- Chapter I: What Is A Conversation Intelligence Platform?
- Chapter II: AI Transcription
- Chapter III: Sentiment Analysis
- Chapter IV: Topic Analysis & Call Categorization
- Chapter V: Automated Quality Management (Auto QA)
- Chapter VI: Automatic Call Summary
- Chapter VII: Custom AI Insights
- Chapter VIII: Additional Considerations
As a data-driven contact center manager, you know Sentiment Analysis can provide you with tremendously helpful Voice of Customer insights and even help your contact center increase profits:
- Data-Driven Decisions: Leverage aggregated sentiment data to optimize products and services based on customer emotions and feedback.
- Identify Negative Trends: Quickly spot negative customer sentiment to address issues before they escalate, improving customer satisfaction and retention.
- Enhance Customer Support: Pinpoint which conversations need immediate attention, allowing support teams to provide timely interventions.
- Proactive Engagement: Set up alerts for low sentiment scores to intervene and resolve potential problems, strengthening customer loyalty.
- Training Focus: Identify recurring issues in conversations to tailor employee training programs effectively.
Large Language Model (LLM)-Based Sentiment Analysis Is A Game-Changer
For the past few years, Machine Learning/Natural Language Processing (NLP)-based Sentiment Analysis was the cutting-edge technology used to analyze customer and agent sentiment in contact centers. While it was easier to configure and somewhat more accurate than rule- or lexicon-based Sentiment Analysis, it struggled with longer conversations or overall sentiment.
This completely changed with the use of advanced AI models like those based on transformer architectures. Sentiment Analysis based on LLMs is capable of not only understanding the full context of a conversation, but also handling nuanced language, sarcasm, and complex sentiments. Furthermore, it can provide explanations for sentiment scores and sentiment definitions, and can be customized using a prompt designer (e.g., score a call positively if the customer is satisfied with the outcome at the end of the call).
We needed a platform that went beyond basic interaction recording. We wanted a single solution that would provide usable customer service insight. We needed analytics tools to measure performance and customer sentiment. Most of all, the data had to be organized, easily accessed, and easy to understand without having to become experts in analytics."
Jared Jenevein
IT Analyst - Companions & Homemakers
Topic Analysis in contact centers is an AI-powered process of automatically categorizing calls based on their content, allowing better insights into customer interactions. With Topic Analysis, you can, for example:
- Detect trends: Identify shifts in customer behavior, like reactions to new features or prices,
- Improve customer experiences: Understand common customer needs to improve service,
- Identify problems early: Catch issues (e.g., shipping delays) early by monitoring call volumes,
- Increase efficiency: Automate call analysis, reducing manual work,
- Spot opportunities for agent training: Identify areas where agents may need additional training based on frequent customer issues or requests,
- and much more.
The latest generation of Topic Analysis is based on Large Language Models with rich contextual understanding and the ability to identify predefined and new, unexpected topics. It understands nuanced and implied topics, can handle complex, multi-topic conversations, and can create unique topic classifications for each call.
Contact centers traditionally used manual evaluations of a small number of calls to improve adherence to standards and best practices. However, this method was labor-intensive, time-consuming, and only covered a limited percentage of interactions (usually 2-5%).
Thankfully, with Generative AI-based Automated Quality Management (Auto Call Scoring), you can rely on AI to evaluate 100% of your relevant calls based on a customizable scorecard. This allows you to get deep and precise insights into agent performance. You can discover performance trends, identify coaching opportunities, measure training effectiveness, measure script adherence, and much more.
This doesn't mean you should stop manually evaluating calls. However, Auto Call Scoring enables you to do this much more efficiently. It automatically identifies calls that require human follow-up (e.g., agent performance was particularly bad), meaning you don't have to pick at random and hope for the best. Now, you can filter through your calls with purpose and identify those that make the biggest impact.
Auto QA is particularly powerful if combined with Sentiment and Topic Analysis as it lets you identify calls by sentiment (e.g., the customer was upset) and/or by topic (e.g., shipping problems).
Auto Call Scoring can not only save you an enormous amount of time and resources, but also give you much more detailed and accurate insight into what is happening in your contact center.
In a fast-paced contact center environment, there are multiple opportunities in a conversation for an agent to miss a piece of information, capture it improperly, or be distracted with note-taking and, therefore, not fully understand and solve the reason for the call.
Call summaries are crucial. Whether they are for auditing compliance for government or insurance entities, agent performance review, trend analysis, product research, etc., the summaries need to contain accurate and complete information.
This is where generative AI comes in. It can generate call summaries, automatically removing the human-error factor. Consequently, agents can focus on the caller instead of taking notes — all while the AI summarizes the pertinent information from 100% of calls.
Call summary solutions need to be based on Large Language Models or generative AI to capture nuanced information, understand call dynamics, and achieve deep contextual understanding.
Remember that certain solutions may offer predefined call summary formats, which provide predefined information and may have limited room for customization. Instead, look for a solution that allows you to customize your prompts. This allows you to focus on specific aspects of the call as well as generate different types of summaries for different users (e.g., agents, supervisors, executives).
Image: The screenshot shows MiaRec's Auto Call Summary feature. Using generative AI, MiaRec not only summarizes the call automatically within seconds but also extracts key facts and even suggests areas for improvement in the call notes.
With the AI doing the heavy lifting of call summaries, the agent spends significantly more time talking to customers instead of note-taking and admin tasks. This increases agent efficiency and reduces the amount of labor needed, which leads to cost savings and an improved customer experience.
"MiaRec's AI Quality Assurance solution has taken the guesswork out of manual call reviews while making the process automated and scalable. MiaRec has assisted us in identifying key areas for quality growth opportunities, standardizing a grading metric, and, most importantly, has allowed us to extract insights effortlessly that were not possible before. A highly recommended AI solution for any Quality Assurance call center."
Aldo Guzman
System QA Analyst & Data Reporting at isp.net
You are probably familiar with the saying that contact centers are "drowning in data, yet starving for insights." Contact centers collect an enormous amount of data every single day, but until now, it has been inaccessible and locked into audio files.
Thanks to Generative AI, you can now extract whatever insights you want from your calls — at scale. Here are just a few examples of the insights you can gain:
- Products Mentioned: Product and/or product category the customer called about
- First Call Resolution: Whether the concern was resolved in the first call
- Sales Opportunities: Identifies cross-sell or upsell moments
- Likelihood To Close: Determines how likely it is that a sales call results in a sale
- Churn Indicators: Flags at-risk customers based on negative cues
- Intent Recognition: Pinpoints customer goals or requests
- Script Adherence: Monitors if agents follow the prescribed dialogue
- Customer Feedback: Uncovers suggestions or complaints
- Process Bottlenecks: Highlights operational inefficiencies from recurring issues
The possibilities are literally endless. If your customers are talking about it in their calls, AI can extract it and report on it.
Image: The image shows a screenshot of Miarec's AI Insights feature. Here, the contact center wants to know the caller's customer status, whether the issue has been resolved (including an explanation), the likelihood of closing, the next best actions, the products mentioned, the product category, and the reason for the call.
Image: The screenshot above shows one of many ways you can report on AI Insights. This contact center reports on call reasons overlaid with sentiment analysis. This allows you to get a good feel for how your customers feel about the specific topics they are calling about and discover any shifts or trends early on. For example, if the sentiment of calls regarding order pacing suddenly swings from overwhelmingly positive to neutral or even negative, something is wrong and needs urgent investigation.
"Automated call quality evaluation scorecards will replace hours of manpower currently spent by several team leads performing these call evaluations manually. It would also provide a truer agent performance rating since all calls are rated, not only the ones that are randomly selected.
Victoria Johnston
Director of CX Operations at SymTech
In addition to the features mentioned above, please consider the following when choosing a Conversation Intelligence Platform.
Cost and Licensing
Transparency in pricing is crucial. To make sure you choose the right solution for your needs and that it aligns with your budget, make sure to understand the platform's pricing structure, including any licensing fees, potential add-ons, or hidden costs.
Return on Investment (ROI)
Investing in a Conversation Intelligence Platform should yield tangible benefits. Assess the potential return on investment, considering factors like improved operational efficiency and enhanced customer experiences. Engage with vendors that offer ROI calculators or assistance in building a compelling business case.
Prompt Designer
A Prompt Designer is one of the most critical platform features to look out for. A Prompt Designer enables you to create new or customize existing AI instructions. It also gives you a safe environment to test your new prompts without impacting your analytics. This will result in more relevant and specific responses and more usable data for your organization, specifically tailored to your needs. In addition, you can also use AI to extract all kinds of data from your call recordings. The potential for use cases is endless.
Image: The image shows a screenshot of Miarec's Prompt Designer. As you can see from the screenshot, you can create a new prompt or tweak an existing one on the left and instantly see the impact on the result by testing it on actual calls. All this happens in a safe environment, so your statistics and reporting aren't impacted by your testing.
Automatic Data Redaction
Data redaction is paramount in protecting sensitive customer information and maintaining trust. Contact centers often handle vast amounts of personal and confidential data, including financial details, health records, and other Personally Identifiable Information (PII), so it's essential to mask or remove this data to prevent potential misuse or breaches.
Implementing data redaction safeguards customers' privacy and helps contact centers comply with strict data protection regulations, thereby avoiding legal repercussions and upholding their reputation in the industry.
Image: The screenshot above shows how credit card numbers and the security code are automatically redacted in the transcript as well as in the audio file as part of Miarec's automatic data redaction feature.
Ensure that the data redaction is based either on a custom-trained Named Entity Recognition (NER) or Large Language Models (zero-shot training). These deep learning models are necessary for contextual redaction — that is, redaction by understanding context and intent. These auto-redaction solutions can identify and redact unstructured sensitive information and improve accuracy over time through adaptive learning.
Usability and Reporting
When selecting a Conversation Intelligence Platform, reporting and analytics capabilities are vital — after all, you are looking to purchase a Conversation Intelligence Platform for your contact center. Take the time to understand how the platform transforms data into actionable insights. What can you report on and how does it match your currently tracked KPIs? Consider what KPIs you wish to track in the future that weren't possible with your existing solution. Last but not least, how easy is it to share and visualize these insights? This will empower your team to make informed decisions, optimize processes, and enhance customer interactions based on real-time feedback and trends.
Image: The image shows a screenshot of a report on call reasons as part of Miarec's AI Insights feature. Rather than in a table or heatmap, this report is a pie chart visualizing how many calls come in about which topic.
A platform's usability can significantly impact its adoption rate. Opt for a solution with an intuitive, user-friendly interface. This guarantees that team members, regardless of their tech proficiency, can navigate and use the platform effectively, maximizing its potential benefits.
Security and Compliance
Data breaches are a question of when rather than if, which makes protecting the data privacy of your customers of paramount importance. The platform you choose should adhere to stringent data security standards, offering features like end-to-end encryption and robust access controls to safeguard sensitive information.
Different industries have specific regulatory requirements, especially concerning data handling and privacy. Ensure the platform you choose is compliant with these regulations, be it GDPR, HIPAA, or others, to avoid potential legal complications.
Support and Training
The quality of its customer support also determines a platform's value. Prioritize solutions that offer prompt, efficient, and knowledgeable support. This ensures that any issues or queries are addressed swiftly, minimizing disruptions. A platform's features are only as good as your team's ability. Opt for vendors that provide comprehensive training resources, such as webinars, documentation, or hands-on sessions, to ensure your team can leverage the platform to its fullest.
Future-Proofing
Your new platform should seamlessly integrate with existing tools and systems, such as CCaS, CRMs, and WFO tools. This ensures a cohesive workflow and maximizes the utility of all your tech resources.
As your organization evolves, your tech needs will, too. Choose a platform that can scale with your growth, accommodating more users, data, or advanced features as required.
The tech landscape is ever-evolving. Engage with vendors who have a clear roadmap for future innovations and updates. This ensures that your chosen platform remains at the forefront of industry trends, continually adding value to your operations.
We needed a platform that went beyond basic interaction recording. We wanted a single solution that would provide usable customer service insight. We needed analytics tools to measure performance and customer sentiment. Most of all, the data had to be organized, easily accessed, and easy to understand without having to become experts in analytics."
Jared Jenevein
IT Analyst - Companions & Homemakers