Five Key Considerations for Selecting an AI Conversation Analytics Software​

Five Key Considerations for Selecting an AI Conversation Analytics Software

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Conversation Analytics: An Overview

AI-powered conversation analytics is revolutionizing the business landscape by extracting valuable insights from customer interactions. This innovative technique involves comprehensive analysis, employing methods like keyword spotting and sentiment analysis. It encompasses data extraction from both text and voice interactions, utilizing artificial intelligence (AI) to convert natural language into machine-readable formats.

This technology’s core lies in leveraging AI to transform customer conversation data into actionable insights. Critical components like text analytics, speech analytics, voice analytics, and sentiment analytics offer nuanced insights into customer interactions, enabling businesses to enhance satisfaction and identify improvement areas. Beyond customer support, conversation analytics finds applications in diverse industries, predicting customer behavior and informing strategies across the buyer’s journey.

We’ve listed 5 important factors to take into consideration when evaluating and selecting an analytics software, so you can make the right choice for your business.

For contact center speech analytics to be truly effective, it is essential for the solution to accurately comprehend both your agents and customers. The system should possess the capability to automatically recognize language, including specific dialects, and support the languages currently utilized by your agents and customers, while also accommodating future language needs. Furthermore, the interaction analytics solution should adeptly identify and understand customer intent, utilizing machine learning to continually refine and optimize algorithms, ensuring the highest level of accuracy in predicting true intent.

Additionally, the solution should automatically pinpoint and segment key classifying elements within a conversation, associating them with relevant categories to provide context and factual information to an intent. For instance, your contact center analytics solution should autonomously identify the initial greeting, identify critical issues and intents, and accurately document the outcome or resolution through precise sectioning capabilities. Leading solutions leverage machine learning to guide the technology in the recognition and classification of these elements.

To gain profound understanding of the customer experience, look for a contact center analytics tool capable of analyzing both spoken and written communication. This will provide you with a cohesive perspective of customer interactions spanning various platforms, such as voice calls, emails, and chat messages.

The optimal choice of automation for a business hinges on various factors. For larger enterprises, particularly in sectors like healthcare, conversational AI proves advantageous due to its heightened sophistication. For instance, healthcare organizations might prefer conversational AI when aiming to streamline processes such as patient registration, appointment booking, or creating a medical virtual assistant for symptom checking.

Conversely, rule-based chatbots are well-suited for providing basic support to smaller enterprises. When appropriately programmed, these chatbots can handle frequently asked questions, track orders, provide updates, and execute routine tasks, thus freeing up valuable time for human agents. This is exemplified by solutions like Tidio, which, with its lower overheads and customizable templates, automates a significant portion of customer inquiries.

The conversation intelligence software you intend to utilize should be compatible with your existing business systems and tools you use for your contact center. In the absence of integration, there’s no need to worry. Most providers are willing to integrate with your business software upon request. However, it’s crucial to establish a clear timeline and receive commitments for support during the integration process. The basic integration should involve automatically connecting with your contact center provider, extracting call recording data, conducting transcription and analysis, and presenting the final insights effortlessly.

Complex integrations may not be necessary from the outset. There’s no value in paying extra for complexity that won’t be utilized initially. Therefore, assess your needs accordingly. Furthermore, regardless of whether a conversation analytics software integrates with your third-party software, always secure an upfront support commitment, especially during the on-boarding phase.

It’s common to be carried away while exploring vendor websites, and these providers are adept at showcasing features that, while appealing, might not significantly impact your specific use case. Be cautious about engaging in product demos with providers who focus solely on displaying their preferred product capabilities. Instead, insist that the representative demonstrates only the analytic features relevant to your needs.

When evaluating potential vendors, consider requesting references that can vouch for the tool’s effectiveness in a similar context. References within the same industry or vertical are particularly valuable. Seek firsthand experiences from organizations of similar size that operate in related industries and analyze comparable data types. The goal is not to choose the overall best tool but rather to select the most suitable tool for your very specific circumstances.

Start Your Conversation Analytics Journey Here

Contact center analytics unveil pertinent and actionable insights hidden within extensive amounts of unstructured voice and text dialogues within the contact center. By transforming this abundant source of information about the customer and agent experience into profound comprehension, your company can enhance compliance and quality, reduce management costs associated with compliance and quality, elevate agent performance, optimize the customer experience, enhance satisfaction, and pinpoint needs and trends that contribute to product and service development.

Selecting the appropriate solution start with delineating the use cases, formulating a robust business case, understanding the necessary capabilities, and meticulously assessing your alternatives. We at Born Digital are available to assist you as you progress in your journey with conversational AI. Get in touch with us now.

Benefits of Conversation Analytics in Customer Service

Benefits of Conversation Analytics in Customer Service

Happy customer on the phone
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Why do you need the conversation analytics?

With 60 % of consumers choosing brands based on service expectations, how you engage with your customers can set you apart. This makes every interaction with customers a golden opportunity to stand out. It’s all about understanding their needs, frustrations, and perceptions. Remember, it only takes one bad experience for more than half of your customers to consider jumping ship!

But how can you identify key pain points and see where you might be losing your valued customers? How do you monitor and evaluate the performance of your customer service agents? The answer is AI-powered conversation analytics which is a powerful tool to transform your customer service approach and build lasting loyalty. By integrating conversation analytics into your strategy, you’re not just solving problems in your processes or custoemr experience, you’re enhancing the whole customer journey.  Let’s break down the key benefits of this data-driven approach:

Reducing costs with real-time analysis

Having a real-time conversation analytics is like having a bird’s-eye view of every conversation, helping you quickly identify trends and areas needing attention. Is it changes to billing details, trouble logging in, or questions about claims that are on your customers’ minds? Figuring out what your customers struggle with the most and the main reasons they reach out to you is key. 

Once you’ve got this insight, you can take meaningful action. Streamline your processes, introduce automation, and give your agents the training they need. This way, you could cut down on unnecessary interactions by as much as 30%.

Spotting Improvement Opportunities

Conversation Analytics goes beyond mere observation; it’s a tool for proactive improvement. By transforming each interaction into a learning moment, our clients have pinpointed areas for process enhancement and automation, leading to the automation of up to 53% of interactions. Our mantra to customers is to analyze first, automate second. Premature automation can be costly and damaging to your brand if it’s applied to processes that don’t need it.

Moreover, sentiment analysis at both the start and end of each interaction acts like an emotional barometer, giving you insight into the customer’s feelings. Is their frustration directed towards a specific agent, or is it about your delivery terms? Once identified, these insights empower you to make targeted, effective changes.

Improving Customer Experience and Loyalty

Conversation Analytics is pivotal in boosting customer experience and loyalty. It identifies critical areas needing improvement in customer interactions. For example, if it reveals a frequent issue of unclear information regarding product features, you can enhance training for your team to provide clearer, more comprehensive explanations.

This targeted action leads to better-informed and happier customers, fostering loyalty as they recognize your commitment to addressing their concerns and improving their overall experience. With the conversation analytics, our customers managed to increase the NPS score by 20 %.

What can you analyze?

  • Customer calls

The Conversation analytics allows you to filter the call interactions based on their sentiment, resolution time, and so much more. The days of picking 5% of customer-agent interactions to evaluate your whole contact center’s performance are over. The technology is much more precise in evaluating and analyzing these calls than humans are. Your customer sercive managers can filter only the calls with the worst sentiment and add the manual evaluation where needed. 

Plus, this tool helps check how closely agents stick to the call script. You can see if they’re properly introducing themselves, attempting to upsell or cross-sell, and even monitor their speaking pace.

  • Chat conversations

The tool extends to chat interactions too, offering real-time insights into how your agents chat with customers. This enables immediate actions, like launching a chatbot flow or posting an informational banner on your website, to cut down on unnecessary queries. 

  • Interaction with digital agents 

Just like with human agents, you can analyze how customers engage with your voicebots and chatbots. It lets you tweak conversation flows, change introductory lines, and much more, enhancing AI-powered interactions.

  • Email communication

For many companies, email is still a key communication channel. Dealing with thousands of emails? This tool sorts, prioritizes, and identifies which emails could be automated or pre-processed, streamlining your email management significantly.

How difficult is the implementation?

The implementation of our Conversation Analytics tool is remarkably straightforward and efficient. A Proof of Concept (POC) can be set up in just a few hours, requiring no training data or extensive setup processes. Clients have the autonomy to upload their conversation data, such as call recordings, directly into the system. This user-friendly approach means that you don’t need our constant involvement to get started.

Once uploaded, the data is immediately available in a pre-defined, easy-to-navigate dashboard. While this dashboard is designed for simplicity and immediate use, it also offers flexibility. Clients can customize the dashboard to suit their specific needs, ensuring that the analytics and insights align perfectly with their unique business requirements and goals. This blend of ease and customization makes the tool not only accessible but also highly adaptable to diverse customer service environments.

Starting Your Analytics Journey

1. Goal Identification

The first step in implementing Conversation Analytics is to clarify your business objectives. Are you aiming to enhance customer experience (CX), boost your Net Promoter Score (NPS), or cut down operational costs? Setting clear goals will guide the entire process and ensure that the analytics serve your specific needs.

2. Metric Selection

Next, decide what metrics you need to track. These could include first call resolution times, overall sentiment, or agent performance metrics like adherence to upselling or cross-selling strategies. Pinpointing these metrics will help focus the analysis on areas most impactful to your goals.

3. Team Training and Data Analysis

It’s essential to familiarize your team with the new tool. Analysis without understanding or observation is futile. Inform your agents about the analytics in place and how they can leverage this data to better meet customer needs. This step ensures that your team is prepared to use the insights effectively.

4. Implementation of Insights

Finally, be ready to act on the insights you gather. If you identify opportunities for improvement, embrace them. Whether it’s automating certain processes or introducing innovative solutions, taking decisive steps based on your analytics can lead to significant improvements in service quality and efficiency.

To sum it up, your company really can only benefit from the Conversation Analytics. If you’re ready to start analyzing and measuring the success of your customer service, Born Digital platform will definitely be a great starting point. 

Rule-Based vs. AI Chatbots: Key Differences

Rule-Based vs. AI Chatbots: Key Differences

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AI vs. Rule-Based Chatbots: An Overview

The ever-growing impact and market prevalence of chatbots in the business landscape cannot be denied. AI assistants play a pivotal role in assisting customers and empowering customer service specialists across a myriad of industries.

Adopting chatbots presents a significant advantage, enabling cost savings of up to 30%. While this statistic alone can sway traditionalists into becoming enthusiasts, a singular high percentage embedded in a vague promise of profit may not suffice to convince every stakeholder, nor should it. For business owners contemplating a transformation in their core department’s daily operations, it’s not a hasty decision. Numerous technical and business questions require clear answers, including the choice between a conversational AI bot or a rule-based chatbot. Let’s delve deeper.

In a simplified sense, the primary distinction between conversational AI and rule-based chatbots lies in their ability to emulate human conversation. Conversations with AI chatbots feel more natural and fluid, whereas rule-based chatbots may come across as robotic or even unintelligent. Linguists may argue that the distinction oversimplifies, as rules govern not only chatbots but also natural human conversations. Nevertheless, when it comes to conversational bots, the nuances are more apparent.

Conversational AI bots leverage artificial intelligence, machine learning, and natural language processing to be more accurate, intelligent, and proficient in answering a wide range of questions. This heightened complexity allows them to function as genuine virtual assistants, surpassing the capabilities of their rule-based counterparts in almost every aspect, though not without certain drawbacks, as we’ll explore later.

On the other hand, rule-based chatbots operate based on scripted conversation models. Their capabilities in understanding and responding to text prompts are confined to the script’s scope, though they can be programmed to respond to specific keywords. They are quick to set up and deploy, meeting basic needs like order tracking or providing general information.

Choosing Rule-Based Chatbots for Your Business

When selecting a chatbot solution, it’s crucial to evaluate its suitability for your intended purpose. Rule-based automation, limited in its capabilities, generally thrives in smaller businesses, websites, and organizations.

Advantages of Rule-Based Chatbots

  1. Cost-Effective: Implementing a rule-based chatbot is budget-friendly, with providers like Manychat asserting that automation can significantly boost revenue generation.
  2. Ease of Implementation: Rule-based chatbots, operating as flowcharts, don’t require language model training. They simplify the user journey, allowing clients to guide conversations by selecting specific buttons like “Pricing” or “Opening hours.” Once the ruleset is established and the bot connects clients with agents, it’s ready for use.
  3. Templates: Customizable, ready-made templates, facilitating easy adaptation for small businesses.
  4. Time Savings: While their capacity to handle customer service or e-commerce aspects is limited, rule-based chatbots still reduce the workload on human employees, enabling them to concentrate on more intricate tasks.

Disadvantages of Rule-Based Chatbots

  1. Lack of Personalization: Unlike conversational AI bots, rule-based counterparts lack the ability to “remember” users and track conversations for future reference, potentially resulting in unfavorable customer perceptions.
  2. Limited Use Cases: Rule-based chatbots cannot adapt dynamically and rely entirely on pre-programmed scripts. Users posing questions beyond this scope may hit a figurative wall, unaware of the chatbot’s limitations.
  3. Poor Fit for Larger Businesses: Due to their flowchart-like functionality, rule-based chatbots struggle with solving complex issues. Larger corporations, offering services beyond simple tasks like parcel tracking, may witness customers frequently resorting to the “transfer to agent” option, rendering their customer service bot obsolete.

Choosing AI-Powered Chatbots for Your Business

Conversational AI chatbots possess a distinct advantage over their rule-based counterparts: intelligence. Leveraging Machine Learning and Natural Language Processing, these chatbots comprehend user prompts and respond dynamically, eliminating the need for preloaded scripts. This results in more natural and fluid conversations for users, offering several advantages:

Advantages of AI-powered Chatbots

  1. Multilingual Support: Conversational AI’s capacity to respond in multiple languages, given appropriate training datasets, is a significant advantage.
  2. Advanced Customer Service: Machine learning applications elevate chatbot support to nearly human levels, enhancing the overall customer experience. Virtual assistants utilizing AI can handle complex issues, making them ideal for large organizations such as blue-chip companies.
  3. Enhanced Data Protection: Unlike rule-based chatbots often deployed on external clouds, conversational AI typically operates on internal servers or private clouds, providing additional protection against data theft—especially crucial for sectors like banking or healthcare.
  4. Consistent Responses: Entity recognition enables AI-powered chatbots to remember conversation context, retaining information about the client’s previous questions or concerns for easy reference.

Disadvantages of AI-powered Chatbots

  1. Longer Setup: Implementing conversational AI requires significantly more time compared to rule-based chat assistants. Proper setup often involves coordination among various team members, including the IT team, DevOps, and testers.
  2. Customer Apprehension: Despite the sophistication of AI companions, some customers remain hesitant to interact with machines. While the preference for chatbots is increasing, with 62% of customers favoring them over waiting for a human, companies in the AI sector need to work on improving the image of conversational artificial intelligence.
  3. Resource Intensive: Achieving a human-like chatbot experience and fully utilizing NLP demands substantial resources to train the AI to respond to diverse situations. This results in increased upfront costs before the chatbot can effectively assist customers.

Use Cases for AI-powered and Rule-Based Chatbots

The optimal choice of automation for a business hinges on various factors. For larger enterprises, particularly in sectors like healthcare, conversational AI proves advantageous due to its heightened sophistication. For instance, healthcare organizations might prefer conversational AI when aiming to streamline processes such as patient registration, appointment booking, or creating a medical virtual assistant for symptom checking.

Conversely, rule-based chatbots are well-suited for providing basic support to smaller enterprises. When appropriately programmed, these chatbots can handle frequently asked questions, track orders, provide updates, and execute routine tasks, thus freeing up valuable time for human agents. This is exemplified by solutions like Tidio, which, with its lower overheads and customizable templates, automates a significant portion of customer inquiries.


It’s essential to recognize that, irrespective of their technological foundation, no chatbot can universally serve as the definitive solution for every business. Before making a decision, careful consideration of the aforementioned pros and cons in alignment with your business and customer needs, as well as your specific goals, is crucial. By weighing these factors against the implementation and maintenance costs of the chatbot, you can arrive at a well-informed choice that aligns with your unique requirements.

Conversational AI in the Energy Industry – Challenges and Use Cases  

Conversational AI in the Energy Industry – Challenges and Use Cases


The energy industry is high-stakes, meaning that providers face a constant demand for quick, accurate, and reliable services in every interaction. The potential for power outages or urgent queries for meter readings are very different customer needs that can be streamlined with conversational AI. 1/5 of energy customers express dissatisfaction with the time it takes to connect with their provider, let alone receive a prompt response. Luckily, Born Digital is an innovator in the field of conversational AI, and can bring the latest AI capabilities to your energy business. Let’s explore how easy it is to enhance and streamline your overall customer service experience with Born Digital’s technology.  

First, let’s identify common issues energy providers face on their customer service contact channels:


Issue 1: Contact Center Overload in Times of Mass Outages 

In the energy industry, mass outages create a significant challenge for CX providers, impacting the quality of service at contact centers. This results in dissatisfied customers and overwhelmed CX representatives. Born Digital’s cutting-edge AI technology offers a strategic solution, effectively reducing waiting times, accelerating response times, and delivering personalized assistance through automation. Our digital agent is a pro at addressing customer needs during crises and seamlessly transferring customers to a human agent when necessary, ensuring quality service no matter the situation. 


Issue 2: Overwhelm from Routine Queries  

Customers reach out to their energy provider’s help centers for routine queries, more so than to address crises. The most common routine queries are billing issues, inquiries about energy pricing, concerns about meter accuracy, payment management, problems with debit and credit balances, and requests for meter installations or removals. Our AI-powered analytics identifies the common reasons for customer interactions, as well as any unique to your company. Our digital agent is then able to address routine customer inquiries, resulting in a reduction in wait times and agent overwhelm, fostering a more enjoyable CX experience for your customers. 


How does our technology help?

  • 24/7 availability

Providing 24/7 Support, Conversational AI in the energy industry ensures round-the-clock availability to answer frequently asked questions and handle inquiries. Whether addressing concerns about energy consumption, explaining billing details, or providing information about renewable energy options, AI-powered chatbots and voicebots contribute to an always-on-support system. 

  • Streamlining operations 

Streamlining operations in the energy industry with Born Digital’s conversational AI means more efficient handling of customer queries and internal processes. Our AI system excels in automating routine tasks like guiding customers through self-reading of electricity meters and managing meter unsealing requests, which significantly reduces the workload on customer service agents. By automating these repetitive tasks, our AI not only enhances customer experience through prompt and accurate responses but also optimizes internal operations, leading to increased efficiency and satisfaction for both customers and the energy provider. 


  • Avoidance of Duplicate Queries 

 Born Digital’s AI systems are designed to analyze and respond to customer inquiries before the chance for contact center overload is possible. For example, if a mass outage occurs and customers are calling in with concerns, our technology will recognize what phone numbers are registered under the outage areas. It will then ask if the customer is calling because of the outage so the call does not fall under the hands of the operator and avoids duplicities. The numbers that called in about the outage are then informed when the outage will be resolved, usually via SMS. This not only saves time for both customers and agents but leads to a more effective and efficient customer support process. 

Projections indicate a 25% increase in business process efficiency, positioning cognitive AI at the center of your business’ future success. Born Digital’s agents stand ready across multiple communication channels (chat, phone, email, etc.) to elevate productivity within your energy business.  


  •   Omnichannel approach

Born Digital can transform even the most complex and high stakes CX contact centers. Born Digital technology is able to cover all digital touchpoints and move seamlessly between channels. If you’re looking for a long-lasting complex solution and not a quick fix, we are here to help. 


If you’re ready to elevate your energy services with the power of conversational AI, sign up for a product demo today. Our technical experts will guide you through how Born Digital can uniquely address the needs of your energy company, taking your service to the next level. 


5 Emerging Trends in Customer Service: The 2024 Conversational AI Landscape

5 Emerging Trends in Customer Service: The 2024 Conversational AI Landscape

The year 2023 has experienced a dramatic transition in the domain of artificial intelligence, presenting it as a critical year of (r)evolution for innumerable enterprises and individuals alike, including our company. In the face of rapidly emerging technologies and the era of generative AI, now is the time for strategic adoption — a time to be alert and innovative, yet measured and thoughtful.

At Born Digital, we are constantly investigating the latest AI trends in customer service, and we’re eager to share our pragmatic view with you. Let’s explore these developments together and utilize them to create advanced customer service solutions as we move into 2024.

1. Knowledge Base Advisors: Your 24/7 Digital Experts

Rule-based chatbots operate without artificial intelligence. When interacting with such chatbots, they strictly follow a predefined path, adhering to human-made rules or a decision tree.

These chatbots prove effective in handling closed-ended questions, such as surveys, product feedback, or conference talk ratings. Additionally, they are cost-effective and simpler to implement compared to conversational AI chatbots. Although they utilize Natural Language Processing (NLP) to interpret user text input, their reliance on a rule engine makes them susceptible to challenges in navigating the complexities of human language. Implementing them as virtual assistants can also lead to user frustration when encountering limitations in the conversation flow.

2. Analytics and Hyper-Personalization ✨

The drive for personalization is stronger than ever, 73% of customers expect customer service representatives to understand their specific needs as Salesforce reports. Consequently, analytics play a critical role in decoding customer behaviors, enabling hyper-personalization in service delivery. AI tools are becoming adept at predicting customer needs and customizing interactions based on past interactions and preferences. By leveraging these insights, we empower your company to deliver a bespoke service experience that exceeds customer expectations. 

3. Digital Humans: Conversational AI meets Visual AI

Visual AI, particularly in the form of digital humans or personas, is redefining the customer experience by adding a human touch to digital interactions. These AI-driven personas can simulate human emotions and expressions, providing a more engaging and personable interaction than traditional chat or voice interfaces. In the digital landscape where brands are fighting for consumer engagement, digital personas represent a fusion of technology and human-like interaction, projected to be embraced by brands who are aiming for AI-based selling, and a superior customer experience at their physical branches or online. Now is the time to give your brand the voice and face it deserves.

4. Omnichannel approach to cover all digital touchpoints 🪄

Zendesk reports that 73% of consumers wish for the ability to move seamlessly between channels, which our omnichannel solutions provide. The convergence of voice, chat, email, social media, and even VR/AR platforms into a cohesive customer journey ensures that consumers can pick up where they left off, regardless of the channel they choose. This is imperative as more than half of contact center leaders anticipate an increase in interaction volumes over the next 18 months.AI tools are becoming adept at predicting customer needs and customizing interactions based on past interactions and preferences. By leveraging these insights, we empower your company to deliver a bespoke service experience that exceeds customer expectations. 

5. Voice AI and Emotion Recognition

The final trend is the advancement in Voice AI, coupled with emotion recognition technology. While 81% of agents, as per Salesforce, still believe that phone calls are the preferred channel for solving complex customer service issues, the integration of emotion recognition enables AI to detect subtleties in a customer’s voice, allowing for empathetic and effective responses. This trend reflects an understanding of not just the content of customer communication, but also the context and emotional state, enabling a more personable customer service interaction. Moreover, it comfortably bridges the gap between the physical and digital worlds for consumers who are still rather hesitant to adopt the newest technologies.

At Born Digital, we’re not just following trends; we’re creating them. Our mission is to support you in delivering a customer service experience that’s ahead of its time. Ready to redefine customer engagement with digital humans of knowledge base advisors? Let’s talk! 

Everything You Need to Know About AI Chatbots in 2023

Everything You Need to Know About AI Chatbots in 2023

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What is a Chatbot?

A chatbot refers to a program designed to comprehend and respond to user inputs, delivering information, assistance, or accomplishing tasks through predefined rules or AI algorithms. They find applications in diverse areas, serving as customer support chatbots and messaging platform bots.

The creation of a chatbot involves two distinct facets:

The creative aspect, encompassing chatbot design, involves tasks such as identifying the target audience, aligning the bot’s tone of voice, crafting responses, structuring conversation flows, and ensuring a positive user experience. It also involves designing the visual elements of the interface.

Conversely, the technical aspect, known as bot building, encompasses the programming of the designer’s flows to bring the bot to life. This includes ensuring an accurate understanding of user inputs, effective information processing, and providing suitable responses. Additionally, it involves establishing connections with external sources like databases and APIs.

In simple terms, bot designers play a crucial role in ensuring that a chatbot operates in line with its intended purpose, supporting customer service agents and aiding clients in achieving their objectives. 

Rule-based vs AI Chatbots

Chatbots can be categorized into two types: rule-based and AI chatbots.

Rule-based Chatbots

Rule-based chatbots operate without artificial intelligence. When interacting with such chatbots, they strictly follow a predefined path, adhering to human-made rules or a decision tree.

These chatbots prove effective in handling closed-ended questions, such as surveys, product feedback, or conference talk ratings. Additionally, they are cost-effective and simpler to implement compared to conversational AI chatbots. Although they utilize Natural Language Processing (NLP) to interpret user text input, their reliance on a rule engine makes them susceptible to challenges in navigating the complexities of human language. Implementing them as virtual assistants can also lead to user frustration when encountering limitations in the conversation flow.

AI Chatbots

AI chatbots leverage artificial intelligence, Machine Learning (ML), and Natural Language Processing (NLP) to replicate human-like conversation. These AI-driven chatbots improve their capabilities with each interaction, delivering more accurate and personalized responses. They demonstrate increased sophistication in extracting pertinent information from external sources, as facilitated by bot designers.

How Chatbots Work

There are two distinctions among key natural language processes:

  1. Natural Language Processing (NLP) serves as a comprehensive term for machines engaging with human language.
  2. Natural Language Understanding (NLU), a subset of NLP, entails a more advanced comprehension of language, involving the extraction of context and intent from inputs.

In essence, an interaction with an AI bot follows a four-step multi-turn loop:

  1. The user inputs information or poses questions in the chatbot.
  2. NLU analyzes the input, identifying intent and additional details such as data, location, or products.
  3. Dialog Management associates the intent with the appropriate segment of the conversation flow and accesses external systems or knowledge bases to retrieve relevant data.
  4. Natural Language Generation crafts a response based on the information gathered in Dialog Management and transmits it back to the user.

Why Businesses Need Chatbots

The presence of chatbots dates back to the 1960s, with continuous technological enhancements over decades enabling their expansion into new markets and engagement with diverse audiences.

The primary business use cases for most bots can be categorized into information-oriented and task-oriented chatbots.

Information-oriented Chatbots

As implied by the name, information-oriented chatbots are crafted to furnish users with answers to their inquiries. These chatbots excel in retrieving precise and pertinent FAQ responses sourced from databases, websites, or APIs. They can serve various purposes, such as:

  1. Interactive interfaces facilitating navigation through extensive user-facing knowledge bases.
  2. Virtual tour guides providing information about exhibits, offering historical facts, and responding to visitor questions.
  3. Health information providers addressing health-related queries, delivering guidance on symptoms, suggesting first aid procedures, and recommending home remedies.

Task-oriented Chatbots

Task-oriented chatbots exhibit a proactive nature, capable of executing specific tasks or actions, rendering them more transactional. Their primary objective is to guide users through predefined workflows or processes by understanding and responding to specific commands or intents associated with their assigned tasks.

This entails engaging in multi-turn conversations with users, utilizing Dialog Management, as discussed in the previous section. Moreover, this step-by-step guidance may involve accessing connected APIs to perform necessary actions, such as verifying users before divulging personal information or incorporating new data into the system.

Task-oriented chatbots excel in:

  1. Appointment scheduling, allowing users to check availability, schedule appointments, receive reminders, and manage rescheduling or cancellations.
  2. Order tracking, enabling buyers to monitor real-time status updates, obtain estimated delivery times, and seek details about their orders.
  3. Financial services, where conversational AI chatbots provide users with account information, conduct transactions, and address financial queries, particularly beneficial in banking and insurance institutions.

Challenges in Constructing Chatbots

The effectiveness of chatbots hinges on the quality of the dataset upon which they are constructed. If the data is biased, corrupted, or inaccurate in any way, the chatbot may mislead or discriminate against users, resulting in frustration and an elevated churn rate.

To enhance your chatbot’s comprehension capabilities, it is essential to utilize datasets encompassing a variety of sentence structures, synonyms, and real-world examples that capture the subtleties of language. This underscores the significance of generative AI in expediting the bot-building process, as elaborated below. Fortunately, our conversational AI platform at Born Digital incorporates NLU models and generative AI integration.

Even with meticulously curated datasets, errors may still arise. As a bot designer, it is crucial to continually scrutinize and pinpoint areas where chatbots make mistakes, identifying common errors to enhance the precision of intent recognition.

Navigating the intricacies of conversation flows poses another formidable challenge in bot design. Employing a low-code visual flow builder proves invaluable in simplifying this process and making it more intuitive.

Lastly, gaining client trust and encouraging the adoption of chatbots present additional hurdles. Historical reservations are understandable, given that, until recently, companies had access to only robotic-sounding bots with low intent detection accuracy. In the current landscape, where advanced conversational AI chatbots are readily available, presenting users with an uninspiring interface would be a missed opportunity and detrimental to business. This sentiment aligns with the findings of the Uberall report, which highlighted the desire for chatbots to exhibit a more “human”-sounding, natural conversation.

The Evolution of Chatbots through Generative AI

The advancement of chatbots now includes the incorporation of generative AI, leveraging Large Language Models (LLMs) with ChatGPT being a prominent example. This widely used tool has the capacity to access all available internet information, enabling it to generate responses to user queries, much like traditional chatbots.

While generative AI tools are beneficial for process automation and expediting NLU model training, they may not be optimal for business applications. Despite the impressive scope of using the entire internet as a knowledge base, there are drawbacks, as evidenced by instances of ChatGPT providing inaccurate or contextually inappropriate answers. Integrating such tools into business chatbots relinquishes control over answers, data storage, and security.

Therefore, it’s important that AI bots undergo training on specific and carefully curated datasets. These datasets must be self-contained, ensuring the security of sensitive information, a critical consideration in sectors such as healthcare and finance. However, it’s worth noting that, as mentioned earlier, ChatGPT remains an excellent tool for expediting the bot design process.

Generative AI showcases its capabilities through various applications:

  1. Similar Phrases: ChatGPT can instantly generate ten alternative versions of a sentence, such as “Tell me how to access my bank account number.” These examples aid in training the NLU model.
  2. Synonyms: Ensuring the model comprehends different user expressions, for instance, in discussions about credit cards.
  3. Conversation Flows: ChatGPT can be tasked with creating a flow initiated by a user asking, “Where is your closest branch?”
  4. Bot Responses: Particularly useful for quick ideation on non-information-specific answers, assisting in defining a brand-matching tone of voice.

Research conducted indicates that incorporating generative AI in training NLU models can accelerate the bot-building process by up to 60%.

What's the future of AI Chatbots?

The chatbot industry is rapidly growing, projected to exceed $994 million with a remarkable annual increase of around $200 million and a 22% compound annual growth rate. Businesses, particularly smaller ones, are integrating chatbots for efficient customer connections. Future trends indicate that chatbots will become more human-like, driven by advancements in NLP and ML, offering natural interactions.

Deep customer insights will guide chatbot behavior, and they will play a central role in reshaping contact centers, potentially running them autonomously. Voice bots equipped with voice capabilities will become mainstream. Businesses will prioritize chatbots for exceptional customer experiences, leveraging messaging platforms, facilitating automated payments, and utilizing social media engagement.

The evolving landscape of chatbots extends beyond increased adoption of generative AI and AI-assisted tools. A notable trend is the transition of conversational AI bots towards becoming team-agnostic. This shift implies that companies will move away from creating distinct chatbots for marketing, sales, and customer service. Instead, the focus will be on developing a singular virtual assistant that serves as an extension of their brand.

As 2023 concludes, the era of chatbots is just beginning, promising further innovations and integration into daily life.

Selecting the Right Conversational AI Platform

Now that you have gained insights into what a chatbot is, its types, and use cases, you can confidently take your initial steps with Born Digital!

Why Chatbot Frustration is a Thing of the Past: No More ‘I don’t understand, please repeat your question’

Why Chatbot Frustration is a Thing of the Past

Gone are the days when chatbots left users frustrated with their repetitive “I don’t understand” responses, endlessly echoing the same phrases. Thanks to generative AI, the creation of chatbots capable of engaging in human-like conversations has not only become possible but also straightforward and quicker.

However, we’ve taken a step further by eliminating the need for training data, making the process even faster and more efficient while maintaining the highest level of answer accuracy. Let’s dive into the process and explore how you can create human-like chatbots within minutes using our platform!

Provide KNOWLEDGE instead of guessing what your customers ask

We begin by “training” your bot using the knowledge base available within your company. You can decide whether you want the bot for external or internal communication — it’s flexible for both. We then upload these documents in any type of text, doc, pdf, ppt format, and there’s no restriction on the number of documents or pages you can upload. However, it’s crucial to ensure these documents are well-structured because the bot’s accuracy depends on the quality of the input data.

Chatbot Frustration

Once your documentation is uploaded, the technology has all the information it needs to answer questions related to the uploaded content. It could be anything from company terms and conditions, insurance policies, product details, travel restrictions, e-commerce offerings, website information, or internal materials like onboarding guides and process manuals. As you can see, the possibilities are endless.

Setting the Instructions

However, your job isn’t done just yet. You still need to instruct the AI on how it should interact. For example, if the documentation is about Born Digital products, we can instruct the bot to “act like a helpful Born Digital customer service agent.” Now, the bot knows to adopt the language and tone of a customer service agent — being polite, always trying to assist customers and more.

It’s important to note that you’re in control of the bot’s behaviour. You can define the boundaries — specify how contextually it should answer, how friendly or humorous it should be, which language(s) it should use (it can handle multiple languages), and how much it should improvise when faced with questions it doesn’t have a direct answer to.

All you need to prepare ahead is the excel sheet with included contacts you’d like to call. The excel sheet for this particular outbound campaign is then imported to the Digital Studio.

Chatbot Frustration

Testing the Magic 🪄

Now your job is practically done. You can now engage in a human-like conversation with a chatbot and witness its magic. The possibilities for use cases are truly limitless. You can implement digital agents for various purposes — whether it’s customer support, answering product inquiries, or assisting with internal processes.

Join the AI Revolution

The AI industry is undergoing a revolution, and so is customer service. Now is the perfect time to experiment with this technology, discover what works best for your business, and gain a competitive edge.

If you’re curious to see this technology in action, we’d be delighted to create a customized chatbot for your company and demonstrate it in a meeting. Interested? Book a free demo with our team today and step into the future of chatbots!

Let’s talk!

No Time To Call? Send a Digital Agent on a Mission!

No Time To Call

Have you ever identified hot leads from a list of 5 thousand prospects within 1 hour? Or have you ever collected verbal feedback from hundreds of customers within a few minutes?

It almost sounds impossible to manage either of those. Unless you have a Digital Agent. The Born Digital Agent!

Within our Digital Studio, it is easier than ever to create an outbound campaign on your own and send your Digital Agent on a mission whenever you need. How does that work?

Practical example

Let’s look at a simplified example of launching an outbound party bot within the Digital Studio.

However, you could use the same launching process for any other complex use case of your choice, such as outbound digital agent for sales leads validation, customer satisfaction surveys, marketing surveys or recruitment process.

All you need to prepare ahead is the excel sheet with included contacts you’d like to call. The excel sheet for this particular outbound campaign is then imported to the Digital Studio.

Digital Agent

When creating a new campaign, you can set up when the campaign should start, when it should end or how many times the Digital Agent should repeat the call in case the prospect was not reached.

You can also set up in which situations those calls should be repeated. For instance, if the line was busy, if the customer did not answer, if the customer hung up at the beginning, etc.

Digital Agent

I launched the campaign. Now what?

Once you kick off the campaign, you can follow the statistics in real-time.

You can see whether the prospects are interested in your offer (or not), whether they answered the phone or maybe asked for a later call. And in case you’ve been wondering — of course, you can easily pause or stop the campaign anytime you need.

Digital Agent

On top of that, all the recorded and transcribed customer responses are automatically transferred to the next stage of the sales process. You can also see the whole transcript and play the selected parts of recorded calls.

So, how does that sound? Are you ready to focus on lucrative deals in your pipeline instead of qualifying each lead manually?

There is no need to dial 007. You can just book a free 30min demo with our product specialist!

Digital Agent

(This could be you!) 🫵 BOOK A DEMO

Bringing AI into Daily Life: Meet Our Digital Human

In today’s world, Artificial Intelligence, Machine Learning, and complex automation are largely seen as stepping stones toward James Cameron’s The Terminator. This assumption is fueled by the most common AI design mistake: attempting to create an exact replica of human behavior.

AI is not human, so it is a mistake to try and make it so. This is where we, Born Digital, come in. We have learned how to combine the strengths of technology and a human approach to serve the larger purpose of bridging the gap between people and professionals in different services and industries. On our crazy ride to bringing AI into the real world, we have introduced a new product: The Digital Human.

The Digital Human Ride

The Digital Human — a ride you don’t want to miss

This product utilizes our unique synergy of technologies to create a new-age solution to real human problems. Speech Technologies, Visual AI, Conversational AI, AI Analytics, and Generative AI are some of the major technologies that are integrated into our digital persona. We currently have two practical use cases for our digital human persona: the Digital Medical Assistant and the Digital Public Officer.

Meet Our Digital Medical Assistant

In the Czech Republic, and worldwide, there is a shortage of doctors. Globally, there are roughly 15 million healthcare positions left empty. Because of this, long wait times and lengthy examinations have become the new standard in healthcare. In the best case, people’s quality of life has worsened, but in the worst case, it has led to the late diagnosis of serious illnesses and lower chances of recovery.

Try to recall your last visit to the doctor and consider how much time was spent writing medical reports, prescribing medication, recommending further examinations, or other tasks performed on the computer during a doctor’s appointment. According to research, doctors spend more than 51% of their time on such administrative tasks. That is valuable time that should be spent interacting with the patient. Our Digital Medical Assistant helps minimize the time doctors spend on administrative tasks by automatically generating reports and analytics after monitoring doctor-patient conversations.

Digital Medical Assistant

In the exam room and during medical rounds, the Digital Medical Assistant allows doctors to devote their attention to the patient while it monitors, transcribes, and analyzes their conversation. At the end of the appointment, the assistant will automatically generate a medical report that the doctor can review and add details to between patient visits or later in their office.

The Digital Medical Assistant will also improve patient care by allowing doctors to rededicate themselves to patients while simultaneously collecting data to be analyzed for the improvement of the overall healthcare system.

Digital Public Officer

Meet Our Digital Public Officer

Small villages and remote towns do not share the same luxury of urban areas’ easy access to public and commercial services like post offices, banks, insurance agencies or government offices. The second use case for the Digital Human Persona is the Digital Public Officer who can act as a single service point for various commercial services, state administration, and municipal authorities.

The Digital Public Officer

The Digital Public Officer can perform a variety of administrative and commercial tasks including authenticating the identity of a customer based on facial recognition, offer payment and information services, scan and provide guidance on the completion of state or municipal documents (tax returns or housing subsidies), and ultimately help automate the process of processing such government and commercial documents. If the AI cannot solve a customer’s request on its own, it will connect them to a remote human officer in real-time.

The Digital Public Officer will provide crucial administrative and commercial services for people of small communities. Through AI-powered civil and commercial services, our digital human persona can reconnect isolated communities with the larger society and economy.

The Digital Human Persona’s Psychology

Don’t worry, the psychology behind our Digital Human Persona isn’t advanced enough to replace your romantic partner or your best friend. Our Medical Assistant and Public Officer utilize psychology to build a trustworthy personality and human-esque presentation to encourage everyday people to interact with complex Artificial Intelligence.

Outside of what our Digital Human Persona is currently capable of, there are endless potential future applications. Our Digital Medical Assistant could one day use AI analytics to compile medical report databases to predict epidemics or pandemics. AI Analytics could push humans into a new era of healthcare with the ability to predict diseases like we predict the weather today.

In case you don’t know where to start with implementing Artificial Intelligence in your company or how to combine the technology and people’s skills, let us know! We are happy to point you in the right direction.

Transform Customer Insights with GPT: A Guide to Wise Implementation

Is it really the right time to implement GPT into your business? And where exactly could it help? If you don’t know where to start, we are here to show you a simple guide which will help you find strength in the force. The good side of the force!

A Guide to Wise Implementation

First, know your customers

We can’t stress this enough. The undeniable truth is that high-quality customer insights rule the game of customer experience. So, if anything, you should keep an eye on that price.

73 % of customers now say that the customer experience is the number one thing they consider when deciding whether to purchase from a company.

And you can’t really improve the experience of your customers without knowing their exact pain points. But how to identify them?

To give you a concrete example, our Born Digital platform is a powerful tool for customer data analytics using artificial intelligence to process and qualify customer data (from across all your platforms). The results already are remarkable. But when you combine it with the GPT integration — that’s where the fun begins!

Let the force (read data) guide you

Let the force (read data) guide you

The latest technology gives you actionable insights that can help you build a deeper understanding of your customer needs.

Firstly, it can extract the data from the interactions with your customers — in a super fast way and in a super high quality. It doesn’t matter whether your customers write you on WhatsApp, Instagram, via e-mail or call you via phone.

You immediately see the main keywords, interaction topics and a short summary of any of those interactions. You also see how your customers feel about the interaction with your company — whether they are frustrated or grateful and how this feeling evolves during the conversation. GPT integration also proved to be a powerful tool when analyzing the performance of your agents.

Transform Customer Insights with GPT

On top of that, you then see all those interactions analyzed within a clear dashboard — including categories where your customers experience the most issues and you can immediately act upon that. You simply use these data as powerful guidance for the continuous improvement of customer experience.

According to Gartner, 80 per cent of organizations expect to compete mainly based on customer experience. So you better make sure you’re not late for that party! GPT is making customer insights as powerful as ever before.

Can GPT answer customers’ requests?

Of course, it can. We talk more about it in this article about the power of GPT in interactions with customers. However, we always say that the prevention part should come before the automation — it allows you to make data-driven decisions, avoid costly mistakes and gain an edge over your competition.

If you wanna learn more and try the magic of GPT-powered analytics with your customers’ data, book a demo here! Until then — may the force be with you.