech technologies—speaker separation even on mono

recording, emotion detection and call-part analysis—can power accurate insights, while text-based

algorithms like automatic interaction clustering can uncover hot topics before they become

widespread problems

Customer interactions, whether they’ re spoken or written, contain valuable information companies

can use to increase customer satisfaction and loyalty, improve operational efficiency, and gain a

competitive advantage. But only if the technology to extract the content and aggregate it. An

individual conversation can point to issues driving personal dissatisfaction. But having the power to

combine one customer’s voice with hundreds of others— through the content of their emails,

phone calls, faxes, text chats and other customer interactions— can uncover the causes of

dissatisfaction across the entire customer base and enable your company to take action.

Text analytics technology transcribes customer calls and customer feedback from speech to text

and combines it with other forms of text interactions such as email and online chat. It then uses

natural language processing models along with statistical models to find and surface patterns.

Frequently Mentioned Topics

Text analytics can help identify the most frequent topics mentioned by customers during their

interactions with your company and point to the root cause behind them. For example, by

analyzing all calls about billing, you can find the recurring issues and questions customers have

about their bills.

Context Analysis

In addition to frequent topic analysis, text analytics also reveals the context in which topics are

mentioned during interactions. For example, the topic “charges” might be found to be closely

linked to the words “long distance.”



Sentiment Analysis

Text analytics also is used to identify the attitude of the speaker or the writer— another dimension

with which companies can assess dissatisfaction and its main drivers. For example, interactions in

which customers express negative sentiment are analyzed with frequently mentioned topics to

identify which issues correlate with negative customer sentiment.

Text analytics surfaces otherwise hidden insights in customer interactions to help improve

customer satisfaction, loyalty and operational efficiency.



Desktop Analytics

A Window into Agent In-Call Performance

Desktop Analytics is a key capability often used in concert with speech analytics. Using a thin client

installed on each agent’s desktop, Desktop Analytics tracks the applications, screens and functions

that agents use while interacting with customers. This insight enables contact centers to identify

agent knowledge gaps and training opportunities as well as improve the customer experience and

operational efficiency.

Extract Customer Data

The agent’s desktop can reveal valuable information about customers during interactions. Agents

often look up customer accounts within a CRM application. The screen may contain information

such as customer demographics, status, and products and services purchased. We can extract

such data via Desktop Analytics and attach it to the interaction as metadata, which can later be

used, for example, to list all customers interactions associated with a specific customer ID.

Companies leverage Desktop Analytics to analyze agent behavior during interactions and learn

more about the customer.



Real-Time Decisions

Make Profitable Decisions on the Fly

Machine Learning

Continuously Improve with Each Customer Interaction

Each customer journey is unique. So being able to quickly extract and apply insights about your

customers’ experiences in real time helps ensure that every future interaction—even the very next

click—is fulfilling, fruitful and profitable, both for your customers and your bottom line.

Machine Learning is a self-learning, automated modeling technique that employs Temporal

Difference Learning (a type of Reinforcement Learning technology) to improve predictive analytics

and decisions at the moment that interactions occur. It leverages our Big Data infrastructure to

develop and continuously refine predictive customer profiles from volumes of raw multi-channel

customer interactions. It learns rapidly from the digital behavior of customers in parallel, rather

than aggregating outcomes after interactions end, enabling your organization to not only make

better predictions, but impact decisions now .



Technology highlights:

Can be deployed across multiple channels at enterprise scale

Designed to be configurable by the marketer or analyst, making it open, flexible

Requires less data to render data models productive and less expense to implement

Works online, in parallel and in real time

Multivariate testing and reporting illustrates performance compared to other approaches using only

predictive analytics or business rules

Differentiated by patent-pending real-time adaptive binning processes, learning from incomplete

customer histories and distributed scalable incrementally updated machine learning models

Benefits:

Organizes raw multi-channel events by customer and uses them to compute predictive customer

profiles

Learns the value of taking actions that may carry a short-term cost, like offering a customer a

discount, but yield long-term benefits, such as increased wallet-share over time

Enables faster response to changes in customer behavior

Efficiently optimizes complex goals, such as improving lifetime value

Prevents the need to constantly build new predictive models to accommodate for model drift

Begins the learning process from the first offer; the system does not wait for the end of a

campaign or program

customer engagement analytics encompasses these highly intelligent capabilities.docx

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