in Customer and Agent Interactions
Sentiment
PART 2 OF 2
What Is Sentiment?
How Does Sentiment Work?
Taking Sentiment to the Next Level
PART 1 OF 2
How Is Sentiment Used?
Sentiment is a machine learning (AI) model trained with a large, comprehensive CX dataset to measure whether a customer interaction is positive, negative, or neutral on a relational scale.
It can be used to:
measure agent performance
understand call volume and trends
identify process or product issues
provide live guidance to agents during conversations with customers
Sentiment also identifies interactions that start negative and turn positive, or the other way around.
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Social media posts
Chats
Phone calls
Emails
Any voice or text interaction can be analyzed in real-time by AI modules for insights into where customer sentiment is low or high—and why.
The model analyzes other factors, too.
1
Semantics, because positive phrases can offset negative ones
2
Context, because some words that may normally be used positively can also indicate sarcasm and frustration
These additions improve accuracy because they give the model more knowledge to work with.
Laughter, which could indicate a positive change
Changes in pitch, tone, or speaking rate, which can signal change in satisfaction
Cross talk, which could indicate confusion or frustration
The model identifies positive and negative words and phrases, whether spoken or text, but it considers two key factors:
Language is key.
Taking Sentiment To The Next Level
AI-powered sentiment analysis presents endless possibilities. It has been proven to be predictive of tNPS (transactional Net Promoter Score), and sentiment is often considered synonymous with the term, “Predictive NPS.” As such, it can be a helpful method of understanding how to improve customer experience and drive sentiment further into the positive.
Understanding volume and trends is the real value behind sentiment analysis, and there are many use cases where it can deliver value.
Being able to accurately measure customer experience begins with pulling together data from all sources where a customer may have explicitly or implicitly provided feedback along their journey and putting it together in a timeline. So as they move from touchpoint to touchpoint across channels, their experiences can be better understood and analyzed.
The Journey Excellence Score (JES) is an amalgamation of many different methods of measuring customer experience quality, delivered as a score from 0 to 100. It uses many customer experience indicators, including but not limited to:
Interactions involving specific contact reasons such as cancellations or complaints Events such as a closed sale, upgrade, or lost sale Survey responses Sentiment scores on contact center interactions Journey “effort,” often expressed in terms of the number and duration of interactions
The Journey Excellence Score provides the next level of sentiment scoring, effectively offering businesses the ability to predict sentiment by finding customer journeys that need to be improved.
WHITE PAPER Introducing the Journey Excellence Score (JES) The missing metric in your customer experience measurement program
sentiment
Feedback Data
complaInts
churn
upsell/Cross-sell
journey duration
Customer Effort Data
channel switching
volume of occurrence
frequency of occurrence
Being able to accurately measure customer experience begins with pulling together data from all sources where a customer may have explicitly or implicitly provided feedback along their journey and putting it together in a timeline so that as they move from touchpoint to touchpoint across channels, their experiences can be better understood and analyzed.
Click to enlarge
Immediate NPS Improvement with Enlighten AI Routing
Anomaly Detection
Query Coverage Analysis
Automatic Categorization
AutoDiscovery
Journey Excellence Score
The Next Level with Enlighten AI
Customer Satisfaction
Process Improvement Analysis
Sales Effectiveness
Product Performance Monitoring
Agent Enablement and Coaching
Personalized Survey Feedback
AutoDiscovery is a first-class filter working alongside AI-generated categories to slice into data models. AutoDiscovery consists of three main capabilities:
These are designed to help reduce time to insight, reduce effort in surfacing high-volume or low-sentiment categories, detect anomalous activity in contact center trends, and better understand how well queries are covering priority conversations for the business.
BROCHURE AI-Enabled AutoDiscovery with Nexidia Analytics
Sentiment scoring is a proven predictive indicator of customer satisfaction such as NPS, tNPS, and CSAT surveys. But at a deeper level, it is the agent behavior during an interaction that can positively or negatively influence the sentiment score on every interaction they have with a customer. NICE Enlighten AI for Customer Satisfaction is an out-of-the-box solution that objectively measures agent behaviors proven to drive customer satisfaction consistently and accurately, on every interaction, with no bias. This provides agents and managers with metrics they can trust.
Automatic Categorization uses customer data to automatically identify topics and relationships among the clusters within the interaction data. The clusters are visualized by size and color via metric filters, such as sentiment, volume, interaction duration, trend anomalies, average handle time, volume, and cross talk, providing for quick insight to the topics and associated attributes.
Query Coverage Analysis maps existing structured queries among the topics discovered in automatic categorization, providing an easy mechanism for identifying topics that are not currently being quantified or tracked. This makes it easy to see where fast action might be taken to close knowledge gaps, while also providing a confidence level for topics already being monitored.
Automatic Categorization uses customer data to automatically identify topics and relationships among the clusters within the interaction data. The clusters are visualized by size and color via metric filters, such as sentiment, volume, interaction duration, trend anomalies, average handle time, volume and cross talk, providing for quick insight to the topics and associated attributes.
Anomaly Detection automatically identifies phrases and topics whose “arrival pattern” differs from its typical pattern. By automatically identifying these changes in trends, Anomaly Detection can help surface emerging topics whose volume is likely to grow due to a newly uncovered problem. It can also uncover topics that might never otherwise be identified through day-to-day analysis of big data and high volumes of interaction.
The proven correlation between AI sentiment and tNPS opens a door for companies to improve how they route customers with employees. Instead of traditional skills-based routing, companies are using the insight from AI sentiment to optimize call routing for each customers’ unique preferences, immediately improving NPS and CSAT metrics. By combining AI sentiment with all available data to understand customers’ unique needs and preferences, Enlighten AI Routing prescribes the best agent for every customer. From the first routed call, Enlighten AI Routing improves NPS/CSAT, increasing positive sentiment and reducing handle time. Enterprises personalize customers’ experiences while immediately improving NPS/CSAT with precisely measured results and an ROI guarantee – all without training, coaching, or employee change management.
Customer Results on Enlighten AI Routed Calls
0
hours
of coaching,
training, or employee change mangagement
%
Decrease in average handle time
6.4
Increase in positive sentiment
13
Decrease in negative sentiment
15
VIDEO How Enlighten AI Routing Works
VIDEO How to Immediately Improve CSAT
Sentiment analytics represents one AI model that delivers insights across a myriad of scenarios. Using its capabilities not just as a single metric but as a first-class filter among other AI-based metrics is just one use case for how predictive AI is taking over CX programs today. The Enlighten AI framework interprets every interaction and transforms subjective behaviors, decisions and outcomes into data that is consistent, accurate, and without bias. It powers use cases proven to improve customer satisfaction and brand loyalty.
A New Way to Meet Your KPIs: Enlighten AI
NICE Enlighten AI is the first AI framework that interprets every interaction and transforms subjective behaviors, decisions, and outcomes into data that is consistent, accurate, and without bias. It powers use cases across the organization proven to improve customer sentiment (NPS, tNPS, CSAT), and turn them into lifetime advocates of your brand.
Intelligent Feedback
Personalized, contextual surveys sent to at-risk customers provides closed-loop actions to minimize the risk of churn
Intelligent Routing
AI intelligent routing matches customers to agents most likely to provide positive customer satisfaction outcomes
CSAT and agent behavior scores empower agents to deliver excellence, one conversation at a time
Complaint Management
Automatically identifies and manages complaints to protect organizations from reputational, financial and compliance risks.
Fraud Prevention
Automatically identifies and stops fraud in the moment
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When it comes to uncovering valuable insights about your customers, Enlighten AI for CX does the work for you. Enlighten AI for CX is the only one-vendor, single AI solution for accelerating action to transform entire organizations into customer-centric businesses, right out-of-the-box. Immediate results offer prescriptive insights throughout the customer lifecycle and are leveraged to optimize every moment of the customer’s journey with the business. We are the only ones who can offer an all-in-one view for effective change across every role in the organization. For the first time, all stakeholders can have the intelligence required to accelerate actions proven to improve customer satisfaction and lifetime value.
To learn more about how AI is revolutionizing customer experience and contact center analytics, visit www.nice.com/enlighten-ai.
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