The era of personalization is here, and businesses of all sizes are investing the time, effort, and resources to stand out from the crowd. It's no longer about creating a one-size-fits-all experience for your visitors. People want to feel like they're getting an experience tailored to their needs and wants. They want to be able to find information with ease and get it when they need it.
Conversational AI chatbots are at the forefront of this strategy because they allow you to engage with your customers on a more personal level without investing in a large-scale marketing campaign.
So how do you go about creating those personalized experiences? How do you get the data you need to make these experiences happen for your users? And what will these experiences look like?
What is conversational AI?
Conversational AI is an advanced technology that combines machine learning (ML), natural language processing (NLP), and now, the capabilities of generative AI, to facilitate more human-like interactions between bots and humans. This technology is not just about understanding human language; it's about creating bots that can engage in meaningful dialogues, make decisions that feel human, and generate content that aligns with customer preferences.
These AI-enhanced chat platforms are a leap beyond traditional chatbots. While most basic chatbots rely on predetermined rules or a set of predefined responses, AI-enhanced chatbots leverage sophisticated algorithms to move beyond limited, script-based interactions to genuine, dynamic conversations.
How do conversational AI chatbots work?
Conversational AI chatbots simulate human-like conversations by harnessing a combination of technologies, including Large Language Models (LLMs), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG). Here's a quick breakdown of how these technologies work together:
- Large Language Models (LLMs): LLMs, like GPT (Generative Pre-trained Transformer), are at the heart of advanced conversational AI. These models have been trained on vast amounts of text data, enabling them to generate coherent, contextually relevant, and often highly personalized responses. They play a significant role in understanding and generating human-like dialogue.
- Natural Language Processing (NLP): NLP analyses the text (whether inputted by typing or converted from speech) to break down and interpret language patterns and structures.
- Natural Language Understanding (NLU): Here, the chatbot uses NLU to grasp the user's intent and meaning, ensuring that responses are relevant and accurate.
- Natural Language Generation (NLG): NLG is used to craft responses that are not only correct but also natural-sounding and engaging, completing the cycle of conversation.
- Automatic Speech Recognition (ASR): ASR converts spoken language into text, allowing chatbots to process verbal communication.
Together, these technologies, especially with the integration of LLMs, make conversational AI chatbots more sophisticated, capable of handling complex and varied interactions. This advancement leads to more natural, helpful, and personalized user experiences.
Uses of AI chatbots
Under minimum supervision, an effective chatbot can comprehend and respond to real-time consumer concerns in a near-human tone and deliver applicable resolutions. Here are some common uses of AI chatbots:
- Customer service: AI-based chatbots can help reduce the number of employees necessary to provide excellent customer service. Customer support chatbots handle frequently asked questions (FAQs) for various industries such as banking, airlines, and digital organizations.
- Human resources: AI chatbots can conduct numerous HR activities, such as onboarding, staff training, addressing employee queries, and revising employee records.
- eCommerce: AI chatbots can provide online shoppers with an enhanced buying experience by interacting with them to determine their needs and provide suitable product recommendations.
- Healthcare: Intelligent chatbots can enhance healthcare services by making them more accessible and inexpensive. They can improve various administrative operations, like assisting patients in filing insurance claims and receiving payments more quickly.
- Education: An AI chatbot for education can have discussions with potential students to gather and pre-qualify leads during enrollment. Chatbots in education can support staff by doing administrative work, answering student inquiries, facilitating online learning, etc.
- Virtual personal assistants: Many people own Internet of Things (IoT) devices, such as Alexa or Siri. Such conversational AI solutions leverage user feedback to refine answers to various queries and requests, including product pricing or stock.
90% of businesses saw significant improvements in complaint resolution, call processing, and customer and employee satisfaction with conversational AI chatbots.
Source: MIT Technology Review
AI chatbots in customer service
Consumers demand quick response times and results when they contact a brand for support, regardless of the platform they choose. On the other hand, human agents can only manage so many cases at once. So, how do you scale customer support to ensure business success?
Enter AI chatbots for customer service. They help customers swiftly resolve small issues and difficulties while freeing up representatives for more complicated, personal encounters. Customer care chatbots can handle simple, repetitive activities that don’t need an agent's soft skills and expertise. These bots improve customer support by gaining data, validating account information, and triaging before connecting clients to assistance.
For example, if a consumer inquires about how to change a password or the projected delivery time, a customer service chatbot responds swiftly by automatically obtaining relevant information.
Benefits of chatbots in customer service
Chatbots offer several advantages over traditional customer service methods, such as phone and email support. One of the primary benefits of chatbots is that they can provide a more personalized experience for customers based on purchase history or customer data. Chatbots can get to know customers over time and offer suggestions and recommendations tailored to their needs. This makes them excellent for cross-selling and upselling strategies.
Another advantage of AI chatbots is that they offer customers a more human experience. Chatbots can engage in conversations with customers and provide emotional support to improve customer retention.
In addition, customer service chatbots can help to reduce costs and improve efficiency. By automating customer support tasks such as answering FAQs and troubleshooting, chatbots can free up time for human agents to focus on more complex inquiries. They can also help to reduce wait times and improve first contact resolution rates.
Also, chatbots can provide around-the-clock support for asynchronous communication. This can be particularly beneficial for businesses with international customers or those operating in multiple time zones.
Finally, you can also leverage your chatbot to gather customer insights. First-party data collection can enhance your service or help you tailor future engagements. Let’s see how gathering and employing first-party data can become a game-changer for your AI chatbot.
Using first-party data with conversational AI
Conversational AI bots can use first-party data to create personalized experiences and improve customer experience. Using AI to understand, shape, customize, and optimize the customer journey, conversational bots can improve their underlying business processes through bidirectional conversations to provide better customer service and customer outcomes.
Analytics plays a crucial role in driving insights from these conversations and can also be used by organizations to derive a competitive advantage based on deepening customer relationships with first-party data.
What is first-party data?
First-party data is data that a company collects from its own customers and interactions. Companies gather this data through web analytics tools, surveys, customer contact forms, loyalty programs, and engagement tools such as live chat, messaging, and voice (this includes click-to-call, VoIP, and telephony solutions that capture transcript data). This can help create personalized experiences relevant to the customer and improve customer satisfaction.
How is it different from third-party data?
Third-party data is information that a company collects from sources outside of its own customer base. You can collect this data from public sources such as census data, social media data, and information gathered from third-party providers. Third-party information is often less accurate and less relevant to the customer.
One of the main benefits of first-party data is that its more accurate than third-party data. This is because first-party information is collected directly from customers, so it's less likely to be inaccurate or out-of-date. Another characteristic of first-party data is that it's more targeted. This data can be used to create targeted marketing campaigns that are more likely to succeed.
64% of businesses use chatbots to provide personalized customer support.
Source: Statista
Creating personalized experiences with first-party data
Collecting first-party data is the first step in creating these personalized experiences. However, it's important to note that you can't simply collect this data and then sit on it. You must actively use it to create these personalized experiences for your customers. There are a few different ways you can use first-party data to create personalized experiences with conversational AI.
One way is to use it to create custom chatbots. You can use first-party data to train your chatbot to understand your customers' specific needs and wants. This will allow you to create a customized chatbot tailored to their needs. For example, Kiehl's has developed a custom chatbot with a multi-step test to accurately determine customers' skin type and offer them the most suitable product suggestions.
Another way you can use first-party data is to make personalized recommendations. You can use this data to understand your customers' specific interests and tailor your offerings. Babylon Health is a well-known online healthcare business that uses AI technology chatbots to deliver consultations based on a user’s health history. The bot can even match you with a physician for live video appointments.
You can also use first-party data to offer multilingual support. This data can help you understand the language preferences of your customers and then provide support in those languages. KLM Royal Dutch Airlines has to engage in more than 15,000 social conversations in different languages weekly.
KLM employed a chatbot BlueBot to deliver faster, more effective, and customized customer care for a seamless process. The self-learning AI chatbot is available 24×7, supports several languages, responds to visitors' requests in real-time, and is accessible via the Facebook Messenger app.
Finally, you can use first-party data to create an end-to-end seamless experience. Connecting different platforms to provide an omnichannel customer experience with first-party data improves conversion and brand loyalty. Augusta Sportswear, a leading retailer of high-performance activewear, receives hundreds of complex ordering and custom clothing questions daily. Agents aren't always able to respond to these inquiries, which is why they partnered with Velaro to build their web chatbot.
Augusta employs a Q&A chatbot with NLP capabilities that help users track and alter sports team uniforms with specified colors, team logos, names, and embroidery. It also answers FAQs on print procedures and product specs.
When a consumer enters a query, the bot can identify the customer's intent and reply more conversationally or can guide users by presenting a selection of help articles to use as a self-help resource. The bot is trained using ML on many question/answer combination datasets. A knowledge base stores the bot's data to converse and offer conversational replies, which it automatically collects information from and utilizes to answer inquiries.
How to create a personalized experience with AI chatbots
The biggest problem with fully automated support is that people don't trust it. This can lead to weaker sales and lower conversion rates. But, you can use simple ways to make your AI-powered chatbot personal and make customers feel like they're talking to a human being.
Here's how to do it:
- Train your AI chatbot to identify message intent: Customer support reps are experts in reading between the lines of a customer inquiry to understand what a user is looking for and address their concerns. AI-powered chatbots should be able to recreate the same. Typically, you can train your chatbot to identify specific keywords or phrases to address their concerns.
- Personalize the conversation design: You can't just snap up a random chatbot and expect it to help your customers. The language must be clear, quick, and appropriate for handling the situation. A good way of implementing this is by using a conversational tone so that your user never feels like they're having a one-sided conversation. Customers will also appreciate having an intelligent choice of responses rather than being given the same stock responses repeatedly.
- Keep up with the conversation thread: Nothing irritates users more than walking in circles with an inadequately-trained chatbot. To provide an entirely personal touch for your clients, design a chatbot that retains data about prior requests so it can offer new recommendations rather than repeating the same replies. You can also integrate your chatbots with your customer relationship management (CRM) software and inventory management systems (IMS) to deliver a practical and appealing user experience.
- Enable multilingualism: Having numerous landing pages in different languages is critical for companies looking for global influence. The same is true for your bot. If an AI-powered chatbot can comprehend a user's language, it'll completely change their perception of your brand. Multi-language chatbots will undoubtedly require extensive training. They must understand message intent in various languages and respond without ambiguity. This is why having the correct chatbot solution in place is crucial.
- Use emojis, gifs, and images: Nothing beats a funny gif or emoji to break the ice. Also, emojis can help in capturing the attention of new web users. Pictures or memes can help set the conversational tone. However, they must be employed in moderation. Not every consumer group will enjoy an image, but it's a wonderful alternative if your product or business primarily aims at younger audiences.
- Carefully train the bots and track performance: If you've ever used an AI-powered chatbot, you probably were given some feedback questions at the end of your experience. These frequently contain a question about whether the bot was able to assist you with your inquiry. This input is critical for any business since they can use it to improve the chatbot's functionality. Companies must retain conversation history and track unresolved questions. This helps to spot typical queries the bot cannot answer and train them for future use.
Future of customer service
The future of customer experience lies in the hands of those who can provide the best customer service. And that means using the latest technology to create personalized experiences that are fast, efficient, and relevant. It can be challenging for competitors to replicate highly personalized customer experiences that are scalable to millions of unique consumers. Such experiences, when done correctly, empower organizations to distinguish themselves and establish a lasting strategic advantage.
Personalization of customer journeys can also help businesses enhance customer retention. As a result, you receive more business for less money.
Conversational AI chatbots are at the forefront of this shift, allowing companies to engage with their customers on a more personal level efficiently and cost-effectively. First-party data personalization is the key to creating a better user experience. By understanding how your customers interact with your chatbot, you can make the necessary adjustments to create an experience tailored to their needs.
