In the dynamic world of modern business, effective communication is the key to success. However, how can future customer communications and interactions be improved without analyzing the previous ones? Understanding and interpreting customer interactions can be challenging. This is where innovative technologies that evolved in the last years come in.

With the advancement of digital technologies and the increasing expectations of customers, businesses face growing pressure to deliver exceptional experiences. In this scenario, understanding the nuances of language and extracting valuable insights from customer interactions becomes essential for competitive differentiation and customer retention.

Moreover, with the spread of data generated with each interaction, companies and organizations can access a wealth of valuable information that can be leveraged to drive growth and innovation. This is where a feature like Speech and Language Analytics plays a fundamental role. BOTSchool is aware of this need from the market and is working to redefine how customers’ information is analyzed. In the following paragraphs, we will share our knowledge about speech and language analytics, including its benefits and use cases, and discover how this feature gains relevance with the evolution of artificial intelligence.

BOTSchool has always had analytical capabilities, allowing companies to understand the performance of their conversational tools when applied across voice and text channels. However, technological evolution brought new possibilities, with Generative AI and communication capabilities revolutionizing the way customers interact and information is treated.

BOTSchool now has the feature of speech and language analytics, which is based on the ability to transcribe recorded calls and perform subsequent analysis to gain a broader understanding of the interactions between the company and the customer. Besides, we can make this analysis with other types of communication, such as emails.
In general, there are two ways to do this analysis:

• The process of speech analytics can occur simultaneously with the interaction, with software transcribing the call in real-time and providing immediate data to the human agents handling the call.
• It can occur in deferred mode, where the conversation is recorded, transcribed, and subsequently analyzed, leading to the processing of information about the conversations.

Both forms are useful for customer service areas to incorporate improvements and for the business to anticipate customer queries.

Speech and language analysis tools collect audio conversations that contain unstructured data and convert them into a structured and comprehensive format that organizations can easily search and analyze. This process helps businesses to identify the reasons for customer churn and provide effective analysis to promote business growth.

• Call recording

Most call centers or contact centers currently record calls for quality purposes, which include exactly this exhaustive analysis of the audio.

• Transcription

Application of mechanisms such as speech-to-text, speech recognition, and Natural Language Understanding (NLU) to transform a recording into a text to gain access to conversation content more visually.

• Transcription Analysis

After the actual transcription, new intelligent mechanisms come into the action of the conversation, such as call quality in qualitative terms, agent performance involved, etc.

What are the benefits of applying conversational analytics to the customer service area of your business? In the current business landscape, where competition is fierce and customer expectations are constantly evolving, companies relentlessly seek ways to stand out and deliver exceptional service. In this quest for excellence, speech and language analytics emerges as a tool offering a range of tangible benefits:

• Enhanced Customer Experience

Understanding customer needs and concerns in real-time allows for a more agile and personalized response. Data shows the following customer’s behaviors:

“Seventy-five percent of consumers are “very likely” to forgive a company for a mistake if they think it delivers “very good” CX (…)”. However, if the experience is not satisfactory, “(…) only 14% of consumers are “very likely” (…)” to forgive that error.

• Increased Productivity

Rapid access to relevant information during and after interactions enhances agent efficiency and reduces average handling time. The reality is that the implementation of “(…) call analytics tools (…)”, which include systems for speech analytics, and sentiment analysis, among others, “(…) can reduce average handling time by around 40%.”.

• Chatbot Enhancement

By training chatbots based on patterns identified in interactions, responses become more precise and helpful.

• Cost Reduction

Automation of analysis tasks and trend identification results in time and resource savings.

Despite the importance of understanding the benefits they bring to various stakeholders, it becomes even more relevant to understand how they work in practical cases. Cases that have truly been proven to yield satisfactory results for companies. Some of these, and those that BOTSchool itself can guarantee expertise in, are as follows:

• Information Retrieval

Identifying trends and important information in a large volume of data.

By having a single audio or a set of audios, which is more likely, we can upload them to the platform and start a series of analyses from there, gaining access to the information present in these audios in a very simple way. The audios are first transcribed, and then we can learn through a concise summary what the call was about. If we have doubts, we can ask generative chat questions about the audio (e.g., “Who spoke?”, “Was the issue resolved?”, “Did the customer leave satisfied?”, “Did the agent manage to solve the problem?”, among others that may be necessary).

Information retrieval on large volumes of data has never been as accessible as now with the new language models that understand and respond much more quickly. Additionally, there is another advantage related to the possibility of having a dashboard with more functional information, showing metrics about the call itself.

• Sentiment Analysis

Understanding customer sentiment towards the brand to adjust strategies.

As mentioned in the previous point, the use of this tool is increasing in organizations that want to gain insight about their users. There is a concern about how the contact ended – did the customer leave satisfied? Did they start upset and then become more satisfied after getting a response? Did they remain dissatisfied, and there was nothing that could be done?

Perceiving these feelings and how they evolve is valuable information for managers involved in customer support because they can adapt future responses and future employees to use mechanisms more aligned with the general sentiment of the people who make contact.

It is increasingly important to acknowledge these sentiments, recognizing what is failing and what is succeeding, to determine future action points.

• Increase cross-selling and up-selling

By conducting a thorough analysis of calls and understanding which ones yielded better results in terms of sales, we can determine the winning behaviors that resulted in sales from these successful calls.

Comparing calls that resulted in new business with those that had a sales attempt but did not result in business leads to the comparison of behaviors such as the success of certain terms and phrases, the better duration of a call, and the balance of agent talk time versus customer talk time. All these factors will allow the creation of more effective sales scripts.

Furthermore, in addition to this advantage, we can obtain important insights related to the products that a specific audience is more inclined to adopt and, therefore, recommend these products or services to pre-defined groups of customers.

• Training Material Creation

Utilizing insights from interactions to develop more effective training materials for customer service agents.

Human agents (and even conversational agents like chatbots) need to be trained! They require training and materials with information on how the processes of a particular company work. If we know in advance what is wrong with customer service or any internal process, it becomes easy to create materials.

Generative AI can quickly create not only text-based content but also images or videos capable of meeting the changing needs regarding the training that should be provided to employees.

We can even have this training material delivered through a chatbot or virtual assistant, which is trained frequently and can also answer the questions of employees themselves, saving time and money for the company.

Speech and Language analytics are a feature capable of revolutionizing how companies interact with their customers. At BOTSchool Next Generation, we are committed to constantly evolving this capability and aim to incorporate the latest advances in AI to provide businesses with a solid tool in this area.

Speech and Language analytics, combined with transcription and natural language understanding capabilities, immediately offers an advantage for understanding the interactions that have occurred. When combined with generative capabilities to easily search for information, it becomes the perfect combo.