Companies that aim to grow and break barriers need modern contact centers solutions that enable direct and transparent communication with their customers while automating tasks that intelligent tools like chatbots can handle. These solutions should, overall, be able to collect data from interactions between the company and the customer, as it is from this data that true transformation occurs, allowing for the optimizations of the future interactions.

The trends suggest that the demand for optimized contact center solutions integrating digital and physical channels will continue to grow. A compound annual growth rate (CAGR) of approximately 13.6% is expected between 2022 and 2030.

The synergy between digital channels and traditional voice channels boosts the collection of raw data, containing valuable insights that businesses can leverage to implement necessary changes, improve interactions, and enhance their operations. The analysis of these interactions was made possible by discovering a new approach with the advent of the new era of Artificial Intelligence (AI).

The concept of interaction analytics refers to the ability to transform qualitative and unstructured data from interactions into quantitative metrics and insights, as well as to perform an in-depth analysis of customers’ sentiments and emotions. This raw data contains valuable information about what works or does not work in each type of interaction, and the insights gained from these analyses can be a significant asset for companies adding a predictive component to the customer service approach.

The interactions analyzed can come from various customer touchpoints with the company. Traditionally, phone calls were the most common way to contact an organization to resolve an issue or answer a query. However, with the emergence of new forms of contact and the popularity of social networks and messaging apps, we now have multiple new touchpoints – such as email, SMS, social media, chatbots, and messaging platforms. With these new methods of contact, customers have started to resolve their issues or seek answers via them. Whether through support emails or public comments on the company’s social media, these new ways of achieving the same result have led to the need for monitoring these channels since they contain information that is highly relevant to organizations and can provide a broader and more precise view of customers’ needs and expectations.

All the mentioned channels offer a wide opportunity to identify areas of improvement across several layers, such as:

• Product/service quality and characteristics;
• Customer journey;
• Efficiency and quality of the company’s customer service;
• Response time and its alignment with customer expectations;
• Agent empathy;
• Internal call management processes.

Voice of the Customer (VoC) concept is at the heart of next-generation customer support, which seeks to develop by leveraging real data. Traditional methods like satisfaction surveys are still useful but fail to provide a holistic view of interactions across multiple channels like SMS, emails, and social media. Thus, with the emergence of new technologies, these previously used methods can be complemented to offer a broader view to decision-makers.

Natural Language Processing (NLP), Natural Language Understanding (NLU), and Sentiment Analysis models have emerged over the past few decades to capture real-time feedback and better understand customer expectations.

The real revolution has come with the rise of Generative Artificial Intelligence (GenAI). This technology can transform various areas within companies, including contact centers. The new generation of AI is being used to fill gaps in automation and self-service initiatives, while also processing and analyzing interaction data, providing valuable insights that enhance customer service and experience.

Interaction analytics works by monitoring and analyzing customer touchpoints with the brand. These touchpoints include traditional IVR, digital channels, and intelligent conversational tools. The collection of these data points allows for the attribution of both qualitative and quantitative values. Let us look at the process step by step:

• Data Collection – recording and transcription, capturing every moment of interaction between the company and the customer. This includes call transcription and logging interaction on other channels like emails, chats and social media.
• Text Analysis – using Large Language Models (LLMs) designed to analyze large volumes of text and generate appropriate responses, this stage identifies keywords, recurring topics and sentiments expressed in interactions.
• Behavior Analysis – through AI, it is possible to identify behavioral patterns such as tone of voice, intent, empathy, active listening, upsell attempts, responsiveness, compliance adherence and improve customer experience.
• Metrics – the resulting metrics, such as satisfaction rate, response time, and problem resolution, are presented in intuitive dashboards, allowing for quick comprehension of the detected results.
• Feedback – based on the generated metrics, contact center managers and decision-makers can implement process improvements, automating corrective actions and adjusting customer service strategies.

The reasons for implementing an interaction analysis platform can be divided into four major categories, which affect different stakeholders and have effects in a transversal way:

• For the Company – the competitive advantage that companies gain by applying innovative systems to analyze customer needs is significant. Anticipating future problems based on current interactions allows companies to differentiate themselves in the market, promoting sustainable growth and long-term success.
• For the Customer – in an increasingly customer-centric business environment, interaction analytics is crucial to supporting a customer-focused approach. Offering customer service that not only solves problems but anticipates them enhances the customer experience (CX) and helps attract and retain new audiences.
• For the Employees – both contact center employees and those in other company areas benefit from the insights generated by these platforms. Agents can receive targeted training on how to approach customers effectively, while other departments can adjust their touchpoints to improve the overall experience.
• For the Managers – managers gain a more comprehensive and accurate view of the challenges customers face when they rely on active listening data. By understanding customers’ pain points, sentiments, and feedback on products, services, and customer service, managers can define strategies more aligned with market needs and make more informed decisions.

BOTSchool offers highly refined analytical capabilities, further enhanced by the application of GenAI and LLMs on the platform. This enables us to provide businesses with tools for transcription, interaction analysis, summarization, diarization, smart chat with transcription, and other relevant data. All of this is presented in an intuitive interface with clear dashboards showing various metrics extracted from raw data.

We continue to expand our platform to meet your companny’s needs, always considering the latest intelligent technology to drive business growth.