If there’s one thing that decides brand success, it's customer perception. Customers today are looking to make more informed purchase decisions while being open to exploring alternatives, breaking inherent dissonance, and adopting new ideas. This often involves searching for very specific information.
But finding the information isn’t as straightforward as it seems.
With most industries being crowded and competition at an all-time high, customers are often barraged with information they don’t need and seldom recall. And this is where chatbots come into the picture.
What is a chatbot?#
At its most basic form, a chatbot is a computer program that mimics human interactions, automates responses based on specific query types, and redirects conversations to achieve successful escalation or resolution.
Chatbots leverage AI and Natural Language Processing (NLP) to understand human language and carry out appropriate actions without the need for human intervention. Needless to say, customers are able to obtain specific information without having to spend a lot of time searching.
A brief history of chatbots#
Chatbots have reached a high level of maturity, especially over the last few years. But they weren’t always as sophisticated. Chatbots were a result of years of experimentation on bridging the gap between humans and computers. In fact, the first ever chatbot, Eliza was created even before the advent of the personal computer.
Early models (Mid-60s to Late 90s):#
Some of the first chatbots ever created such as Eliza, Parry, or Alice were designed to simulate human conversation. And to a certain degree they were able to generate responses based on specific input. But while these chatbots were groundbreaking at that time, they failed the Turing test, a test that establishes independent thought and intelligence.
The age of the internet (Late 90s to Early 2000s):#
The advent of the internet age pushed development into a new phase of thinking. In the early 2000s, SmarterChild, the brainchild of ActiveBuddy Inc., was made available on AOL and MSN instant messenger. SmarterChild garnered a lot of attention with over a billion messages exchanged a day and over 10 million users at its heyday. It was also widely considered a predecessor to one of the most well known virtual assistants, Siri.
The smartphone age (Early 2010s to present):#
With breakthroughs in smartphone technology came the development of the virtual assistant. These chatbots could do a lot more than just respond intelligently—they could understand human language better through AI, NLP, and ML, perform smart actions, and have stimulating conversations. Siri, Google Assistant, Cortana, and Alexa are a few examples of the newer, more improved virtual assistants.
It’s also worth mentioning that with the recent development of ChatGPT, we’re ushering in an era in which the text generated by Chatbots are becoming indistinguishable from human speech. Exciting times, indeed!
The two cornerstones of chatbots#
Chatbots fulfill a simple function but their effectiveness is intertwined with two functional driving components—AI and data. These cornerstone principles go hand-in-hand and influence complexity and accuracy.
This component is primarily responsible for decision making and execution. While AI breezes through simple task automation, it may struggle with complexity. Whether that’s down to mistranslations or handling requests beyond scope, developers can overcome these obstacles by programming smart prompts that reassign the conversation to a human agent whenever necessary.
Data is a vital component that impacts the accuracy of output. Chatbots are heavily data reliant and the accuracy of the data built into it. The more accurate the data fed into the chatbot is, the more accurate the results. Accuracy also hingest on the chatbots’s ability to interpret the data. And so if the training module isn’t built in a satisfactory manner, it could cause malfunctions, or end up being unpredictable.
Types of chatbots#
Chatbots have innumerable use cases. Whether it’s to help IT streamline processes, help customers find a particular piece of information, or to execute routing tasks, chatbots are commonly built in three forms:
These chatbots function within the confines of a particular script, logic tree, or set of rules. The user’s input often dictates the direction of the conversation which is highly predictable. This also means that the chatbot can only respond in a set number of ways. Prompt-based chatbots are not meant to handle extreme complexity.
Data-driven chatbots are advanced programs that are context-aware and are able to execute a sophisticated variety of functions. Unlike prompt-based chatbots, data-driven chatbots have a higher understanding of data functionality and are able to use predictive analytics, machine learning, and user preferences to provide more intuitive conversation.
AI-based chatbots have an added layer of sophistication. They are able to process natural language, derive context, and make smart suggestions based on user intent. Additionally, they allow users to guide the conversation any way they want. What makes them unique is their ability to gather conversational data over time and create smarter responses.
Chatbots vs Conversational AI vs Virtual Assistants: The difference#
There are a lot of terms that get thrown around in this space and it’s easy to mistake one for the other. While chatbots, conversational AIs, and virtual assistants all fall within the realm of chatbots and are loosely based on the same concepts, it’s important to know that they are not exactly the same thing. The table below gives you an overview of the differences that make them unique.
The inner workings of chatbots#
Behind every chatbot is an architecture that does three very specific tasks:
Intent recognition is the first step that chatbots carry out. The Natural Language Processing engine recognizes conceptual entities (keywords, concepts, language, etc) and numerical expressions (numerical data, dates, times, etc) within input. It then interprets this data to understand the intent of the user and the nature of the task at hand.
Mapping intent to action#
Once the intent of the user is established, the chatbot needs to recognize the appropriate action that needs to be carried out. The more data fed into the chatbot, the more accurate the interpretation and mapping tends to be.
The third and final step is the execution of response. This may be in the form of a simple text response, starting a logic tree conversation, extracting data from a knowledge base, a response based on backend interactions, or specific redirection.
Elevating the customer experience with chatbots#
Chatbots come with a host of advantages. Forward thinking brands deploy chatbots to complement brand image and curate personalized experiences.
Chatbots are great multitaskers—they can engage multiple users at once and can be instrumental in reducing customer wait times, optimizing your support channel, and driving better engagement.
Chatbots are a great way to start conversations with prospects visiting your website. Whether it’s pointing users in the right direction or answering queries, chatbots can help create positive first impressions while greatly simplifying navigation.
Chatbots are highly customizable. This means that different scenarios can trigger varied responses. For instance, the chatbot can be trained to greet returning customers in a personalized manner or point them in the direction of high value content that helps accelerate deal closure.
Stimulating conversations are great but the ability to anticipate needs takes the customer experience to the next level. Chatbots are able to harness customer/visitor intelligence to learn preferences over time and have smarter conversations that are driven by intent.
There have been numerous instances where marketing and sales teams spend a lot of time with prospects only to find they aren’t a great fit. Chatbots can be incredibly helpful in posing qualifying questions and carrying out lead qualification even before your sales teams set up meetings.
Chatbots are great at fulfilling predictable queries but they don’t always have a solution. And this is why they can be trained to consult, or reassign complex tasks to a live agent. This translates to faster resolution and higher customer satisfaction.
Proactive customer interactions#
The importance of capturing customers with high intent cannot be understated. Chatbots can be programmed to notify live agents when high value customers visit your site. This allows them to proactively start conversations with prospects and leverage their intent.
Chatbots generate a goldmine of customer data. This data can come from two avenues—preferencial data from repeated customer interactions and feedback that customers provide at the end of the conversation. Analyzing this data helps brands optimize customer touch points, tweak their website experience, trigger actions based on identified purchase patterns, and remove potential roadblocks to the buying process.
Challenges in implementing chatbots#
Like every new technology, using chatbots comes with a few imperfections of its own. Here are a few obstacles that organizations need to be aware of while implementing chatbots in their CX strategy.
Even though chatbots have been around a long time, integrating them within the CX experience can come at a certain risk. Chatbots using AI and natural language processing require time to learn from typical customer interactions. Visitors may not necessarily understand this since they’re often focused on their objectives. So a faulty experience may put them off, resulting in a loss of potential business.
Security is a big talking point when it comes to customer data. Naturally, first-time visitors are not always comfortable sharing personal information without the knowledge that it will be handled safely. This can raise concerns and cause visitors to hesitate and possibly turn away.
Language and dialect#
Human language is a fluid concept and humans communicate in multiple ways. Dialect and phraseology are variables that can pose significant challenges while recognizing intent. Misspelling words and typos can also trip chatbots up. And unfortunately, Natural Language Processing has a long way to go to find a solution to this problem.
Human nature is very unpredictable. Thoughts aren’t linear and sometimes emotions trump rationale. Chatbots, on the other hand, are found lacking in this respect and cannot quickly adjust to the unpredictability of human command and emotion.
The modern customer craves a warm, personalized touch point. They expect stimulating conversations that are purposeful and pointed. Chatbots are unemotional by nature (obviously) and hence, the lack of emotion results in a natural apprehension to engage. This also means organizations looking to implement a chatbot experience have to constantly monitor, train, and update the program to behave more human-like.
Common chatbot use cases#
Chatbots have a number of conventional and unconventional use cases. They can fulfill a number of roles across departments and functions owing to the dynamic ways they can be molded. With that in mind, here are a few ways in which chatbots can be leveraged.
- Business process and task automation
- Meeting and appointment scheduling
- Customer support and helpdesk
- Data monitoring and reporting
- Employee engagement and payroll management
- Website engagement and query resolution
- Prospecting and lead qualification
- Claims processing
- Healthcare advisory and information
- SaaS self help and deployment
- Retail order placement and returns
Four crucial tips to picking the right chatbot#
Irrespective of the use case, the right chatbot does more than just disseminate information; it drives conversations and is constantly evolving. Picking the right chatbot for your business may seem straightforward but there are nuances that have to be tackled.
Map the exercise to a defined goal#
Implementing chatbots is not exactly a cheap affair. And so it’s really important to carefully align them to a singular business objective. Whether that is to improve website engagement, better prospecting or handle a large volume of customer support tickets, it’s vital that business units are able to see how chatbots address a problem and understand the value chatbots bring to the table.
Always consider the security angle#
Irrespective of how secure a platform is, there are vulnerabilities that don’t immediately meet the eye. Chatbots are no different. When it comes to customer information, it’s important to ensure it is handled, stored, accessed, and purged correctly. Choosing a solution that aligns with the latest security and compliance regulations can go a long way in putting customers at ease while sharing their information.
Implement to improve existing processes#
There is a perceived notion that newer technologies will end up replacing older ones that may just be working well. But this doesn’t have to be the case. While choosing a chatbot, consider how they can help improve processes and expand capabilities over time rather than step over and replace existing processes.
Data plays a big part in how efficient chatbots are. It’s essential to choose a solution that not only generates the right kind of data but is able to interpret, harness, and learn from it. It’s also vital to consider the effort that needs to be put in to feed, maintain, and fine-tune the program to get smarter with time.
Mark Hurd, the CEO of Oracle predicted that by the year 2025, roughly 85% of customer interactions will be automated. This only points towards the fact that chatbots aren’t going anywhere. In fact, the evolution of technology will only give rise to a smarter, more engaging chatbot experience. Not only will the chatbot predictably automate a significant number of processes but will create new avenues of work and change the way we view the world today.