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Learn the difference between conversation and conversational intelligence and how they can improve the customer experience.
“85% of employers say they directly benefit from AI in the workplace” –MIT Sloan Management Review
The difference between conversation and conversational intelligence and how they can improve the customer experience.
In the world of artificial intelligence and advanced communication technologies, the terms “conversation intelligence” and “conversational intelligence” might sound similar, but they have distinct concepts that can significantly impact various areas of human-machine interactions. While they both revolve around conversations, it’s crucial to understand their differences in order to truly comprehend their value for businesses of any structure. That’s why in this blog post, we’ll dive into the differences between conversation intelligence and conversational intelligence, shedding light on their different uses and applications.
Conversation intelligence primarily focuses on leveraging data and analytics to gain insights from human-to-human conversations. It involves capturing and analyzing conversations using advanced technologies, such as natural language processing (NLP) and machine learning algorithms. By examining various aspects of a conversation, such as tone, sentiment, keywords, and speech patterns, applications that have conversation intelligence can extract valuable information and optimize future conversations.
Businesses can utilize conversation intelligence platforms to enhance their sales and customer service strategies. These platforms enable companies to record, transcribe, and analyze customer interactions, providing valuable data for evaluation and improvement. Insights gained from conversation intelligence can help identify areas of improvement, such as identifying the need to enhance agent training programs, and even identify potential sales opportunities that might have been overlooked.
According to the SearchEngineJournal, only about 1-3% of recorded business calls that are recorded are manually scored by analysts. Since conversation intelligence records and tracks every call that comes through, these tracking numbers can be raised to 100% and identify weak aspects of business calls that might have been missed by human analysts.
Based on a study conducted by Forrester Consulting, 89% of marketers have stated how using AI conversation intelligence software is crucial to staying competitive in their business industry.
A big part of the job as a sales representative is communicating the value of the products and services being sold through sales calls. Conversation intelligence platforms can automatically record, transcribe, and analyze hours of sales calls. The platforms can help sales reps identify effective keywords and deliver insight into what types of customer interactions are effective and not. According to a survey by Gartner, 51% of sales organizations have deployed or plan to deploy customer interaction platforms into their everyday operations in the next five years.
According to a study conducted by Invoca, the company’s customer interaction software can produce an ROI of 395% over three years and a payback period of fewer than three months. Using their business’s conversation intelligence platform, the company experienced a 10% increase in inbound sales calls as well.
When using conversation intelligence platforms, business analysts can feel comfortable recording customer interactions even if the conversation is conducted in another language from their own. This is because of the intelligence platform’s ability to transcribe conversations into any language for easy deciphering and analysis.
It can be difficult to track the effectiveness of a business or business-to-customer conversation when multiple people are participating in the call or meeting. That’s why another great feature of conversation intelligence platforms is the ability to identify individuals in the call based on the uniqueness of their tone and voice. Voice recognition can come in super handy when you want to identify the main speaker of the group to analyze their leadership skills or rate how well a team can communicate with each other.
Conversational Intelligence, on the other hand, centers around the ability of machines to engage in natural and meaningful conversations with humans. It encompasses the development and application of conversational agents, chatbots, and virtual assistants that can understand human language, respond appropriately, and even simulate human-like conversations. The goal of conversational intelligence is to create systems that can effectively communicate and assist humans in various fields.
This form of intelligence relies on advanced technologies such as natural language understanding (NLU), dialogue management, and context awareness. It enables machines to comprehend user queries, extract relevant information, and generate accurate responses. Whether it’s a voice-activated virtual assistant helping with daily tasks or a chatbot providing customer support on a website, conversational intelligence plays a crucial role in enhancing user experience and efficiency.
The conversation starts when a user interacts with the platform through a chat interface, voice commands, or other input methods. The user’s input can be in the form of text, speech, or even gestures, depending on the platform and its capabilities.
The conversational platform utilizes NLU techniques to parse and comprehend the user’s input. NLU involves breaking down the input into meaningful components, extracting important entities, and understanding the user’s intent or the purpose of the query.
Once the user’s input is understood, the platform identifies the user’s intent—what the user wants to achieve or the action they want to perform.
Conversations often have context, and conversational platforms aim to maintain and track that context to provide relevant and coherent responses. Context includes information from previous user inputs, user preferences, and system state. By considering the conversation history, the platform can offer more accurate and personalized responses.
To fulfill the user’s request, the conversational platform may need to integrate with various backend systems or APIs. For example, if the user asks for the latest news headlines, the platform may connect to a news API to gather relevant information. This integration allows the platform to provide real-time and dynamic responses.
Based on the user’s intent and the information gathered, the platform generates an appropriate response. The response can be in the form of text, speech, or a combination, depending on the interface used. The generated response is designed to be human-like, engaging, and relevant to the user’s query.
Conversational intelligence platforms often incorporate feedback loops to continuously improve their performance. User feedback, such as ratings or explicit corrections, can be used to refine the platform’s understanding and response generation capabilities. Machine learning techniques are employed to adapt and enhance the platform’s performance over time.
When companies are experiencing heavy flows of customer traffic and customer support/help desk tickets, conversational intelligence in the form of chatbots is a cost-effective solution to help customers instead of hiring or contracting customer service agents. According to Ring Central Blog, it can cost up to $31000 to hire a new customer support agent. Employing conversational intelligence agents can help eliminate these costs.
According to Zendesk Blogs, when onboarding customer service reps, new employees are put through a training program that usually lasts between four and six weeks in length. Since conversational intelligence tools already come equipped with the information and ability to help customers, this learning curve training period can be reduced.
Using Google Dialog Flow, businesses are able to employ chatbots, virtual assistants, and conversational agents on nearly any platform a business operates on whether that be your company’s website, mobile app, or messaging platforms like Facebook Messenger and Slack. The exciting fact about Google Dialogflow (like most conversational intelligence platforms) is how its use of machine learning technology gives it the ability to continuously improve its language and response processing over time. Additionally, Google Dialogflow allows the integration of many other Google-based tools such as Google Cloud and Google Analytics.
Microsoft Bot Framework is another conversational intelligence platform that offers users the ability to customize and build their own chatbots. The platform offers a library of development tools while supporting multiple programming languages (such as C# and Java) to build your chatbots with flexibility and ease. Additionally, the ability to integrate Azure service (Azure Cognitive Services and Azure Bot Services) into Microsoft’s framework allows users the ability to customize and create chatbots with advanced features such as data storage and speech recognition. This platform has a large community of developers that provide learning resources and libraries so users can find the answers they need when facing any challenges while using the platform.
“Einstein bots can reduce support cost by 29% and increase customer satisfaction by 31%” -Salesforce Admins
The final conversational intelligence platform on this list is Salesforce Einstein Bot. One of the best features of the platform is its ability to integrate with Salesforce’s CRM software. This gives chatbots the ability to access customer data from the main SalesForce database, enabling the bots to give personalized and more custom answers to users’ questions. One of the platform’s most interesting features is its Prediction Builder tool. Using this platform, users can create custom AI predictions based on data stored in your SalesForce database. Some of these predictive features include the best time to contact a customer, the likelihood that a customer buys a product, churn risks, lead scores, and revenue potential.
Chatbots can act as great chat assistants for those who need help booking their next vacation. It’s reported that 75% of travelers may rely heavily on chatbots when planning their travel arrangements.
Chatbots in conversational intelligence platforms can be used in the real estate industry to quickly respond to customer queries about open listings and property updates. According to an article by Chatbots.org, web engagement on a site can be boosted X3 when a chatbot is present.
Conversational intelligence platforms can help e-commerce businesses provide top-notch customer service to users. The platform can be used for functions such as customizing customers’ product recommendations and tracking shipment orders.
Conversational intelligence platforms can be invaluable to customers when they are used on medical websites to help patients book appointments, recover patient data, and answer FAQs (frequently asked questions).
While both conversation intelligence and conversational intelligence involve actions taken to improve the customer experience, it’s essential to recognize their distinctions. A summary of their differences can be seen below.
Conversation Intelligence concentrates on analyzing human-to-human conversations, whereas conversational intelligence is concerned with enabling meaningful interactions between humans and machines.
Conversation Intelligence revolves around data analysis, extracting insights from conversations, and improving human-to-human communication. Conversational Intelligence, however, emphasizes the development of intelligent systems (such as chatbots) capable of engaging in conversations with humans.
Conversation Intelligence can be found in finds applications used in industries that involve sales, customer service, and team communication. Conversational Intelligence, on the other hand, is applied in virtual assistants, chatbots, and other human-machine interaction scenarios.
Conversation Intelligence employs technologies like NLP, machine learning, and data analytics. Similarly, conversational intelligence relies on NLU, along with dialogue management, and context awareness to enable human-like interactions.
With the use of chatbots expected to double over the next two to five years, some may question whether humans in customer service and business analyst jobs will be replaced. Though these platforms have some pretty impressive capabilities, fortunately, they are not expected to fully replace humans. However, they can eliminate repetitive job functions in customer service roles such as answering common questions or providing basic customer support like password resets. By automating these repetitive tasks, intelligence platforms can free up the time and resources for customer service agents and analyst to complete more complex and valuable work that better improve their organization.
Overall, the concepts of conversation intelligence and conversational intelligence have distinct scopes and applications. Conversation Intelligence harnesses data analytics to extract valuable insights from human-to-human conversations, while conversational intelligence focuses on developing intelligent systems that can interact with humans seamlessly.40
By understanding these differences, businesses, and developers can leverage these concepts more effectively to enhance communication, improve customer experiences, and drive innovation in these exciting times of artificial intelligence.
Dattatraya Shetty is an IT Professional with 2 Decades of experience in areas of Product Development, Implementation & Service Delivery Management. As the Head of Implementations and SOC Compliance in Smartkarrot he is on a mission to provide relishing customer experience.
Published May 11, 2023, Updated May 11, 2023
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