Table of Contents
Introduction
In the past when I interacted with a website’s chat feature, my disappointment usually came with the realization that on the other end of the conversation was, indeed, a bot. These days are different. Chatbots, like human customer service agents, now run the gamut from great to terrible.
At times it's even hard to tell if the agent is of the human or bot persuasion (we’re way past the Turing Test, now), and perhaps it doesn’t even matter that much.
Does a potential client seeking information about a particular product or service really care if a human or machine gives it to them, as long as the information is useful and served up in a timely manner?
I’d bet the answer for most would be a solid “No.” While there’s a loss of human conversation (and potentially jobs) involved, most of us simply need to gather information and make decisions quickly.
You’ll find plenty of articles online with opinions about someone's list of “best” AI chatbots, how to make your own chatbot to answer customer queries, and more. To offer something different, I wanted to go over the basics of devising your strategy.
Are you trying to build your own AI chatbot? Would you like to learn how Mythic can help you integrate them into your business’ web presence? Schedule a discovery session with me here to learn more.
This article will cover:
- the types of AI chatbots,
- how B2B companies use AI chatbots,
- managing the AI chatbot to human workflow,
- using bots for knowledge management,
- helpful context you’ll need to provide the bot,
- the question of chatbots instead of FAQ’s,
- some potential downsides to using AI chatbots.
What Types of AI Chatbots Are Out There?
We’ve all interacted with them in our online lives, but how does one start to think about these increasingly-frequent interactions? AI chatbots can generally be categorized into two types based on their level of sophistication and capabilities:
Rule-based and Self-Learning Chatbots
These topics can get a bit technical, but I’ve distilled down the basics for you here. Let’s go in ascending order of complexity.
Rule-Based Chatbots
As a business, your first stop on the AI chatbot journey will probably be here.
These are the simplest type of chatbots, which respond to user queries based on a set of predetermined rules upon which they were initially programmed.
They can handle basic inquiries and follow decision-tree pathways, but they lack the ability to understand complex requests or engage in natural, free-flowing conversations.
Rule-based chatbots are best used for simple, straightforward tasks such as answering FAQs or directing users to the right resources.
Self-Learning Chatbots
These chatbots leverage advanced AI technologies like Machine Learning (ML) and Natural Language Processing (NLP). There are two subtypes within this category:
Retrieval-Based Chatbots
These AI chatbots work by selecting a response from a fixed set of responses pre-defined in their system.
They use ML algorithms to match one’s queries with the most appropriate response, and they can handle more complex interactions than rule-based chatbots.
Generative Chatbots
These are the most sophisticated types of chatbots, capable of generating their own answers rather than choosing from a predefined set.
They use a type of ML known as sequence-to-sequence neural networking, which makes them capable of handling complex conversations and providing more human-like interactions.
Hybrid Chatbots
Yep, you guessed it. A hybrid bot is a combination of rule-based and self-learning bots.
They are programmed with a set of rules for handling basic queries and also equipped with machine learning capabilities to handle complex conversations. This makes them more versatile and capable of potentially delivering more customer engagement.
It's important to note that the choice between these types of chatbots will depend on the specific needs and resources of your company, such as:
⭐️ the complexity of your customer interactions,
⭐️ the resources you can devote to chatbot training and management, and
⭐️ your desired level of customer service.
The landscape of AI chatbots presents many options, each with its unique strengths and ideal applications.
Whether your interactions are straightforward or complex, whether your resources are vast or limited, there are likely AI chatbots that fit your mold.
How are B2B companies using AI chatbots?
In 2023 and beyond, AI will continue playing a significant role in B2B lead generation in many ways, one of which is chatbots. While there’s truly no substitute for real, human conversation, let’s look at how you can make use of an artificial intelligence chatbot.
24/7 Customer Service
AI enables chatbots to understand and respond to customer inquiries in natural language around the clock. Machine learning algorithms allow chatbots to learn from previous interactions and improve their responses over time. This can help you in automating customer service, at least at some level for certain businesses.
A word of caution, though. Please make sure your new virtual agents have a clearly defined protocol to direct potential customers to a live person. If the interaction happens after business hours, the bot can give accurate answers about when they can expect a response (see “Escalation Protocol” below).
In some instances, real customer conversations can give you insights into your business that an AI chat might miss. A balanced approach is always wise, and here especially.
Qualifying Leads
Qualifying leads is crucial to any sales process, and AI chatbots can really streamline and enhance what we can do here. They can not only save valuable time by automating the initial stages of lead qualification but can also increase accuracy by using consistent criteria.
AI chatbots can be programmed with predefined criteria to assess the potential value of a lead.
This could be based on factors such as:
⭐️ the size of the company,
⭐️ the lead's role in the company,
⭐️ their industry, and
⭐️ their stated needs or interests.
For example, a chatbot might prioritize leads from larger companies or those who express a strong interest in your product or service.
But AI can go even further by using machine learning algorithms to learn from each interaction. Over time, the chatbot can refine its understanding of what makes a valuable prospect, improving the accuracy of its qualification process.
Moreover, AI chatbots can integrate data from various sources to form a more comprehensive lay of the land, in this case your “Land of Leads.” ?
For example, it might combine data from the chatbot conversation, the lead's behavior on your website, and information from your CRM system. This can help the chatbot determine not only the potential value of the lead but also the best way to follow up with them.
Finally, AI can use predictive analytics to estimate the likelihood of a prospect converting into a customer. This can help your sales team focus their efforts on the most promising possibilities, improving efficiency and increasing the chances of making a sale.
Data Collection
While it’s always good to avoid cliches, artificial intelligence is a game-changer when it comes to collecting and interpreting data during customer interactions. As users engage with an AI chatbot, they provide a wealth of information, both directly and indirectly.
The low-hanging fruit is direct data, such as contact details or specific inquiries. This is typically easy to extract and classify.
However, an equally significant but often overlooked data source is the implicit, or indirect, information gleaned from user interactions. This can provide valuable insight into a lead's interests, behavior, and preferences.
But what exactly do we mean by “implicit/indirect information?”
An AI chatbot can interpret the implicit data by analyzing the context and content of a user's inquiries. For example, if someone repeatedly asks questions about a specific product or service, the chatbot can infer that this is an area of interest for them.
It can analyze the frequency, depth, and specificity of the queries to understand the writer's intent and degree of interest. This type of data can be invaluable for tailoring personalized marketing strategies and improving product or service offerings, even in B2B contexts.
Thinking over a longer time horizon, advanced AI chatbots can utilize machine learning to identify patterns or trends in the data over time. They can correlate a user's questions or comments with other data points, such as the time of interaction, the user's location, or their browsing history.
Powerful stuff!
These correlations can reveal even deeper insights into user behavior and preferences, helping businesses predict future actions, identify potential opportunities, and deliver an enhanced, personalized experience.
By making the most of both explicit and implicit data, businesses can optimize their engagement with leads and reach closer to maxing out the effectiveness of their marketing efforts.
Appointment Scheduling
Artificial intelligence is rapidly transforming everything, and the way businesses handle appointment scheduling is no exception.
AI-enabled chatbots, specifically, are equipped to interpret and manage scheduling requests, and can book appointments without needing any human touch.
This capability not only enhances efficiency but also greatly improves the user experience by providing immediate responses.
The key advantage of using an AI chatbot for scheduling lies in its ability to comprehend natural language queries related to bookings.
Whether a person wants to schedule a new appointment, reschedule an existing one, or inquire about availability, the chatbot can interpret the request and respond accordingly.
It can understand various ways users may ask for appointments, and even handles time zone conversions if needed.
Once a request is made, the AI chatbot cross-references the desired appointment time with a connected calendar system, ensuring that there are no conflicts.
It then books the appointment and sends a confirmation to the user, all in real-time. This automated process eliminates manual errors, reduces scheduling conflicts, and frees up your team's time to focus on more complex tasks.
Furthermore, AI chatbots can send out reminders as the appointment nears, decreasing no-show rates and generally, hopefully, making our lives easier ?.
By providing a smooth and efficient scheduling experience for website visitors, AI-powered chatbots have the potential to boost overall customer satisfaction and operational efficiency.
Content Delivery
They say that “content is king,” but every monarch needs to ensure that their messages reach the right people, at the right time, in the right way.
Enter AI chatbots, the new-era emissaries, adept at content delivery tailored to each individual's unique interests and behaviors.
AI chatbots can gain insights into a lead's interests by analyzing their interactions and conversations. Through Natural Language Understanding (NLU), a subset of Natural Language Processing (NLP), chatbots are getting better at comprehending the context and nuances of a lead's queries or comments.
This allows our AI chatbots to identify key themes or topics that the lead is most interested in.
With machine learning algorithms, these tools can even predict the type of content a lead is most likely to engage with. Such predictions are often based on past behavior, like the type of content the lead has interacted with previously, the time they are most active, or the format they prefer - be it text, video, or interactive content.
With these insights at hand, our chatbot tools can curate and deliver a content experience that is not only relevant but also highly personalized. This can lead to higher engagement rates, stronger relationships with potential clients, and ultimately, a higher likelihood of conversion.
In essence, AI chatbots act as intelligent content delivery systems, bringing together the ideal content with the right lead at the right moment. The idea here is to bridge the gap between content creation and content consumption, making content marketing more effective, efficient, and dare I say…fun?!
Personalized Experience
One really can’t understate the power of personalization in today's digital landscape. AI chatbots are at the forefront of delivering such tailored experiences.
They are capable of harnessing data collected from user interactions and utilizing this information to customize their responses to match the specific needs and preferences of each lead.
When a user engages with an AI chatbot, they leave digital breadcrumbs that reveal their interests, concerns, and preferences. This could happen via the questions they ask, the topics they delve into, or even the language and tone they use.
An advanced AI chatbot can parse and interpret these data points, crafting a personalized profile for each lead.
Personalization could manifest in several ways. For instance, the chatbot might adapt its communication style to mirror our own. If we use formal language, the bot could respond in kind. If our language is more casual, the bot could switch to a more informal tone.
This mirroring helps create a more comfortable and engaging conversation for the audience.
But personalization extends beyond language. Leveraging machine learning, AI chatbots can offer customized recommendations based on a potential customer’s expressed interests or implied needs.
For example, if a user repeatedly asks questions about a particular product or service, the bot can recommend related content or complementary offerings. This could involve suggesting blog posts, white papers, case studies, or even product demos that align with the user's interest area.
Customer Segmentation
AI can analyze the data collected from leads to group them into segments based on shared characteristics. This could involve clustering algorithms to identify groups with similar needs or behaviors, or classification algorithms to predict which segment a new lead is likely to belong to.
In each of these cases, AI not only automates tasks that would otherwise require a human hand, but also enables more sophisticated, personalized interactions that can improve the lead generation process and customer data analysis.
Managing Workflows Between Chatbots and Human Agents
Managing conversations between AI powered chatbots and real people is a critical part of using chatbots in a B2B context. This often requires a balance of automation and human interaction to provide the best possible customer experience. Here's how you can get it done:
Define Triggers for Human Intervention
Depending on the complexity of a query, a chatbot (or virtual agent) may need to hand off the conversation to a human agent. Triggers can be defined based on specific keywords, phrases, or the sentiment of a person’s response.
Use your existing knowledge, customer service training materials, and more to help determine important language that’s specific to your business. AI chatbots can learn as they go, but give them as much of a head start as you can.
Hybrid Customer Service
One can apply a system where chatbots handle the initial customer interactions and common inquiries, but if the customer's queries get too complex or if they explicitly request it, the chatbot transfers the conversation to a human agent.
The transition should be smooth and the human agent should have access to the entire chat history.
Unified Dashboard
Try implementing a unified interface or dashboard where staff can monitor chatbot conversations in real-time and intervene when necessary. This can also be a platform where customer service agents can manage multiple conversations at once, both those initiated by themselves and those transferred by chatbots.
Training & Feedback Loop
Just like people (though perhaps not quite as well, yet), AI can learn. Think about using the data and outcomes from the interactions between chatbots and human agents to continuously train and improve the AI models.
This can help reduce the need for human-based work over time, allowing your customer experience team to focus on the trickier and more nuanced needs of your audience.
Escalation Protocol
We can’t have a useful chatbot strategy without an effective escalation protocol.
This is a set of guidelines that delineates when the chatbot should step back and hand the conversation over to a human agent. One needs a balance between automation and human touch, ensuring that while the chatbot handles routine tasks efficiently, complex or sensitive issues are addressed with a human understanding and intervention.
This transition could be triggered by any number of factors. One of them is the complexity of a query. While our chatbot friends can tackle many inquiries, certain issues might be too intricate or unique for them to handle, necessitating the involvement of a human agent.
The protocol may also be triggered based on a customer's sentiment. AI chatbots can be programmed to gauge the emotions behind a writer’s messages. If a customer seems frustrated or angry, it might be beneficial to escalate the conversation to a human, who can handle the situation with empathy and nuance.
And there’s even more: the number of unsuccessful attempts a chatbot makes to resolve a query can also be a factor. If the chatbot is unable to give a useful answer after a certain number of tries, the conversation should be handed over to a human agent to ensure customer satisfaction.
In essence, a well-defined escalation protocol can significantly enhance the effectiveness of an AI chatbot, ensuring that it delivers the best possible customer experience. It’s a pivotal piece in the puzzle of blending automated efficiency with human understanding.
Post-Interaction Surveys
After a conversation has been handed over from a chatbot to a human agent, it can be useful to conduct a brief survey to gather the customer's feedback on their experience. This can provide valuable insights for improving the process.
Remember that the goal is to provide the best possible customer experience. While chatbots can handle a large volume of interactions and answer many questions accurately, human agents are still crucial for handling complex issues, adding a personal touch, and negotiating tricky customer relationships.
How Can AI help with Knowledge Management?
Using AI chatbots to recommend resources is a great way to provide value to your customers and guide them towards the information they need.
To put this properly into effect, make sure to think things through. There’s a lot to consider, but with the right plan and strategy, this tool could make a huge difference in how long you keep eyeballs engaged with your content.
Intelligent responses delivered with ease are what we’re aiming for here.
Cataloging Your Responses
Start by creating a detailed inventory of your resources. This might include blog posts, whitepapers, tutorials, webinars, product manuals, FAQs, case studies, etc. Make sure each resource is tagged or categorized based on its content, purpose, and target audience.
Nobody wants to spend more time than needed on routine tasks, like searching for a much-needed resource.
Integrating with the Chatbot
Feed this resource inventory into your chatbot's knowledge base. Depending on the chatbot platform you're using, this might involve uploading a database, integrating with an API, or manually entering the information.
User Interaction
Crafting a dynamic, engaging, and meaningful user interaction with a chatbot involves careful planning and thoughtful strategies. Here's a deeper look into how you can optimize chatbot engagement for recommending resources:
Contextual Understanding
The chatbot should have the ability to comprehend the context of the writer’s query. It needs to identify keywords, phrases, or sentiments that help determine the user's requirements or interests.
For instance, a person who is asking about advanced features of a product might benefit from a detailed technical guide. Alternatively, someone asking about troubleshooting might need to know about support tickets.
Dialogue Flow Management
Ensure that the chatbot's conversation flow is designed in a manner that facilitates resource sharing at appropriate moments. You would never want a human agent to abruptly interrupt the conversation to recommend useless or irrelevant resources but you do want them to add value to the ongoing discussion.
An AI chatbot is no different. It's important to carefully design, test, and refine your conversation flow. Use staff, long-standing clients, or even focus groups to help come up with as many different chat scenarios as possible. More will come as you go along.
Adaptive Suggestions
The bot should be capable of dynamically adjusting its suggestions based on our responses. If you show interest in a specific topic, the bot should prioritize articles for you related to that topic.
Balancing Proactive & Reactive Behavior
While the bot should proactively suggest resources that might be helpful, it should also respond reactively to explicit user requests. If a user asks for resources on a specific topic, the bot should be able to quickly provide relevant recommendations. Again, reiteration is key here.
Layered Learning
For those involved in continuous or repetitive interactions, the bot should progressively suggest advanced resources. Starting with basic guides or tutorials, it can gradually recommend more in-depth resources as users demonstrate increased understanding or interest.
This factor will help you in choosing among the best AI chatbots out there. The level of sophistication needed will be a big factor in your decisions.
User-Friendly Behavior
When presenting a resource, the chatbot should provide a brief description along with it, highlighting why the information is beneficial. Nobody wants to see a link or a document without any context.
That will certainly not engage a prospect effectively.
Encouraging Engagement
The chatbot can stimulate user engagement by framing the sharing of resources as a chance to learn more or solve specific problems.
Phrases like "You might find this guide helpful..." or "Our clients often find this tutorial useful for..." can encourage users to engage with the recommended resources.
By focusing on these aspects, you can improve the human-chatbot interaction, make it more meaningful, and significantly enhance the effectiveness of your resource recommendations.
Natural Language Processing
Most like those powered by OpenAI's ChatGPT already do, but make sure that your chatbot uses NLP to understand user queries in a natural, conversational way. This will allow it to recommend articles that are most relevant to the user's needs and interests.
Feedback Loop
Give your clients the option to provide feedback on the recommended resources. Ask them to rate the usefulness of the resource, or provide some comments. This feedback can then be used to improve future recommendations.
Track Engagement
Monitor how your prospective clients interact with the recommended resources. This can help you understand which types of resources are most valuable to your users and guide your future content creation efforts.
Personalized Recommendations
As your chatbot learns more about each user's preferences and needs, it can start to provide personalized resource recommendations. This might involve using machine learning algorithms to predict what types of resources the user is most likely to find useful.
Remember, the goal of recommending resources is to provide value to your customers and help them get the most out of your products or services. Therefore, it's important to regularly review and update your resource inventory and ensure your chatbot is effectively guiding everyone towards them.
What Level of Context will Your Chatbot Need?
Depending on the complexity level of your business and customer service, perhaps a lot. The more detailed context you can input, the better the chatbot will serve you. With increasingly advanced AI technology, the relevant context types will continue growing.
For the sake of time, I’ve narrowed down the field to 7 areas that shouldn’t be overlooked.
Product or Service Knowledge
Our chatbots need to have detailed information about our products or services. This includes their features, benefits, how they work, pricing, and any other information that might be relevant to potential leads.
Customer Profiles
It's important that the chatbot understands the types of businesses that typically use your products or services. This includes their industry, size, location, specific pain points, and how your offerings can help them.
Industry Terminology
Don’t leave out the office jargon. The chatbot should be familiar with the terminology used in your industry and by your customers. This can help it better understand inquiries and provide more accurate responses.
Sales & Marketing Information
Make the chatbot aware of your sales and marketing strategies, such as ongoing promotions, upcoming product launches, or special events. Commonly used terms can be listed out and developed. Any specific lingo that your team uses would be captured here, too. Try not to leave anything out and be sure to seek as much input from your team as possible.
Company Information
The chatbot should have knowledge about your company, including its history, values, and key personnel. If someone wants to learn about your company, and in detail, ideally this new tool will help improve customer satisfaction by getting them the information they need.
Conversation History
One of the key elements of context in chatbot interactions is the history of the conversation itself. The chatbot should remember previous interactions with each customer, including the information they've provided and any issues they've had in the past.
This is one area where an AI chatbot can really shine over a human agent, given our limited capacity for complex and repeated memorization.
Integration with Other Systems
To provide the most accurate and helpful responses, your chatbot should be integrated with your other business systems, such as your CRM, calendar, or product inventory. This can help it access and provide real-time information.
Such integration capabilities will also help guide you in choosing the best AI chat app, platform, or solution.
Yes, Chatbots Can Answer FAQs
Frequently Asked Questions (FAQs) have been with us for some time now, and not much has changed in the way they are written and used. This makes them a perfect opportunity for an update with AI-assisted chatbots. They can be programmed to understand and respond to a wide range of common questions based on a predefined knowledge base.
Let's go a bit deeper.
Understanding Questions & Providing Answers
AI chatbots use Natural Language Processing (NLP) to understand the customer's query. This technology allows the bot to comprehend the context and sentiment behind a user's message, even if it's not worded exactly like the given questions.
Once the chatbot understands the question, it can provide the appropriate pre-defined answer from its knowledge base. This response can be a direct answer, a link to the appropriate page on your website, a document download, or anything else that might be useful.
Be sure to consider all the possibilities, while learning and refining as you go.
Handling Unrecognized Questions
This is where things get really interesting. If the chatbot doesn't recognize a question or isn't confident in its ability to provide the correct answer, it can be programmed to escalate the query to a human agent. The agent would then provide the answer, and the chatbot can learn from this interaction for future reference.
AI-assisted chatbots can provide instant, accurate responses to FAQs, freeing up your customer service team to handle more complex queries. They can also be available 24/7, providing customer support even when your team is unavailable. This makes them a valuable tool for enhancing your customer service and improving customer satisfaction.
There's Always a Catch: Potential Downsides of AI Chatbots
I’ve been going on and on about what AI chatbots can do for us, but there can be potential downsides associated with their use, especially if not properly implemented or managed. Here are a few to consider.
Limited Understanding
Even with the most sophisticated Natural Language Processing capabilities, AI chatbots may sometimes fail to comprehend complex or ambiguous queries, especially those that involve industry-specific jargon, slang, or regional dialects.
This can lead to misunderstandings and frustration for the user. Not fun.
Lack of Human Touch
For the record, this is definitely a good thing in our world. Chatbots cannot fully replicate the empathy, emotional understanding, or creativity of a human interaction. Obviously this can be a detriment in situations where a customer is upset or has a complex issue that requires a more sensitive, personalized touch.
Keep your bots close, but your humans even closer.
Data Security & Privacy
Since chatbots collect and process user data, they need to be designed with strong security measures to protect this data. Privacy concerns can arise if users feel their information is not being properly safeguarded.
Be sure to discuss this carefully with your IT team, or schedule a discovery call with Mythic and we'll discuss your needs.
Implementation Challenges
Depending on the complexity of one's AI chatbot, the implementation process can be time-consuming and resource-intensive. Without proper planning and technical expertise, there may be challenges in integrating the chatbot into existing systems and workflows.
Training & Updating
AI chatbots require ongoing training and updating to ensure they can handle new types of inquiries and keep up with changes in products, services, or business practices. Neglecting this aspect can lead to a decline in the chatbot's performance over time.
Dependency
This is an important one. Over-reliance on chatbots can lead to reduced human support, which might affect the quality of customer service, especially in handling complex queries that are beyond the capabilities of AI.
While we all want to make full use of this new technology, too much dependency on anything is not ideal. Like with most things in life as in business, shoot for a balance.
Inaccurate Lead Qualification
In the context of lead qualification, an AI assistant may occasionally misjudge a lead's potential value due to lack of context or nuance that a human might understand.
While these potential downsides exist, many can be mitigated with careful planning, design, and management of the AI chatbot.
Regular reviews and updates, strong data protection measures, and maintaining a balance between AI and human customer service can all help to optimize the benefits of AI chatbots while minimizing the downsides.
Navigating the New TOFU
AI chatbots open a world of immense possibilities to marketing professionals, especially with advancing “top of the funnel (TOFU)” sales opportunities. Maneuvering this promising landscape, however, isn't without its potential challenges and pitfalls.
Chatbots might struggle to comprehend complex queries because of their predefined programming. Beyond that, privacy concerns always arise when dealing with sensitive user information. We all know that ensuring secure data handling is paramount.
Implementing these technologies isn't a walk in the park; it requires technical expertise, time, and resources.
But these hurdles are not impossible to jump. You need a well-thought-out strategy and ongoing updates to improve the chatbot's learning and adaptability. Couple this with a balanced approach that harmonizes the speed and efficiency of automation with the warmth and understanding of human interaction, and you’ve really got something.
In the grand scheme of things, the challenges associated with AI chatbots represent growing pains of an innovation in its relative infancy. With careful handling, they can help us all grow and mature into something better (and more advanced) than before.
Conclusion
It’s not an overstatement. AI chatbots are a potent set of tools, one that enables companies to reimagine the landscape of B2B interactions.
By optimizing lead generation, qualification, data collection, and even appointment scheduling, they up our business efficiency game, all while enhancing customer experience. Our new implements can intelligently interpret direct and indirect data, provide personalized interaction, and automate repetitive tasks.
In our rapidly evolving digital world, AI chatbots are worth considering seriously. Are they the latest tech trend, waiting to fade away? I think not.
But they are a strategic investment for any businesses aiming to optimize their processes, drive customer engagement, and stay competitive.
By making the most of what they do best while effectively navigating their around challenges, organizations can harness the power of AI chatbots to take their B2B interactions and operations to the next level.
Let the humans focus on more complex issues. Am I right?