Understanding smart chatbots in 2024

Overview

Though most modern consumers prefer to interact with chatbots before escalating to live agents, they can still be frustrated by their inability to deliver human-like interactions or answer open-ended user questions. Smart chatbots aim to alleviate these issues by ditching predefined scripts, instead utilizing advancements in natural language processing (NLP) and machine learning to provide more in-depth and personalized responses.

These bots’ ability to answer a wider range of customer inquiries, both simple and complex, can lead to higher levels of customer satisfaction and expanded outreach capacity.

Today, we’ll dive into how smart bots work, the benefits that they can provide, and how to incorporate them into your customer engagement blueprint.

What are smart chatbots?

Smart chatbots are advanced software applications designed to simulate human conversation through text or voice interactions. These bots leverage artificial intelligence (AI), NLP, and machine learning algorithms to respond to user inputs in a way that mimics real-world communication. Unlike basic chatbots that follow scripted responses, smart bots can understand context and adapt their responses based on the conversation's flow. They’re capable of handling both simple and complex queries, learning from interactions, and improving their performance over time.

How do smart chatbots work?

At their core, smart chatbots utilize NLP to interpret user language, parsing input text or speech to identify key components such as intent and context. Natural language algorithms break down the user's message into structured data that the bot can process, allowing it to comprehend the subtleties of human communication.

Once a chatbot understands a user's input, it relies on machine learning models to generate an appropriate response. These models are trained on vast datasets containing examples of real-world communication, which help the bot improve its conversational AI abilities. Additionally, smart chatbots use context management to maintain the flow of conversation, recalling previous interactions and using them to provide relevant responses.

Why should your business consider smart chatbots?

By offering around-the-clock availability, smart bots ensure that customers can request assistance at any time, helping improve satisfaction rates. These chatbots can simultaneously handle a large volume of inquiries, both basic and nuanced, reducing wait times and increasing efficiency. 

Their ability to deflect a wide range of user queries also assists in freeing up real people to focus on higher-priority tasks. Smart chatbots and live chat are often employed synergistically to enhance customer experience (CX) by combining the efficiency of AI with the personalized touch of human interaction. 

Furthermore, smart bots can play a vital role in sales and marketing. They’re often leveraged to engage potential customers by providing personalized recommendations, answering product-related questions, and even assisting with the purchasing process. By analyzing user data, smart chatbots can offer tailored marketing messaging, enhancing the impact of campaigns.

Benefits of smart chatbots

When compared to traditional chatbots, smart bots deliver a plethora of advantages that can significantly improve the CX and improve operational efficiency. If your budget allows for it, we always recommend smart chatbots over script-based bots. Their ability to understand context, recognize patterns, and constantly improve their abilities can provide your business with a leg up on the competition in customer support, sales, and marketing efforts. 

Better conversational experience for the customer

A key advantage of smart chatbots is their ability to maintain context throughout an interaction. They’re able to remember previous interactions and use this information to provide relevant and coherent responses. This context-awareness enables them to handle complex dialogues effectively, addressing follow-up questions without losing track of the conversation’s flow. For instance, if a customer asks about a product and then follows up with a question about its availability, the smart bot can seamlessly continue the discussion, whereas a traditional chatbot might require the user to restate their query.

Continual learning for the chatbot

Smart chatbots also continuously learn from interactions, enhancing their ability to provide personalized experiences. Through machine learning, they analyze past conversations to identify patterns and improve their response strategies. This adaptive approach empowers them to offer more relevant recommendations, tailored responses, and proactive assistance. Traditional chatbots, limited by their static programming, cannot adapt in this way.

Expansion of knowledge base

The ability to improve from past interactions also allows smart bots to continually refine and expand their knowledge bases. These chatbots are designed to learn from every interaction, processing user inputs to identify patterns and trends. They then use this information to refine their understanding of user intents and improve their response accuracy. Smart chatbots can also easily be updated with new data sources, such as knowledge articles, FAQs, and product information, enabling them to stay current with the latest information. 

Conversely, traditional bots typically rely on static databases that require administrators' manual updates. Expanding their knowledge base must be explicitly programmed, which can be time-consuming and resource-intensive.

Challenges of smart chatbots

As with any new system or software, you may run into some difficulties when implementing smart chatbots into your business structure. Fortunately, with a little extra attention, teamwork, and patience, most of these challenges can be overcome relatively painlessly.

Cost

Implementing smart bots can sometimes be a financial challenge for businesses due to the initial setup costs. Integrating these chatbots with existing systems such as CRM software, databases, and communication channels often requires custom development work, which can be expensive.

Still, if your organization possesses the funds for a smart chatbot, its ability to improve customer satisfaction and preserve real-world resources can provide a significant return on investment (ROI) in the long run. These bots also typically offer more robust insights through data collection and analysis, which can lead to more informed business decisions and further cost reductions.

Training difficulty

It’s paramount that chatbot training data is extensive and diverse to ensure the software can accurately respond to a wide range of user queries. However, preparing large datasets can be a time-consuming and resource-draining process, especially for businesses operating in niche industries. The quality of training data is crucial as well; inaccurate or biased data can lead to flawed model predictions and unreliable bot performance.

To overcome these challenges, businesses can employ various strategies to improve the efficiency of chatbot training. One approach is to leverage existing data sources, like customer interactions and support tickets, to gather relevant training data. Additionally, companies can use data augmentation techniques to enhance dataset diversity, such as generating synthetic examples or incorporating external datasets.

Limited knowledge of chatbot

Understanding the underlying technologies of smart chatbots is crucial for effectively building them. Often, navigating the complexities of these technologies requires specialized skills that may not be readily available within an organization. This has the potential to cause delays and suboptimal outcomes during the implementation process.

To alleviate this concern, companies can either invest in training for internal assets or solicit the assistance of external specialists. To educate employees, team leaders might provide them with access to online courses, workshops, and certification programs focused on AI development and chatbot design. When contracting outside help, businesses can recruit AI specialists, data scientists, and software engineers with expertise in developing smart bots.

Types of chatbots

The two most widely used types of chatbots are retrieval-based and generative. Retrieval-based bots are relatively basic and limited in scope, while generative AI chatbots can handle a wider range of interactions while also sufficiently personalizing them. 

Retrieval based

Retrieval-based bots are chatbot systems that generate responses by leveraging predefined scripts from a database. These bots don’t generate new responses from scratch, but rather select the most appropriate response from a set of predesignated options. The selection process is typically based on matching keywords in the user's input to responses stored in the database.

These chatbots are often used in scenarios where the range of possible user inputs is relatively limited, and they may struggle to handle open-ended or complex conversations. Since they rely on prespecified responses, retrieval-based bots may experience difficulties in providing relevant answers to queries that deviate from the expected patterns. This can result in less engaging interactions, particularly in scenarios where users require personalized communication.

Generative

On the other hand, generative chatbots utilize AI to create responses to user inputs in real-time, without relying on pre-defined templates. These bots use deep learning techniques, such as recurrent neural networks (RNNs) and transformers, to engage in more human-like conversations. Generative chatbots are capable of handling open-ended conversations and providing more contextually relevant responses.

By generating replies dynamically, these bots can adapt to a wide range of user prompts, delivering personalized answers to queries. They excel in situations where users require more than just simple information retrieval, such as complex problem-solving, creative tasks, or emotional support.

How to create a smart chatbot

Developing and implementing a smart chatbot may seem like an intimidating process at first glance, but with the right resources and a well-rounded approach, it’s often more straightforward than anticipated.

Define goals

Firstly, establishing specific objectives helps to clarify the purpose of chatbot implementation. By clearly defining the intended outcomes, such as improving customer service efficiency or enhancing user engagement, businesses can focus their efforts on developing functionalities that directly contribute to achieving these goals.

Moreover, setting unambiguous benchmarks provides a framework for measuring the effectiveness of a smart bot. By setting key performance indicators (KPIs) and success metrics upfront, companies can track the chatbot's performance over time, evaluating its contribution to organizational outcomes. For instance, if the goal is to improve customer satisfaction, KPIs might include response times, resolution rates, and customer feedback scores. 

Surprisingly, only 51% of companies attempt to designate aligned goals, while just 6% regularly revisit them; making goal-setting an integral component of your chatbot implementation gameplan can provide your organization with a significant competitive advantage.

Determine communication channel

Different communication channels have varying user preferences and usage patterns. Companies should select the most appropriate channels that align with their identified objectives and audience demographics. By understanding where their customers are most active, businesses can strategically deploy their chatbot to maximize reach.

Additionally, determining optimal communication channels before creating a bot allows companies to tailor its functionality to suit each platform's unique requirements. For example, a chatbot on a messaging app may need to support rich media, such as images and videos, while a website chatbot may prioritize integration with existing web design elements.

Decide chatbot technology

Determining which technologies you’ll be utilizing before creating a smart chatbot enables businesses to assess the technical resources needed for implementation. Depending on the chosen technology stack, developers may require specific skills in areas such as NLP, machine learning, or software development. When companies understand the technical requirements upfront, they can allocate resources appropriately and build the necessary talent to maintain the chatbot effectively. 

Choosing the right technology stack also verifies interoperability with existing IT infrastructure. Organizations may need to integrate their smart bot with databases, messaging platforms, and other backend systems to provide optimal UXs. By selecting technology that supports standard protocols and APIs, businesses are more likely to avoid any compatibility issues. 

Design and train chatbot for conversation 

When designing your smart bot, it's essential to first define its purpose, target audience, and scope of functionalities. Understanding specific user needs guides the design process and informs decisions about conversation flow and language style. Once objectives are established, outlining the bot's conversational flow involves mapping out potential user journeys, defining conversation paths, and identifying key decision points that drive interactions forward.

Once the chatbot’s architecture is in place, training it involves collecting training data, training AI chatbot models, and refining its performance through iterative testing. Training data should be diverse and balanced to verify that the chatbot can handle a wide range of scenarios. This may necessitate manually annotating examples of user queries and corresponding responses, as well as incorporating feedback loops to improve the bot's accuracy. 

Testing

Conducting smart chatbot testing involves verifying that it performs as intended; businesses should thoroughly test all features and different types of interactions across multiple scenarios. Companies will often simulate various user inputs to validate the bot's ability to understand queries and generate appropriate responses. Functional testing also includes edge case testing to identify potential limitations, such as ambiguous queries or unexpected inputs.

Leveraging usability testing is also vital in evaluating the chatbot's user interface and overall UX. This kind of testing usually involves soliciting feedback from users through surveys or interviews to assess how easy the chatbot is to use. Usability testing helps in examining factors such as navigation, clarity of messaging, and accessibility across different platforms. 

Implementation

To begin the implementation process, organizations must define the use cases for their smart bot, identifying specific areas where it can add value. This necessitates conducting a thorough analysis of customer needs, pain points, and interaction patterns. By establishing the desired outcomes and target audience, companies can tailor the chatbot's functionalities to address specific business needs and deliver tangible benefits.

After the chatbot has been developed and tested, implementing it into live production involves configuring access permissions, deploying software updates, and monitoring performance. This might involve deploying the bot on cloud platforms to ensure scalability and availability. Ongoing maintenance is essential as well, as it verifies that the chatbot continues to perform optimally, while meeting evolving business requirements.

Smart chatbot use cases

The flexibility and adaptability of smart chatbot software allows it to be utilized in a wide variety of environments. Customer support, marketing, sales, and HR teams across all industries can benefit from the use of these bots, as they promote fast and consistent communication, while reducing the burden on human agents.

Customer service

Smart chatbots excel in handling customer inquiries and guiding them through processes, while also offering immediate responses without the need for human intervention. Whether it's providing product information or troubleshooting specific issues, these bots serve to streamline support processes, reducing wait times and enhancing overall satisfaction.

Furthermore, smart bots are adept at scaling support operations effectively. Their ability to handle a wide-range of inquiries simultaneously helps ensure that no customer is left unattended during peak hours or sudden spikes in demand. 

49% of US adults have utilized an AI-powered chatbot for customer service in the past year; we expect this number to keep growing as these bots become more prevalent and their capabilities continue to expand.

Sales

Sales-based smart bots can act as proactive virtual assistants, initiating conversations with potential leads and guiding them through the sales funnel. For instance, a clothing retailer could deploy a smart chatbot on their website that offers personalized styling tips and product recommendations based on a user's input and browsing history.

Smart chatbots shine in lead qualification as well, allowing sales teams to focus their efforts only on high-potential prospects. Through interactive conversations, these bots can gather valuable information about leads' needs, budgets, and timelines, qualifying them based on predefined criteria. For example, a software company could deploy a smart bot to engage with website visitors interested in their offerings, asking qualifying questions about their business requirements.

Marketing

By embedding lead capture forms within chatbot conversations, marketers can collect contact details and preferences instantly. For example, an e-commerce retailer could deploy a smart bot on their Facebook page to offer quiz-style engagement where users answer questions about their lifestyle, behaviors, and interests.

Additionally, smart chatbots can integrate with marketing automation platforms, allowing marketers to orchestrate omnichannel campaigns and track performance metrics seamlessly. This allows brands to create targeted engagement that reaches potential prospects on whichever channels they utilize most.

HR

In a human resources (HR) setting, smart chatbots offer the potential to revolutionize the recruitment process by automating initial candidate screening, scheduling interviews, and providing information about job openings. For instance, a business could deploy a bot on its careers page to engage with potential candidates, answer FAQs about the application process, and collect basic information.

Smart chatbots can also enhance employee onboarding by providing new hires with essential information and guidance in a personalized manner. These bots can welcome new employees, introduce them to the company culture, and guide them through the onboarding process. For instance, a chatbot integrated into an HR portal might greet new hires on their first day, provide them with access to training materials, and help them set up their new accounts.

E-commerce

For digital-first retailers, smart bots can serve as virtual assistants, guiding customers through their purchasing journey by providing personalized recommendations and assisting with order tracking. Leveraging its NLP capabilities, e-commerce chatbots can respond to customer inquiries with tailored communication in real-time, mimicking the experience of interacting with a knowledgeable live associate.

Moreover, these chatbots can play an essential role in post-purchase support, providing assistance to customers after they’ve completed an order. Smart bots may be used to send order confirmation messages, provide shipping updates, and address any concerns that may arise during the delivery process. For example, a chatbot integrated into a messaging app could notify individuals about their order status and estimated delivery time, as well as offer assistance with returns or exchanges if needed.

Banking/financing

When guiding customers through complex banking procedures, smart chatbots can collect necessary information, verify identities, and complete required documentation to expedite processes. For instance, a financial institution might deploy a banking chatbot to assist customers with mortgage applications, asking questions about their financial history and property preferences, and then providing personalized loan options based on their responses.

Additionally, as security is of particular importance for banking establishments, smart chatbots play a key role in fraud prevention by monitoring transactions, detecting suspicious activities, and alerting customers about potential risks. For example, a bank could deploy a chatbot to notify customers if a questionable purchase is detected on their account, allowing them to confirm or dispute the transaction immediately.

Financial institutions have the potential to automate up to 90% of all interactions with chatbots, and this number may be even higher when leveraging AI technology.

Telecommerce

Text-to-speech-enabled (TTS-enabled) chatbots can be useful tools for organizations that utilize the telephone as their main channel for marketing, sales, or customer service. With TTS, smart bots can deliver responses audibly, providing an accessible experience for users who prefer spoken communication. For instance, they might assist customers in navigating product catalogs, placing orders, and accessing support services through voice commands.

Even more, TTS-enabled chatbots can serve to deliver personalized audio messages in a natural and conversational tone. Bots can greet users with welcoming messages, announce special promotions, and audibly provide updates on order status. For example, a telecommerce business could deploy a chatbot while customers are waiting to speak with a live agent, greeting them with a friendly welcome message and offering personalized recommendations.

Start building your smart chatbot today with Freshworks!

Freshworks’ Freshdesk Omni acts as the market’s premier customer engagement platform, combining the communication capacity of Freshchat with the ticketing prowess of Freshdesk.

Tools like journeys can send personalized, proactive messages to users, tailored to wherever they’re at in the sales funnel, while small-talk capabilities can humanize interactions, rather than simply providing cookie-cutter responses. Additionally, in the rare case that our Freddy-AI-powered smartbot can’t sufficiently resolve an issue, conversations can easily be transferred to a live agent with retained context.

Businesses can also meet their customers wherever they are and in whichever language they prefer, as CSS easily connects with platforms like web, mobile, WhatsApp, Facebook Messenger, Apple Business Chat, and more, while providing the ability to interact in 33+ dialects.

Freshdesk Omni is simply a must for businesses that want the whole package and nothing less!