Thanks to the rise of generative AI large language models, organizations of all types are looking to chatbots to enhance customer and employee experiences while reducing the cost to serve and creating operational efficiencies at scale.
“The global chatbot market is valued at $15.57 billion in 2024, and by 2029 the market will grow to $46.6 billion.” — Exploding Topics
There are some standout industries and experience areas that will fuel the AI-powered chatbot acceleration, especially when it comes to supporting multi-step complex tasks and predicting or personalizing interactions. For example:
- In ecommerce moving from answering the question “is a size large available?” to recommending a small list of products in a size large that are associated with past browsing and purchase history.
- From answering a question about when an appointment is to scheduling the service start to finish including negotiating availability and sending appointment reminders.
- Chatbots are evolving to support and maintain continuity across multi-channels (e.g., social media, messaging apps, websites) and moving from expected customer service discussions to complex customer interactions like supporting a car purchasing decision, executing banking transactions, or vacation planning.
- Moreover, Chatbots are helping employees by improving their ability to perform job duties, supporting their employment journey, and servicing customers – – all with less up front training, fewer mistakes and better results!
In this point-of-view, we will provide an overview of chatbots – including benefits and potential drawbacks, 7 key steps for implementing the technology, and common pitfalls to watch out for.
Understanding organizational context and rationale is a key ingredient for success.
While there are many potential benefits to chatbots, there are also drawbacks if not implemented appropriately and with a sound strategy as a baseline. Some of the key advantages include faster customer service, 24/7 availability, cost efficient management of customer service requests, data collection and integration, and ability to support several languages.
Conversely, some drawbacks include a lack of understanding of natural language leading to misinterpretation, lack of personalization or emotion, potential maintenance and expense, ethical issues and privacy concerns.
Advances in data and AI are quickly pushing these experiences up the value chain. The horizon is close where we are able to move beyond scripted conversations to truly understanding and solving human problems via digital real-time interactions. There could be a day, in the not-too-distant future, where ‘talking’ to a chatbot fully replaces any type of live human interaction (think of talking to an ‘AI Advisor’ as opposed to a chatbot).
It’s important to understand that chatbots aren’t right for every business model and proper evaluation should be performed prior to deciding to implement this technology.
A deep dive into generic and custom chatbots.
Let’s dissect the two most common types of chatbots that organizations consider, each with their own pros and cons.
- Fixed bots (generic) that offer a limited set of capabilities and require manual updates to change their language and assistance aptitudes.
- AI-based (custom) bots that use dynamic learning and self-updates based on customer interactions.
- Hybrid bots utilize features of both generic and custom bots and are becoming increasingly popular due to their enhanced sophistication with lower costs.
Generic or fixed chatbots are especially useful as a tool for providing basic sets of pre-packaged information and handling basic customer inquiries. These bots can be purchased and designed off the shelf and implemented with limited effort and low budget. While generic chatbots are handy, they may not always hit the mark as a solution, often due to the dynamic nature of a customer’s question. The primary example of a generic chatbot is menu-based, leveraging a decision tree structure, and working based on if-then logic to help create an authentic conversation flow within guardrails. Rules-based bots are most effective when they are built around high frequency customer or employee queries. In practice, examples of these bots include informational FAQs and support for basic recurring queries.
AI or custom chatbots are often built from the ground up with a specific business use case in mind. They are typically utilized when complex customer interactions are involved, deep integration with existing systems is required, and when organizations want to utilize a chatbot to maintain or enhance its brand perception. Custom AI chatbots are more expensive to build and maintain, requiring a higher degree of technical skill and deeper understanding of the business and customer. The most common types of custom chatbots include NLP (natural language processing), ML (machine learning), and voice bots. For example, sales bots (recommendations, offers), lead generation (captures and transacts based on customer information), and voice assistants (e.g., Alexa, Siri) are the most commonly used custom bots.
Keys for effective implementation
According to a recent Forrester study, nearly 50% of customers feel frustrated and have negative opinions of a brand due to a chatbot experience. When bots fail to deliver a positive interaction, it ends up doing more harm than good, leading to negative perception and resulting in engaging the highest cost service option–live agents. Therefore, getting chatbot implementations right – from assessment and planning through launch and promotion – is paramount.
Below are 7 key steps and considerations for driving an effective custom chatbot implementation, along with some common pitfalls to avoid.
- Define the purpose
- Identify specific use cases, goals and objectives for the chatbot. For example, what specific challenges do your customers face when interacting with your business? Inform and prioritize these use cases by analyzing your data and talking to customers.
- Determine your core target audiences and outline clearly functionalities and features that align to their personas.
- Complete a cost/benefit analysis with a specific focus to understand if the volume of inquiries and tickets merits the cost of the AI investment. Additional considerations should include whether there are 24/7 needs or other factors that may drive up costs, and whether lower cost solutions exist (e.g., FAQ’s) that could offset the need for a custom, more expensive chatbot.
- Collect and curate the data
- Gather relevant datasets for training the model.
- Ensure data quality and diversity to avoid biases.
- Leverage live user conversations to guide data collection.
- Choose and train your model
- Choose between fine-tuning an existing model (e.g., GPT, NVIDIA NeMo) or training a custom model from scratch.
- Determine whether to leverage a single model or multi-bot architecture depending on the need for complexity and scale; the latter is more necessary when covering multiple user cases or domains and complex interactions (e.g., informational, customer service, financial, etc).
- Implement transfer learning techniques for efficiency.
- Develop fallback mechanisms for handling unexpected inputs.
- Promote natural language understanding
- Implement intent recognition and entity extraction.
- Develop sentiment analysis capabilities and enhance language with custom dictionaries and synonyms.
- Calibrate response generation
- Fine tune the model for generating contextually appropriate responses.
- Implement response filtering to ensure safe and relevant outputs.
- Develop mechanisms for personality consistency in responses.
- Integrate and deploy
- Develop APIs for seamless integration with various platforms.
- Implement security measures like encryption and authentication.
- Set up monitoring and logging systems for performance tracking.
- Test & optimize
- Conduct extensive user testing to identify areas for improvement.
- Implement A/B (split) testing for different conversational strategies.
- Continuously refine the model based on user feedback and performance metrics.
Common Pitfalls for Custom Chatbots
Introducing chatbot technology into a business environment can be a large undertaking and have dire results without the proper planning and execution. Below are some common pitfalls when building custom chatbots.
- Lack of clear purpose: Without a well-defined goal, chatbots can quickly become generic and ineffective.
- Overcomplicating conversations: Trying to make your chatbot too human-like can backfire. Keep responses simple, direct and focused on solving problems efficiently.
- Neglecting continuous training: AI models improve with feedback. Regularly updating responses based on user interactions ensures your chatbot stays relevant and effective.
- Failing to integrate with existing systems: A chatbot that doesn’t connect with your CRM, email, or scheduling tools can create more manual work instead of reducing it.
- Not monitoring performance metrics: Without tracking engagement, response time and user satisfaction, you won’t know if your chatbot is truly adding value.
At Cuesta, our team possess deep experiences in helping customers to develop and operationalize cutting-edge digital experiences starting with building deep organizational data maturity. Want to learn more about our view? Reach out to one of our experts today, and we’d be glad to discuss.
