AI-Powered Contact Center Analytics Solution

AI-Powered Contact Center Analytics Solution

10 min read
A group of people at a desk with headsets on, utilizing AI technology to access an information base.

Understanding Call Center Analytics

Any discussion on call center analytics needs to begin with a thorough understanding of the broader topic of speech analytics. Speech analytics is a technology that identifies human speech and text and converts that into structured, actionable data. In its most basic form, a speech analytics solution consists of a speech engine that converts speech to data, an indexing layer that searches the information, a query engine that lets users query the data and, lastly, a reporting application that enables you to present the analytical findings.

Speech Analytics in a Contact Center

As its name implies, contact center speech analytics is the specific application of speech analytics within the call center setting. Contact center speech analytics have become particularly vital in recent years, as call centers—and their human agents—grapple with increased traffic in the wake of the global pandemic.

It’s not humanly possible to listen to and analyze every minute of every single customer conversation that happens in the contact center. After all, there can be thousands or tens of thousands of audio- and text-based interactions every day. Traditional contact center speech analytics often fail to deliver actionable insight into the details and nuances that make up the true voice of the customer in these conversations.

That’s why market-leading companies are increasingly turning to conversational/interaction analytics to harness valuable unstructured data from customer conversations (including voice and text) and turn it into meaningful, deep insights. By listening to and understanding every conversation using advanced conversational artificial intelligence (AI), companies can inform customer experience decisions and strategies, automate and improve compliance and quality control, and improve agent performance.

Benefits and Advantages of Call Center Analytics

Today, there is a wide gap between rudimentary speech analytics offerings and advanced, sophisticated conversational AI-powered solutions built on enterprise-class platforms. Companies should look beyond deployment of a few basic speech analytics capabilities when choosing a solution for transforming the customer and agent experience.

A comprehensive contact center analytics solution that leverages conversational AI gives its users a major competitive advantage over their peers. Benefits include, but are not limited to:

Better Agent Performance
AI-driven contact center analytics can also be used in real time to guide agents during conversations to help them understand customer sentiment, emotion and intent. These insights can help agents drive better engagement and find faster (and fuller) paths to resolution—all of which improves key performance metrics. Additionally, evidence shows that agents who feel empowered by such technology are themselves more engaged and less likely to turn over.

Improved Customer Experience
The better contact center agents understand customers, the better the experience on both ends of the line. By identifying customer needs, behaviors and even preferences, contact center analytics provide the building blocks for creating a hyper-personalized customer experience—a key differentiator in the age of digital disruption.

Building the Business Case For Call Center Analytics

There is a strong business case for AI-enabled contact center analytics solutions today. Industry leaders and innovators need data to gain and sustain a competitive edge, win new customers and foster loyalty in current ones, drive more revenue, and introduce new products and services based on customer needs. But traditional contact center analytics are unable to uncover insights from the largest source of untapped data in your company: contact center conversations with customers.

That’s because the tens of thousands of conversations happening each day are simply recordings of unstructured data, with no way to derive deeper context or insight. Until now.

AI-powered interaction analytics turn unstructured data from voice and text conversations into actionable insight that your company can use to solve some of its most pressing issues in the contact center. However, before you begin shopping for a contact center analytics solution, defining the use cases and outcomes that are the most urgent and important to address can help you create a compelling business case for why your company needs interaction analytics now.

Call Center Analytics Use Case 1: Quality and Compliance Management

Questions to Ask Solution Needed Benefits

1. What percentage of conversations can your QA team currently audit for compliance?

2. How many people does it take to audit for compliance?

3. How long does it take to investigate a customer complaint?

4. Is there a backlog of complaints or compliance issues to investigate?

5. Can you objectively, consistently, and efficiently score agents on quality and compliance regardless of QA team member?

6. Can you correctly identify the source of customer complaints?

• Monitor and analyze all customer conversations

• Automatically track and score compliance

• Automate compliance workflow

• Use search capabilities to identify relevant calls and portions of calls

• Automate agent feedback

• Reduce time spent on quality / compliance management by up to 50%

• Drive 100% compliance with regulations and corporate policies

• Scale compliance capabilities without increasing staff

• Eliminate agent concerns about inconsistent or biased scoring

Call Center Analytics Use Case 2: Agent Performance Management

Questions to Ask Solution Needed Benefits

1. What percentage of conversations do you currently review for performance?

2. How much time does it take to manually monitor and score agent performance?

3. How long does it take before agents get feedback on performance after a call?

• Monitor and analyze all customer conversations for visibility into agent performance and behavior

• Automate agent feedback

• Provide customized guidance based on actual behavior

• Increase sales effectiveness by up to 15%

• Provide personalized sales guidance based on actual behavior, strengths and weaknesses derived from conversational data

Call Center Analytics Use Case 3: Customer Experience/Journey Optimization

Questions to Ask Solution Needed Benefits

1. Do you have an integrated view of intent, emotion, and sentiment across all conversations and channels?

2. Can you analyze every customer conversation to identify and understand trends and patterns?

3. Do you need deeper insight into customer sentiment, emotion, and intent to improve customer satisfaction and your brand’s Net Promoter Score? 4. Do you have predictive analytics that model propensity to buy/pay to improve sales and collections outcomes?

• Monitor and analyze all customer conversations

• Understand sentiment, emotion, and intent, identify complaints, and uncover issues impacting satisfaction and churn

• Identify trending topics and behavior patterns

• Improve CSAT and NPS

• Reduce customer churn

• Optimize the customer journey based on conversational insights

• Improve sales and collections rates by understanding which customers have a propensity to buy/pay

Call Center Analytics Use Case 4: Product Marketing/Management Feedback

Questions to Ask Solution Needed Benefits

1. Can you capture and report on unsolicited feedback within omnichannel customer conversations?

2. Can you distill unstructured data into insights about your products and services?

3. Can you search conversations for specific keywords to gauge customer interest in a new product or service?

• Monitor and analyze every conversation to identify and extract product-related information and trends

• Understand which products or services customers prefer

• Identify unmet customer needs

• Improve product quality

• Develop new products based on customer interest

Contact Center Analytics Reporting

When it comes to contact center analytics reporting, there is no one-size-fits-all. Because different businesses have different needs, having a versatile platform for contact center analytics reporting is essential. Built-in reporting provides insight into conversational performance and the customer journey. Seek out a solution that offers custom dashboards and visualizations, text search, topic discovery and collaboration via shared workbooks.

Evaluating Contact Center Analytics Solutions

Legacy speech analytics tools don’t offer the real-time analysis, omnichannel support, or deep insight that modern AI-powered interaction analytics solutions offer. While some legacy tools have “bolted on” limited AI capabilities to the existing product, many are designed more for improving the accuracy of speech-to-text transcriptions than identifying trends and patterns in the data or understanding customer sentiment, emotion, and intent.

When evaluating true AI-powered contact center analytics solutions, look for the following essential capabilities.

Domain-specific conversational AI technology

Understanding human-to-human conversations is the most difficult problem to solve in the AI world. However, domain-specific conversational AI has made huge leaps in sophistication by focusing on narrow applications such as the contact center and specific industries such as financial services, telecommunications, healthcare, and others. That’s why, for optimal understanding of your agent and customer conversations, look for a conversational AI solution that is specific to the domain of the contact center.

Industry-leading speech and text recognition

Contact center speech analytics need to understand your agents and customers with a high degree of accuracy to be truly effective. Your solution must be able to listen for and detect the language, including specific dialects, automatically. It should also support the languages used by your agents and customers, both today as well as your future language needs.

Omnichannel conversation analysis

For deep insights into the customer journey, seek out a contact center analytics solution that can handle both voice and text analysis, giving you an integrated view of customer conversations across multiple channels, including voice, email, and chat.

Intent recognition and analysis

Your interaction analytics solution should be able to identify and understand customer intent, using machine learning to continuously tune and optimize algorithms to deliver the highest degree of accuracy possible in predicting true intent.

Automatic topic identification and business entity and keyword extraction

The solution must also automatically identify and section key classifying elements in a conversation and match them to categories to add context and facts to an intent. For example, your contact center analytics solution should automatically identify the greeting, discover key issues and intents, and recognize and record the outcome/resolution with an accurate sectioning capability. Leading solutions will use machine learning to guide the technology in recognizing and classifying elements.

Customer sentiment and emotion recognition and analysis

Another important capability is sentiment recognition, which provides insight into the customer’s state of mind and further helps you uncover trends and patterns in the customer experience. At a minimum, contact center analytics should recognize, extract, and score customer sentiment as positive, neutral, or negative. However, to better understand your customers’ feelings within the context and intent of the call and take relevant actions based on your understanding, you should choose a solution that also identifies customer emotions such as sadness, frustration, anger, and happiness — as well as identifies the agent and customer behaviors that led to the emotions such as agent empathy, politeness, engagement and positivity.

Automated quality and performance management

Turning unstructured data into insights is the first step in automating quality, compliance, and performance management. The next step is to automate the feedback loop. A robust contact center analytics solution automatically scores and analyzes agent performance, updates self-learning agent dashboards with feedback, and shows quality and compliance analysts where best practices are not being followed or agents have gaps in skills.

Support for your recording solutions

Choose a solution that supports the recording formats and tools on which your company currently relies so that you can harvest and analyze data from past conversations.

Top Three Questions to Ask Before You Buy

1. How quickly can your contact center analytics solution / conversational AI platform be implemented?

2. Can your analytics solution/ platform be extended easily to support more AI-powered use cases?

3. When will you see a return on investment with your solution?

Choosing the Right Contact Center Analytics Provider

Beyond evaluating the technology and capabilities you need to support your contact center analytics use cases, choosing the right vendor can mean the difference between rapid success and delayed or diminished returns. The ideal conversational AI platform partner should offer:

Start Your Contact Center Analytics Journey Here

Contact center analytics reveal relevant and actionable insights hidden within massive volumes of unstructured voice and text conversations in the contact center. By turning this rich source of information about the customer and agent experience into deep understanding, your company can improve compliance and quality, reduce compliance and quality management costs, improve agent performance, optimize the customer experience and improve satisfaction, and identify needs and trends that inform product and service development.

Choosing the right solution starts with defining the use cases, creating a solid business case, understanding the capabilities you will need and carefully evaluating your options. We’re here to help as you take the next step in your conversational AI journey. Contact us now.

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