Uniphore Glossary of Terms

Call abandonment rate is the percentage of total inbound customer service calls in which the customers hang up before engaging with a live customer service agent.

Abandonment rate is influenced by factors such as the speed in which the customer is connected to a live agent and the degree to which a customer is dissatisfied with the product or service they’ve called in for.

KPI benchmarks regarding call center abandonment rate can be scaled to examine call drops by time of day, geographical region and duration of total call length. These data sets can be used to improve efficiencies regarding wait times. The benefits of a relatively low abandonment rate correlate with customer satisfaction and brand loyalty.

In general, a call center abandonment rate of between 5-8% is considered the industry standard.

After Call Work (ACW) begins in the moments after a call concludes. These are the steps an agent takes when they hear the dial tone that wraps up and processes the call. In order to be ready for another call, an agent must complete any ACW.

The tasks associated with after call work may vary depending on company protocols and desires. However, there are a few common ACW duties that agents often complete after the call ends. These can include:

• Logging information, from the reason for contact to the outcome
• Scheduling any follow-up contacts or actions
• Analyzing call data
• Sharing information with coworkers and/or other departments

After call work ties up a conversation’s loose ends. When calls aren’t cemented into a system that recognizes their importance, valuable customer information is lost. That’s why ACW ensures all valuable data is recorded for Interaction Analytics and other analysis purposes.

Watch Vijai Shankar, Uniphore’s VP of Product Marketing & GTM, break down the role of automation in after-call work in Season 2 Episode 9 of Conversations That Matter.

An agent assist is a service that supports agents in real-time during conversations. Even the most prepared and skilled agents can struggle during periods of high call volume or with complicated customer conversations. An agent assist helps navigate difficult calls, routine responses, and customer requests all the while improving phone interactions and speeding up response time.

Agent assist solutions provide aid and workflow management in all areas of agent work, including:

• Beginning every call with automated customer data
• Quickly diagnosing customer intent and need
• Providing guidance during each conversation
• Implementing workflows in conversations and with task completion
• Assembling call summaries for After Call Work
• And much more

Real-time agent coaching empowers your agents to be successful. With increased productivity, reduced stress and anxiety and an improved work experience, the right agent coaching can simultaneously drive both increased revenue outcomes and employee satisfaction and retention.

Effective call-center coaching is timely, targeted, actionable and objective. A conversational artificial intelligence (AI) platform can offer real-time agent coaching at scale to help with clear line-of-sight coaching during every interaction. A well-coached agent provides a better experience for customers by putting to practice empathy techniques and skills designed to reduce on-hold times.

Agent coaching allows agents to learn new skills, receive constructive feedback and improve performance. The automation of coaching provides data regarding an agent’s areas of opportunity and allows a supervisor to customize 1:1 meetings based on data and projected outcomes.

The utilization of effective agent coaching ensures employee retention and increased satisfaction.

Agent guidance is the direct support and real-time coaching that guides agents through calls. Complete with live insights and next-best actions for delivering consistent customer experience, agent guidance provides call center workers with the right tools to prevent customer churn.

Calls can be as frustrating for the agents as they are for the customers. But with the right tools to handle the rise in high-effort interactions, customers have better experiences and agents are prepared to handle their queries and needs. These tools are gap-filling techniques, teaching agents on the fly — and often in the middle of difficult calls — while ensuring customers receive consistently positive care.

Automatic Speech Recognition (ASR) is a key element of conversational AI that enables computer programs to understand and interact with humans through speech. While ASR has many applications, its primary function is converting spoken words into computer text.

In a call center setting, ASR-enabled programs can free agents from time-consuming keyboard data entry tasks, which can accelerate call efficiency and enable the agent to focus on client engagement rather than on manually typing notes or searching directories.

ASR is also used in intelligent self-service platforms to guide callers toward resolving their queries themselves and, consequently, deflecting routine calls from live support. This process aids cost reduction efforts while simultaneously navigating customers to the right channels for inquiry resolution by way of automated assistance.

The versatility of ASR software has made it an increasingly common presence in call center organizations.

An agent’s Average Handle Time (AHT) is the average amount of time it takes for a call to be completed. AHT consists of the duration of the call, from the moment an agent picks up the call to the final tone. This time is spent discovering what customers need – listening to their complaints, information or stories to provide a diagnosis – and resolving or answering their query. Transfers, hold time and conversations are just a few of the elements included in AHT.

Average handle time length will depend on the agent’s ability to respond to the customer as well as the customer’s monologuing time. The amount of time it takes to help a customer results in the AHT, and is human-focused. While a customer might be happier with faster service, call centers in general have to walk the line between reducing average handle time and ensuring customers feel listened to and supported for the entirety of their call.

Average Wait Time (AWT), also known as Average Speed of Answer (ASA), refers to the duration an inbound caller spends in queue.

This metric differs from Average Handle Time (AHT), which calculates the average length it takes an agent to complete a call or customer interaction. Call centers can observe this metric with varying degrees of granularity, across ring groups, phone numbers or agents.

The industry standard AWT is 80% of calls answered within 20 seconds.

Customers increasingly expect real-time service and instantaneous access to a call center agent. Reducing the time callers spend waiting to interact with your agents can lower your call abandonment rate and improve overall customer satisfaction.

Brand advocacy is the likelihood that a person will promote your brand or business to their peers. Examples of brand advocacy include word-of-mouth customer recommendations, positive online reviews and social media praise. While most brand advocates are happy customers, employees, partners and social influencers can also raise a company’s image—and marketability—through advocacy.

Brand advocacy is typically measured as a company’s Net Promoter Score, or NPS. NPS is a critical metric for contact centers. Similar to customer satisfaction (CSAT), NPS surveys ask customers to rank their willingness to recommend your brand, product or service on a scale of 1 to 10. Customers that return scores of 9-10 are typically your biggest brand advocates.

Positive brand advocacy helps companies grow their business organically, reach a wider audience and strengthen brand trust—all of which increases sales and captures market share.

Buyer engagement refers to the level of participation and/or interaction a buyer has with a company’s portfolio of products or services. Companies measure buyer engagement through buyer behaviors at various touchpoints, such as opening an email, clicking on a video or answering an agent’s phone call. The stronger the level of engagement, the more likely a potential buyer will make a purchasing decision.

Forecasting buyer engagement is accomplished through data collected specifically regarding the messaging, content, training resources, coaching opportunities and historical successes that drive buyers’ decisions. This data is combined with insights into what specific avenues see greater engagement in order to maximize visibility to targeted buyers.

Buyer engagement is valuable for sales teams, marketing departments and management alike, driving customer-facing methodologies, developing client loyalty and generating new revenue streams.

Buyer sentiment refers to a buyer’s initial, emotional reaction to a sales presentation or offer. Buyer sentiment can provide insight into the likelihood of a purchase. As a result, a positive initial contact can influence the future purchasing decisions of potential clients.

Buyer sentiment data provides insight into the effectiveness of both a sales representative’s messaging and the method of communication. Engagement over phone, for example, can be contrasted with engagement via email communications in order to quantify the efficacy of contact center processes.

Buyer sentiment can assist agents in forecasting realistic sales timelines and leverage positive initial contact with products and services to ensure client acquisition. The data analysis of buyer sentiment across multiple delivery methods and communication outcomes provides valuable insight into what comprises effective customer engagement.

Chat bots are an older (albeit still commonly used) customer service tool primarily used in self-service platforms. Unlike AI-powered self-service solutions, which can respond dynamically to human input, chat bots run on scripts that can only handle simple requests that follow a pre-written dialogue.

Despite the ongoing popularity of chat bots, call centers still rely on human agents to intercept caller inquiries that traditional chat bot technology can’t process. While this tool can still be utilized to handle simple tasks, such as routing a caller to an available agent, its limitations reduce its usefulness  in customer support roles.

Computer vision (CV) is a form of artificial intelligence (AI) that helps analyze facial expressions to detect emotion and level of engagement in video interactions.

The implementation of a CV platform in a call center enables connected computing devices to assess the reactions of both callers and agents as they navigate customer experience issues.

CV technology can also be utilized to aid in authentication and authorization protocols by identifying individuals in a digital image.

Harnessing this technology as part of an emotional intelligence (EQ) platform improves the customer experience by revealing the emotional context driving conversations and outcomes. A database of computer vision-captured interactions allows predictive analysis to detect patterns of human interaction that can improve future outcomes.

Equipped with this data, a call center is able to generate valuable insight into sentiments and engagement based on customer experience interactions occurring in real-time.

Conversational AI allows humans and machines to have conversations via verbal or written channels. Using Natural Language Processing (NLP) and Machine Learning (ML), AI-powered software systems that handle interactions learn to recognize speech and text patterns and respond in kind. In customer service applications, conversational AI responds to customer requests, dilemmas and situations as a human agent would. It can also augment live agent interactions by transcribing and analyzing customer input, autopopulating form fields and feeding agents relevant data in real-time.

The applications of conversational AI span industries and corporations, and are already in use in many day-to-day operations. From chatbots to automated messaging services to voice searching software available on many platforms, conversational AI helps businesses and customers by offering smooth interactions and solutions.

A customer engagement management platform consolidates the tools and processes businesses use to manage customer engagement and interaction.

This technology understands when to task self-service or assisted service platforms based on the needs of the customer. By matching the context behind a customer’s call with business protocols, agent skill classifications and routing infrastructure, a customer engagement management platform can navigate the caller toward the resource or agent that will provide the best outcome.

The tool also provides a call center with a direct line of sight into the customer relationship management data that informs customer experience oversight.

CX, or Customer Experience, is the sum of a customer’s perceptions of a brand or a business as the result of their interactions with it. CX is an ongoing and evolving process that spans from before a customer engages with an organization to the point of interaction to the time after a product or service has been received. Each touchpoint along the customer journey—from a company’s website to its email campaigns—influences CX and, consequently, a customer’s relationship with the company.

Companies leverage CX technologies to make customer experience more enjoyable, engaging and conducive to buying their products or services. Examples of CX technology include self-service portals, intelligent virtual assistants (IVRs) and chatbots and voicebots. Many companies employ a customer experience management (CEM or CXM) system to manage and evaluate multiple CX touchpoints. Unlike CRM, which uses data to track, analyze and facilitate customer engagement, CEM gauges a customer’s perception of a brand or a business, often through Voice of the Customer surveys and other feedback methods.

Because company perception impacts sales, customer loyalty, brand advocacy and more, building a positive CX is vital to a company’s bottom line. The emergence of conversational AI and automation is enabling companies to integrate and streamline their CX programs, creating an easier and more intuitive customer experience across all touchpoints. The result: higher conversion rates, better customer satisfaction (CSAT) and net promoter scores (NPS) and reduced customer churn.

Customer loyalty is a level of engagement that indicates how likely customers are to repeat business with a company and choose their goods and services over those of their competitors. Key factors that drive customer loyalty include:

• Consistently meeting or exceeding customer expectations (i.e. customer satisfaction)
• The ease and speed of solving customer issues
• Timely fulfillment of follow-up actions
• The level of personalized service offered
• A company’s perceived trustworthiness
• Positive brand image and perception

Customer loyalty is a good indicator of which sales, marketing and customer service strategies and solutions are working best. Customer loyalty is also closely related to Brand Advocacy, as loyal customers typically make the best brand advocates and regularly return high Net Promoter Scores (NPS).

CRM—short for Customer Relationship Management—is a system that combines technology, business strategies and procedures with the goal of strengthening a company’s business relationship with its customers. CRM systems often integrate sales, marketing and customer service tools to track, analyze and engage with individuals at various touchpoints throughout their customer lifecycle.

Businesses use CRM systems to collect and store customer information, automate certain tasks, streamline customer service interactions and connect various company departments. By bringing every aspect of a customer’s relationship with a company under one roof, CRM can help identify sales opportunities, target marketing campaigns and facilitate customer service outreach and follow-up activities.

Today, new innovations in conversational AI and automation are greatly augmenting and enhancing the capabilities of traditional CRM systems. With AI-enabled CRM, businesses can gather and leverage richer data from each interaction—including customer sentiment, intent and emotional information—to form an even deeper understanding of their customers and accelerate their level of engagement.

Customer satisfaction is a measure of how happy customers are with a company, its products and/or services. Often abbreviated CSAT, customer satisfaction provides a benchmark for how a company’s offerings meet, exceed or fall short of customer expectations.

Companies often rely on customer satisfaction (i.e. Voice of the Customer) surveys and other feedback methods to gauge their CSAT level. Customer satisfaction surveys typically ask customers to rate various aspects of their experience with the company on a scale of 1 to 10. The results are then calculated and aggregated to produce a comprehensive CSAT score. While CSAT scores can vary by industry, having a score of 75% or higher is generally considered to be good (meaning three out of four customers reported having a positive experience).

While many things can impact customer satisfaction (such as product quality, reliability, etc.), customer experience consistently ranks among the top factors. For customer service, and contact centers in particular, long hold times, nonresolution of a customer’s issue and unfulfilled follow-up actions (i.e. promise management) are key CSAT detractors.

Emotion AI tools use artificial intelligence to “read” emotions based on audio and visual cues. When technologies compute feelings using information from the lilt of your voice or your expression to your words, they are attempting to better understand your state of being. These machines collect data based on inflection, tone, word choice and many other systems and analyze them to recognize patterns in behavior and choices.

Whether interpreting voice analytics or using facial expression analysis, emotion AI understands stress, anger, happiness and joy and can offer a deeper level of understanding in CX applications. It can strengthen customer relationships, engagement and brand loyalty. With emotion AI, enterprises can predict customer reactions and improve outcomes, including higher sales conversion and First Call Resolution (FCR) rates and better customer satisfaction (CSAT) and Net Promoter Scores (NPS).

Emotional intelligence in AI refers to a machine’s ability to learn and respond to human emotion. Given the importance of feelings, passions and emotions in decision making – especially when it comes to products and services – training AI with emotional intelligence improves customer experience (CX). Now, agents don’t have to decipher a customer’s emotions and desires alone. AI systems that are equipped with emotional intelligence are able to pinpoint how conversations are going and how clients truly feel, allowing the root of their problem or challenge to be elevated and responded to quickly and effectively.

Call center agents are assisted by the emotional intelligence offered by AI, but the benefits extend far beyond the dialing and tone of a call. Artificial intelligence is able to gauge and map customer satisfaction and happiness across the entirety of the customer journey, and adjusted approaches offer opportunities to improve CX.

Emotional Quotient (EQ) is the measurement used to evaluate levels of emotional intelligence in customer interactions. Artificial intelligence tools that use EQ can accurately evaluate customer emotions and prompt appropriate responses based on emotional data. With emotional quotient in effect, users can be sure that their AI is doing its job to the fullest ability, improving conversations and overall customer satisfaction.

EQ enhances customer experience (CX) by providing AI-powered tools with a precise and actionable benchmark for interpreting a wide array of customer emotions. When customers feel listened to and are given the highest quality of service by their agents and tools, their entire customer journey benefits – as does their trust and loyalty to a company.

Explainable AI (XAI) is artificial intelligence that is programmed to “explain” why it made a given set of decisions. The purpose of XAI is to give transparency and credibility to the results generated by deep learning algorithms. Explainable AI increases program accountability by recording and disclosing a program’s strengths, weaknesses, decision criteria, data sources and more. As AI models become more complex (and their datasets become exponentially larger), there is a growing demand for XAI as a safeguard against misinformation.

First Call Resolution, also known as First Contact Resolution, refers to a call center resolving an issue for a customer on the first call. When customers call for help they expect a solution for their problem to be found during the length of the call. Factors that impact FCR include the complexity of the customer request, agent access to relevant information and knowledge of appropriate next-best actions.

First Call Resolution is a valuable metric for measuring individual agent performance as well as overall contact center efficiency. FCR directly correlates to customer experience – feeling listened to, respected, and understood in their dilemmas – as well as the tools call agents have at their disposal to find success. As a result, contact centers with high FCR rates see higher customer satisfaction (CSAT) scores and lower customer churn.

Generative AI is a type of artificial intelligence that can produce, or generate, new content.  Generative AI uses unsupervised learning algorithms to create novel text, imagery and more.  It does this by detecting underlying patterns related to what is input by a user and creating similar content. Modern generative AI models are trained on massive datasets and utilize several techniques to function:

• Generative Adversarial Networks (GAN): GANs utilize two neural networks—a generative network and a discriminatory network—to simulate conceptual tasks. The generative network generates synthetic data similar to the source data, while the discriminatory network tries to distinguish between the synthetic data and the original.
• Variational Auto-Encoders (VAE): Broadly, VAEs encode and compress the source input, then decode and attempt to reconstruct the information from the original source. This process helps generative AI models “fine tune” their outputs.
• Transformers: Transformers are deep learning models that mimic cognitive attention to classify and gauge the significance of input data. Transformers are trained on large language and/or imagery datasets and are an increasingly popular method for generating text and imagery.

AI-powered software called intelligent applications includes rules engines, user interfaces, notifications and alerts, and other components that handle specific use cases within the call center.

This software enables intelligent agent assistance with multimodal conversational self-service such as voicebots and chatbots.

Intelligent apps continually learn from customer interactions and other sources to build upon a database of historical relevance and predictive future outcomes. The result for call centers: a constantly building pool of artificial intelligence-powered learning that can accelerate efficiencies and troubleshoot customer concerns.

Historical and real-time data from user interactions allows intelligent apps to make predictions and suggestions, allowing for highly adaptive and personalized user experiences. This constantly-evolving software ensures call center agents enter each customer interaction armed with up-to-date information and data relevant to the conversation. Predictive analysis anticipates user behaviors and delivers instant and actionable suggestions to drive customer satisfaction.

Intelligent decision support uses machine learning and reasoning to discover insights, find patterns and uncover relationships in data, automating the steps that humans would take if they could exhaustively analyze large datasets.

An intelligent decision support system (IDSS) utilizes artificial intelligence (AI) techniques in order to behave the way a human consultant would. From the initial gathering of relevant data to diagnostic interpretation of possible solutions, IDSS fully automates decision processes based on algorithmic success probability outcomes.

Intelligent decision support systems aid decision makers by performing tasks without the need for human intervention. IDSS integration benefits call centers by expediting customer service solutions and assisting agents in arriving at favorable outcomes. When a customer issue or question is raised, AI-powered IDSS can process the query against all similar entries in a database, arriving at a proven solution instantly.

Interaction analytics is the analysis of every conversation. It is the practice of taking the valuable records of conversations that occur on calls, chat transcripts, and emails and distilling the information into meaningful insight for company-wide benefit. Conversations in these and other formats are reviewed using artificial intelligence and natural language processing (NLP) tools.

Interaction analytics takes the wealth of information and data that exists within conversations and draws conclusions about sticking points, miscommunication, customer satisfaction and more. Interaction analytics tools ensure a company understands their customers’ needs and desires related to the offered product or service. Additionally, because they can analyze 100 percent of interactions, these tools are invaluable for quality assurance and compliance auditing needs.

A knowledge base provides a single source of truth for call centers. Contact center agents rely on knowledge base systems as the main point of reference for the entirety of their business’ processes and protocols. Many knowledge bases exist as static or manual databases with limited searchability. This requires agents to memorize large amounts of company procedures or search out next-best actions for complex requests–often while a customer is on hold.

By contrast, a knowledge base platform with an added layer of conversational AI removes the barriers between information streams. This allows real-time assimilation of customer data and relevant knowledge to be brought to the surface, making all key data available to agents the moment they need it.

The increased efficiency of an AI-assisted knowledge base reduces agent anxiety and customer wait times. The time saved by the near-instant surfacing of relevant information gives agents more time to accurately troubleshoot client concerns and focus on what matters most–customer experience.

Large Language Models (LLMs) use deep learning algorithms to process large volumes of written text. They utilize Natural Language Understanding (NLU) and Natural Language Processing (NLP) to identify context, patterns and relationships in language. As its name implies, large language models are trained on large language-based datasets. LLMs work by processing inputs through a network of neurons, each processing the output of the former neuron, until a final prediction is made on the meaning of the original input.

Natural language processing and natural language understanding (NLP and NLU) are components of conversational AI that help computers understand and interpret human language.

NLP and NLU are able to identify, separate and contextualize normal languages (the tongues we speak) from formal or constructed languages (programmable languages like Python and Java). The NLP and NLU processes automatically format words into sentences aligned with our human speech patterns, so the information can be passed back to us.

Natural language processing and natural language understanding draw from multi-disciplinary sources such as machine learning, artificial intelligence, computer science and computational linguistics in order to bridge the gap between human communication and computer comprehension.

NLP and NLU further the understanding of linguistic nuance for computer systems, and allows for the automation and extraction of both information and insights contained in documents to be categorized or organized.

Natural Language Understanding (NLU) is a subset of Natural Language Processing (NLP) that utilizes Artificial Intelligence (AI) to help machines understand spoken and written human communication. NLU leverages machine learning to recognize elements of speech such as intent, sentiment and syntax.

NLU assists call centers in understanding the purpose or issue behind a customer’s call, and is an important pre-framing tool for agents looking to resolve it. By analyzing and predicting customer intent, behavior and emotions, NLU helps agents and interactive voice response (IVR) systems better understand and respond to customer issues.

The Net Promoter Score (NPS) is an index that ranges from 0 to 10 in order to measure the willingness of customers to recommend a product or service to others.

A company’s net promoter score is a proxy for discerning customer satisfaction and brand loyalty.

NPS is a critical contact center KPI. Net promoter scores are calculated by asking customers a question and having their response gauged on the corresponding scale. This provides quality quantitative feedback in order to measure customer experience (CX).

Based on the response, NPS scores generally fall under three categories:

• Promoters (scores 9 to 10): They’re your brand advocates.
• Passives (scores 7 to 8): They’re not exactly loyal, but won’t vocally disparage your business.
• Detractors (scores 0 to 6): Will not recommend your company, may speak against it.

NPS score is calculated by subtracting the percentage of detractors from the percentage of promoters.

Promise management occurs after promises have been made during a call, ensuring that each commitment is kept with smooth and timely follow-through. Every conversation will likely involve some element of commitment-making. These may include billing requests, appointment scheduling and other tasks that an agent approves. After promising a certain effort, promise management comes into play.

In order to build long-lasting, positive and growing customer confidence, there must be consistent and dependable delivery. When promises are managed effectively, a customer can expect their request to be taken care of as quickly as possible. An agent’s promise management should include timely responses as well as an accurate and pinpointed resolution. A customer that experiences immediate, high-quality promise fulfillment will have enhanced commitment to the company and product – as well as a strengthened relationship with the brand.

Revenue Intelligence (RI) refers to the sales and product data gathered and analyzed by artificial intelligence (AI). Collected from a variety of sources, including current customers and prospective clients, RI takes data and turns it into actionable insights.

By focusing on trends with revenue generation opportunities, businesses utilize RI technologies to forecast customer demands. This data-centric approach gathers information from all applicable segments of a business, pooling insights from sales and marketing departments with historical successes and additional context from customer support teams in order to coalesce customer truths.

RI provides valuable, scalable insights for a sales team. The ability to pinpoint targeting data and buying signals with granularity arms a sales rep with powerful tools to better highlight lead opportunities and deliver communications specifically tailored to consumer needs. Contact centers with RI programs see higher lead conversion rates by targeting the contacts, channels and times that prove to be most successful.

Revenue Operations (RevOps) consolidates sales, marketing and customer service operations to drive revenue generation and client acquisition.

Utilizing marketing resources, sales processes and customer retention strategies in one cohesive platform, RevOps consolidates data streams and communication across departments. This results in increased efficiency and a reduction in the downtime associated with waiting for feedback from siloed teams.

RevOps benefits the business by establishing shared goals. Goals related to conversion rates, sales cycle rates and win rates can thus be achieved in unison through streamlined cross-department collaboration.

Sales engagement refers to a seller’s effectiveness in delivering a valuable customer experience (CX). Sales engagement encompasses all of the interactions that occur between call center staff and buyers, from phone conversations to email campaigns to direct mail and chat services. This information is valuable to sales reps and marketing departments alike.

Data collected from sales engagement interactions can help drive sales performance by offering detailed levels of insight into successful conversations that have resulted in client acquisition and retention. Sales engagement data allows for the creation of best practices and standards that are backed by metrics. Individual sales rep performance can thusly be measured against performance thresholds in order to provide coaching opportunities and continued education.

Sales engagement data provides marketing departments a clear line of sight into the campaigns, services and verbiage that is most effective in driving customer buying patterns. Exceptional sales engagement equals exceptional CX.

Sales Operations (SO) refers to the series of processes, technology and data that coalesce in order to maximize revenue generation capabilities. A subset of SO, Sales & Operations Planning (S&OP) creates a comprehensive sales plan to manage the efficiency and productivity of the sales team as they work towards a sales goal.

Sales operations utilize multiple systems tools (e.g. CRM software) along with inter-department communications, training and other resources to ensure revenue optimization. SO provides a calculated, systems-based approach to sales. By eliminating unnecessary challenges facing a sales team, members can focus on revenue generation unencumbered.

Sales performance management (SPM) refers to the automation and unification of sales processes. Designed to improve operational success, SPM informs quota achievement outcomes by combining quota management and planning with gamification and analytics.

Sales performance management utilizes machine learning technologies to collect and analyze data that contact centers can use to maximize revenue opportunities. Aligning individual sales goals with a businesses’ overall strategy, SPM combines reporting and analytics with objectives management in three key areas:

• Quota management: Set individual, achievable goals based on the overall revenue potential of a territory
• Territory management: Create territories based on geography, business units or other custom factors
• Incentive compensation: Calculate variable incentive targets leveraged with customer data and forecast potential compensation scenarios

Sales performance management reinforces contact center data by providing a streamlined sales process and insight into the factors affecting performance outcomes and incentives.

Self-service platforms allow customers interacting with contact centers to find answers to their questions or resolve their own problems. This increased efficiency empowers a customer base and can increase brand loyalty and customer engagement.

By providing access to relevant information or facilitating the services needed to resolve customer issues, contact centers boost the productivity of their agents, who are free to handle more complex problems. Today’s self-service portals are easily accessible, user-friendly and operate across both desktop and mobile smart devices. Utilizing resources such as chatbots, knowledge bases or frequently asked questions (FAQs), customers can access information relevant to their account and call history, as well as details related to the products and services they’re inquiring about.

Self-service platforms help contact centers save time, effort and money by empowering customers to resolve their issues independent of agent interaction.

Speech analytics is the process of extracting meaningful insights from calls in order to improve future conversations. There are thousands of hours of audio and text-based interactions that hold a goldmine of data on customer journeys, experience and desires. When these recorded calls are analyzed, the valuable unstructured data can help inform practices in order to facilitate smoother interactions.

Modern speech analytics software delivers actionable insights into the details and nuances that make up the true voice of the customer. These elements include everything from the feelings and sentiments of a customer to the context and meaning of their interactions. What’s more, speech analytics software generates these insights automatically without cumbersome, time-consuming effort on the part of call center workers.

Value-based sales are the conveyance of the worth of a product or service to the potential buyer. Rather than exclusively featuring the product or service itself, value-based selling highlights the significance of the resulting purchase and how it might impact the buyer in a positive way.

By showcasing the enrichment opportunities a product or service can provide a potential buyer, agents utilizing this sales approach tap directly into the wants and needs of a customer. Value-based sales put the intended buyer first, guiding them through both the process of purchase and the feelings of satisfaction they are likely to experience once the transaction is complete.

Identifying pain points and offering immediate solutions is the key to the value-based sales approach. A focus on educating the potential buyer regarding the improvements they’re sure to enjoy with a given product or service makes value-based selling advantageous over simply listing product features.

Virtual sales describes the process in which salespeople integrate with technology applications in order to interact with customers online. Designed to replace traditional, in-person sales conversations, virtual sales allow for both synchronous (communications occurring at the same time) and asynchronous (communications at different times) functionality.

Virtual sales have become a popular solution for businesses in the wake of the COVID-19 pandemic. Virtual sales conversations occur at the convenience of the customer, regardless of their time zone or location. And with today’s agent assist solutions, salespeople can leverage a wealth of data and logistics information from their workstations in order to ensure customer satisfaction.

Virtual sales allow a business to ascertain customer needs via personalized conversations, and enable remote purchasing of goods and services based on similarly remote interactions. This level of service and communication, as well as the overall ease of use of virtual sales, can increase consumer confidence in a brand or business.

Voiceprint biometrics technology recognizes voice patterns for frictionless verification of individuals such as agents, eliminating the hassle of manual authentication and increasing customer trust.

Also known as voice recognition, this security authentication process listens for vocal tonality tied to the user in order to authenticate their identity. Similar to face recognition and fingerprint authentication systems, voiceprint biometric authentication relies on unique tonal markers in order to offer seamless access to connected devices and programs.

Call centers benefit from the use of voice recognition software in order to combat fraud and increase customer confidence that the call is secure.