AI-based Chatbots - Why Marketers and Start-up Founders Need to Pay Attention?
Is this the right time for chatbots?
Businesses have been implementing different versions of chatbots to enhance customer communication and to streamline operations. However, integration of these chatbots into existing ecosystems can become a tiresome and lengthy process, where you’re left with a shoddy version even after several months.
AI might be looking to change that.
What are Chatbots?
These are computer programs that simulate conversations with humans, often in a human-like manner. It is used to automate certain layers of customer interactions, especially in customer service applications, where they can answer questions, provide support, and resolve issues.
The phone-tree is one of the earlier uses of a chatbot (although ELIZA was the first), where you needed to select an option from a button-menu on your phone based on pre-recorded prompts – Something still used by telecom companies.
Chatbots usually fall into 2 categories –
Task-oriented – These deal with structured interactions where the user requires a particular solution, often working stateless as if interacting with a new user. Food delivery apps often use these when you’re trying to find your order.
Conversational – These can be predictive in nature, especially when stateful and working on data from your past interactions. Medical chatbots often remember your prescription history and your medical conditions. And now, Natural Language Processing (NLP) models are changing the way these conversations occur.
This might be an oversimplification as there are different use-cases for a chatbot, but it’s important to identify the differences for you as a content marketer or a business founder.
It’s easy to believe that you must deploy a complex, AI-driven virtual assistant into your system, but you might just need a simpler chatbot for most processes.
The reason why everyone is suddenly talking about chatbots again is because the new iterations of generative AI have significantly reduced the human effort required to set up a chatbot, by some estimates down 90%.
In 2017, almost 60% of consumers believed that a human could understand their needs better than a chatbot. But according to a study by Drift, since 2019 we’ve seen an almost 92% increase in use. This is the largest growth for any brand communication channel, showcasing the relevance of this tool.
Life before GPT:
Chatbots were generally rule-based and limited in their ability to hold natural conversations.
They were used for simpler tasks such as customer support or dealing with frequently asked questions.
They had limited applications in marketing and sales.
Life after ChatGPT:
Chatbots now have improved natural language understanding. They can pick up context, recognize intent and extract meaning from complex statements.
They’re trained on a much larger dataset, allowing them to refine their responses beyond the limited options that existed before.
They learn, they adapt, they match language styles, they recognize patterns, they even generate content. Large Language Models (LLM) like ChatGPT have allowed chatbots to significantly improve the customer experience, maybe even allowing customers to forget that they’re talking to a bot.
The launch of LLMs had a major impact on the chatbot industry, making them more sophisticated and human-like in their conversations. This surge has the potential to empower businesses with improved customer dealings, increased operational efficiency, and enhanced user satisfaction.
How do you implement chatbots into your business or marketing funnel?
The question that arises is not when, but how will companies integrate chatbots into their workflow. 67% of global consumers had an interaction with a chatbot over the last 12 months, and the industry is expected to hit $4.9 billion by 2032. So what should content marketers and startups do?
Define objectives and use-cases – Determine the specific tasks you want your AI-powered chatbot to handle – lead generation, order tracking, customer support, transaction management, or personalized recommendations. Identifying the areas that require automation, and their expected outcomes, is the first step in efficiently developing and deploying your chatbots.
Select the right platform or tool – There are a multitude of platforms and tools that can develop a chatbot to align with your requirements, like ChatGPT, IBM Watson, Chatfuel. Always look at the features and capabilities – natural language processing, machine learning, sentiment analysis, ease of use, customization options and compatibility for integration.
Use conversational AI APIs to create your chatbots, which provide a pre-trained model to generate natural language responses.
This is a quick and easy method on creating chatbots, but might not be intuitively tailored to your requirements.
Data collection and training – It all starts and ends with data. You need to train your chatbot on relevant data, which includes customer interactions, product catalogs, frequently asked questions, and any other information deemed applicable to the conversation. Ensure that you have all the data that’ll be required.
Design conversational flow – Every brand and business has its own tone of voice that caters to its own customer base. You need to map out the user journey to ensure that the chatbot interactions are in accordance with your expectations. This flow should take into account the different inputs, intent and possible responses. AI based platforms can help with this, especially if you use existing templates.
Back-end systems – You need to consider the requirements that might arise from your IT team (if you have one) - Integrating your chatbot with databases, APIs and other business tools, so as to provide real-time information. Platforms might offer these facilities in-built with their services, but it’s important to factor this when making your selection.
Testing and refinement – As we remove the human element from these systems, it’s important to ensure that your chatbot is tested across various scenarios and user inputs. You want to test its language understanding, response generation and system assimilation. Once launched, you need to track the performance and ensure that analysis of the data allows for further improvements in the chatbot’s capability.
Human back-up – This is a machine after all, liable to breakdowns and inconsistencies, and it’s vital for companies to create a seamless handover to human agents in critical situations.
Downtime for your chatbot can have a significant impact on your business and brand.
Why are chatbots a great tool for strategically using marketing automation and AI?
Customer support – This is the main reason for using chatbots, as it can reduce the human intervention required for resolving problems, answering questions and offering support. AI-powered chatbots can also provide personalization based on user preferences and history.
Lead generation – Chatbots can qualify prospects and collect the required user information through conversational interactions. It can also guide users through the sales funnel by providing product information and tailoring recommendations to their individual needs.
Feedback and research – An under-appreciated aspect of chatbots is the data they can glean for analysis. Based on consumer interactions, brands can refine their product offerings and improve their service models. The customer experience becomes paramount, which can be analyzed in centralized dashboards to help strengthen your marketing strategies and decision making.
24/7 availability – A key benefit is that these chatbots work even when you’re asleep. Customers could require help at any point, especially when you’re dealing with international time-zones. Chatbots can handle multiple conversations and scale effortlessly for increased demands.
Brand differentiation – Having chatbots in your system can showcase your brand as forward-thinking and innovative. It exhibits a commitment to understanding and satisfying the customer experience, when executed correctly. The latest AI tools provide customer personalization and natural language understanding, which can help differentiate the brand in a competitive market and build relationships with customers.
Time and money – Automating certain tasks can have a marked effect on your bottom-line. Chatbots can handle time-consuming and repetitive tasks, thereby allowing reallocation of your human resources to high-value activities, such as strategy and creativity.
Some chatbot development platforms to explore
Google Dialogflow - This is an NLP platform that can help you build conversational interfaces for various use-cases, powered by the same machine learning tech that powers Google Assistant. You can build chatbots and voice assistants for apps, websites, and devices. It also offers integration with various messaging platforms like Facebook Messenger and Slack. Malaysian Airlines used this to create a chatbot for Facebook Messenger, where customers could search, book and pay for flights.
IBM Watson Assistant – A cloud-based AI platform, it can help businesses build and deploy chatbots across channels. Another use of machine learning and natural language processing, the IBM Watson technology can be utilized for training, testing and refining your chatbots. The dashboard is intuitive and doesn’t require any explicit coding. In a partnership with the City of Helsinki, Finland, chatbots were created for multiple services including healthcare, financial services, housing, and infrastructure.
Microsoft Bot Framework – Content marketers and businesses can create and implement chatbots through Microsoft’s comprehensive platform that supports multiple channel integrations. It provides functionality for coding and for no-code development, allowing for a robust use of its libraries and tools to design conversational flows, handling user intent, and generating appropriate responses. The City of Corona, USA, used Microsoft’s tool to allow their citizens to find answers for common questions through a chatbot.
Amazon Lex – A part of the Amazon Web Services (AWS) suite of solutions, Lex is an AI-based chatbot development platform that creates conversational chatbots with seamless incorporation to AWS services. It has integrations with various communication channels, using natural language understanding to process consumer input. Financial services have used this tool for customer interactions via text or voice.
And this is just the start. There are a whole host of platforms like Intercom, Botpress, Kore.ai, Drift, ChatFuel, Pandorabots, etc.
It’s important to note that there are limitations when it comes to the deployment of these AI-powered chatbots, with concerns around accuracy, data security, and integration into your existing systems. If the training data is inadequate, it directly translates to the quality of customer interaction and experience. Dealing with unpredictable customer behavior might also be beyond the scope of your chatbots.
So should you avoid chatbots or try them on some level? Write to us and share your experiences in trying to implement a chatbot.
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