Chatbots or conversational AI has taken a quantum leap from the very first endeavor – ELIZA.
Contributing towards customer support and highly interactive personalized services – chatbots are offering all the concrete services of a modern service desk. Bundled with the functionalities and capabilities of ML, NLP, sentiment tracking, predictive analytics, and adaptive learning, they are undoubtedly versatile.
With 32% of the customers viewing chatbots as just the right solution for support, the popularity of chatbots is soaring.
However, the current business landscape is under constant metamorphosis with rapid technological evolution, and digital maturity is a must!
Here, we discuss ways chatbots have matured in 2022 and how businesses, ranging from small businesses to large enterprises, can make the most of these matured bots.
Chatbots – Chronological Evolution
During the initial stages of chatbot development, developers employed core Natural Language Processing (NLP) methods as machine learning was still not a viable option. Gradually ML methods allowed ways to channel more code and data.
The first revolutionary step was the 2018 paper BERT from Google that transcended the research boundaries and introduced SSA or Sensibleness and Specificity Average. It is an important attribute for human conversations.
However, two notable mentions that laid the groundwork for the modern-day chatterbots are:
- ELIZA (1964 – 1966)
When it comes to the chronological evolution of rule-based chatbots, we have MIT’s ELIZA (1964 to 1966). The program was a massive hit, and when tested over the psychiatric patients, they perceived it to be a human.
ELIZA utilized pattern-matching and substitution tech and demonstrated how superficial human conversations could be.
- Cleverbot (1997 – 1998)
Created by a British AI scientist, Cleverbot was a web-based chatbot that used AI for interacting with humans. Core NLP and Fuzzy Logic could tackle millions of stored records and had a heuristic approach towards its working. Currently, the bot is maturing by adopting ML techniques as well.
As the list can stretch a lot, we offer the most notable mentions in the following visual:
Some other notable primitive chatbots that deserve mention are:
- Mitsuku — 2002
- Rose — 2011
- Xiaoice — 2014
- Melody — 2015
- Meena — January 28, 2020
- Blender — April 29, 2020
As chatbots were evolving, their ubiquity and popularity revealed the deficits and technology gaps they had. One of the foremost ones was – the lack of AI capabilities. Most of them remained a computer program that simulates human conversations based on core NLP rules.
While they could pass as human staff, as the rule-based chatbots, their capabilities were limited to presenting simple FAQ content. Further, the time to value was also huge! It took somewhere between 9 to 12 months to build and deploy a chatbot!
Further, the traditional or primitive chatbots required manual training that would span over six to nine months and consume expert efforts of engineers and data scientists.
Finally, this training was an ongoing event because the conversational chatbot couldn’t learn autonomously.
These hurdles presented the new technological foundations for tech giants like IBM, Apple, and Facebook.
Another chapter is how these giants transformed conversational AI into automated chatbots with intelligent and advanced functionalities!
However, some of the major functionalities that were added during this age, or between 2010 and now, are:
- Adaptive and automated learning
- Personalization
- Instant build-and-deploy chatbot platforms
- Smart and lucid interactions
- Gathering business and market intelligence
- Collecting contextual data from visitors or customers
Take a look at the following visual showing the evolution of chatbots based on use cases:
Up next, we discuss how chatbot usage changed the times and lives of customers, developers, business owners, and academicians all over the globe!
What Changes Have We Noticed After Chatbots Came to the Market?
With market projections hinting at the worldwide chatbot market share to be 1.25 billion U.S. dollars in 2025, we can conclude one thing safely:
Automated chatbots are here to stay!
Let us begin the section with an engaging visual that shows various ways businesses organize across the world using automated chatbots:
Now, let us discuss how we arrived at this stage!
Chatbots offered two major functionalities – messaging and technology! Over the years, they transformed from vague FAQ content presentation programs to smart and lucid conversation masters. They have evolved in terms of interface, functionality, and significance to the business world.
Below, we discuss the changes spawned by chatbot adoption in the business world.
- Organizational Tasks and Roles
Chatbots are great conversational agents and offer obvious sales, marketing, customer service, and support benefits. Automated and ML-powered chatbots became a smart and efficient replacement of a customer service provider. They are online 24X7, 365 days a year!
They are never on a holiday, and never get angry or frustrated while serving the same information repeatedly.
Results:
- Ground-level customer support is now automated
- Chatbots present self-help material and resources as well
- Smart and interactive chatbots present recommendations
- Customer support reps and sales reps can now focus on more productive work
- The fintech sector has 24X7 support with the latest market digs for the investors and stakeholders
- Better UI and Smart Capabilities
Once adopted, chatbots evolved continuously, getting spearheaded functionalities and highly versatile in the process. A noticeable shift from dry UI to CUI or conversational UI transformed the entire human-bot-interaction experience.
Now, smart automated bots can entirely replace a human agent and assist the customers and employees in various ways.
Results (Now chatbots can):
- Offer product recommendations
- Have lucid customer interactions
- Take orders, check status, and make bookings
- Complete financial transactions
- Handle complex queries
- Improve marketing campaigns
- Manage the investments and cryptocurrency trading without human intervention
- Custom Chatbot Building
While you might think that such high-end capabilities from the most advanced chatbots are not affordable in the SME ecosystem, think again!
Several chatbot development platforms or legacy chatbot vendors for the business sector offer custom chatbot development. You can find options that require some coding skills and the ones that don’t require coding skills.
A number of chatbot tools come with an easy-to-use drag-and-drop editor via which you can create and deploy a fully functional custom chatbot within minutes!
Furthermore, these tools offer forever free versions, and different paid plans suitable for business enterprises of different types and scales.
Results:
- More and more business organizations are adopting chatbots
- Workplace productivity is on the rise
- Contextual data is always available
- Employees have more business and market intelligence collected by custom chatbots
How Have Chatbots Matured in the Current Scenario?
In this section, we aim to explore five ways in which we’ve seen chatbots mature lately. They will also help you understand how customer service automation is gaining ground through the use of chatbots.
- Marketing Campaigns
Chatbot campaigns are more efficient and promise higher conversion rates. With 56% clickthrough rates achieved via chatbot campaigns, businesses are leveraging chatbots in innovative ways, such as:
- Growing subscriber lists
- Gathering customer data for market research
- Personalized campaigns
- Targeted campaigns based on hypersegmented audience
A classic example is Absolut Vodka, which chose a chatbot-based promotional campaign.
- Personalized Shopping
Brands like Sephora, Whole Foods, Mastercard, and Spotify are using automated chatbots to offer highly streamlined personalized shopping experiences.
From discussing purchases to presenting options, upselling, and cross-selling via smart recommendations and sharing knowledge resources – they offer versatile services.
Some visuals are shared below to offer you a glimpse:
- Gathering Sales Insights
Automated modern-day chatbots can collect market data and visitor data. They can gather leads, present more qualified leads to your sales reps, and offer guided transition of prospects into customers.
Coming into action right when the visitors land on your web pages, the chatbots can collect their contact and contextual information for your sales reps and present them with more credible data.
- Robust and Instant Support
ML and AI-powered chatbots can deliver more robust and instant support via:
- Canned responses
- Knowledge base
- Self-service modules and resources
- Self-learning and adaptive learning capabilities
Hence, the support reps can focus on more productive work and always have contextual or historical data at their disposal.
Further, for SMEs, hiring a big staff is a financial constraint, complementing a small team with smart chatbots offers a more efficient way to deliver support.
- Sentiment Analysis
Today, brands are leveraging chatbot services to analyze sentiment on comments, feedback, and social media mentions from customers. While humans cannot monitor, track and process all the content (likes, shares, comments, feedback, and chats) from your customers, chatbots can!
They are now processing text to identify the underlying emotions, and performing VoC analysis to deliver impeccable customer support during real-time interactions.
For example, take a look at the following visual that shows how chatbots perform sentiment analysis:
Chatbots: T Minus Zero and the Road Ahead
While chatbot technology is maturing at a steady pace and is serving us with many smart capabilities, we have three areas to focus on:
- Detecting emotions in text, voice, and generic interaction or social media mention
- Identifying the right voice in relation to irony and sarcasm
- Learning new languages to make regional customer support as efficient as in official languages
To match the industry requirements, the technology stakeholders and developers have to focus on the three Is – Interaction, Intelligence, and Integration. Further, chatbot maturity has to keep the granular and fine-grained capabilities in the center to keep the pace of development at par with evolving business sectors.
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