It’s been a year since Collins Dictionary tapped “AI” as the word of the year, but it’s only starting to influence how we do business.
As the experts struggle with what it means for white-collar workers, investors, and regulators, it’s not going away. One earlier report, for example, suggests AI “will have a cumulative global economic impact of $19.9 trillion through 2030 and drive 3.5% of global GDP in 2030.”
It’s also vital to power the next generation of AI chatbot technology — especially in the social media sphere, where most consumers interact with brands today.
The Secret Sauce? Automation
At their most basic, automated chatbots are like virtual assistants. Brands use them to literally “chat” with users, whether existing customers, prospects, or an aimless browser. Engineers design them (and brands depend on them) to streamline customer communication, paving the way for a seamless experience that benefits everyone.
The most basic chatbots operate on two key components: scripts and AI. Scripts are (obviously) drafted responses to the most expected questions and common scenarios. They make up the blueprint for chatbot functionality.
And while those scripts remain the foundation of chatbot operations, AI allows them to grow beyond that. AI empowers chatbots to move beyond simple keyword recognition and regurgitation. They evaluate what a user says (or types), intuit the intent behind it, and respond accordingly — in the most natural way possible.
In short, the user types in question. The chatbot scans its scripts. If it finds a response, it uses it. If not, it relies on AI to draft a new one. The chatbot learns from every one of these interactions, getting better at handling tricky or nuanced conversations.
This chatbot automation gets answers to consumers quickly and allows brands to manage customer service efficiently. The numbers are tough to dispute:
- MindValley, an edtech firm, saw a 522% jump in Masterclass sign-ups from Instagram while slashing their support response time by 99% by leveraging Manychat’s Instagram Automation.
- OlimpiaHome, an Italian e-commerce site, saw a 30% lead jump.
- And Candace Juné, founder of Epic Fab Girl, rolled out Manychat’s Instagram Automation and witnessed a “118% increase in leads compared to landing page opt-in campaigns from June 2021 to September 2021, and now saves 15 hours per month answering Instagram DMs.”
How It Works
The lightning speed and Galapagos-level adaptability of AI make chatbots incredibly effective. AI relies on multiple tools to make that happen.
Contextual Awareness
For starters, AI-powered chatbots can recall every past interaction from a conversation, which allows for more personalized and relevant responses tailored to a specific user. In short, AI uses contextual awareness to “connect the dots” between what the user says — and doesn’t — to respond more like a human.
Knowledge Base Integration
AI works with an exhaustive amount of data — a digital library, if you will — to access and process input and respond seamlessly. It processes the query and taps the library for the best reply. The AI processes the user’s questions and determines which part of the knowledge base might contain relevant answers, leveraging context and keywords. Once it settles on the most appropriate information, the AI reworks it into the most natural response. If the chatbot determines that a particular answer isn’t helping users, it can flag it to improve future answers.
Machine Learning
Despite how technical it might sound, machine learning is simply how a chatbot learns from examples rather than specific instructions. For instance, by poring over multiple cat and dog pictures, the chatbot studies the patterns to determine how to tell them apart. ML algorithms enable chatbots to learn from every interaction, improving their ability to understand user needs and provide relevant information.
Natural Language Processing (NLP)
NLP combines language rules and patterns (such as grammar) to parse how humans communicate. AI-powered NLP empowers chatbots to better decrypt the intricacies of human language from the context, perceived intent, and even the latest slang to generate more accurate — and relevant — responses to user queries.
Sentiment Analysis
AI chatbots rely on sentiment analysis to interpret the emotions (or tone) behind a user’s message. The sentiment informs the chatbot’s ability to customize its responses based on its interpretation of whether the user is happy, frustrated, or neutral. The chatbot examines the user’s words, phrases, and punctuation. It then typically assigns each message a sentiment score (positive, negative, or neutral). The sentiment score allows the chatbot to show empathy or excitement, creating more human-like interactions.
Finally, AI chatbots depend on predictive analytics to organically improve user interactions by anticipating responses and behaviors. For example:
- The chatbot can accurately anticipate a user’s concerns or responses by analyzing previous interactions.
- They can also forecast behaviors based on existing datasets. For example, they can identify which users will likely convert into paying customers or predict when they might stop engaging with a service, allowing for proactive engagement.
- For sales-driven chatbots, predictive analytics can score leads based on their likelihood of converting into paying customers. The chatbot can adjust its responses depending on the lead’s position in the sales funnel.
In short, predictive analytics allows AI chatbots to anticipate the needs and preferences of each user, creating more efficient and lifelike interactions.
Future Trends Brands Must Prepare For
Consumers have been pursuing more direct interactions with brands for years. As a result, brands have increasingly relied on chatbots to make themselves available around the clock. Gartner Inc. predicts that “80% of customer service and support organizations will be applying generative AI technology in some form to improve agent productivity and customer experience (CX).”
“The impact of AI on the customer service function cannot be overstated,” Drew Kraus, VP Analyst in the Gartner Customer Service and Support practice. “Not only do we expect organizations to replace 20-30% of their agents with generative AI, but also anticipate it creating new jobs to implement such capabilities.”
Chatbot technology is also veering toward more human-like interactions, driven by advancements in NLP and sentiment analysis. With improvements, chatbots can better understand user intent and the underlying tone.
“Digital customer service will transform customer experience outcomes by reducing friction and eliminating unnecessary customer effort,” said Kraus. “By creating a seamless customer experience, this technology will reduce churn and enhance customer satisfaction.”
But challenges persist. Chatbots still sometimes misinterpret a customer’s question or struggle with certain accents or language quirks. Even so, engineers are working overtime to circumvent these hurdles by integrating AI to improve capabilities.
With so many of us growing accustomed to the constant company of voice-activated assistants — whether it’s Alexa, Bixby, or Siri — consumers have come to rely more heavily on voice bots. As a result, voice bots stand poised to transform customer service, particularly in sectors like banking, where virtual assistants can provide real-time information and process payments directly through platforms like Facebook Messenger.
The persistent popularity of social media platforms is also driving chatbot adoption. And they’ll continue to do so as brands leverage messaging apps to engage with customers. With more than 100,000 bots active on Facebook Messenger alone, brands recognize the largely untapped potential of chatbots to drive marketing efforts, provide immediate support, and — most importantly — power sales. By helping brands automate payments, chatbots are making the transaction process quicker and more convenient.
Chatbots streamline workflows internally, helping companies automate tasks from HR processes to IT support, improving efficiency across departments, and easing employee workload.
As chatbot technology keeps evolving, it will become an increasingly essential part of our daily lives, with applications in multiple sectors, such as fitness, e-learning, and healthcare.
Settling on the Best Chatbot Platform
When settling on the best chatbot platform, brands must consider two critical factors: User experience and integration capabilities. Considering them, brands need to answer several questions.
User Experience (UX)
Here’s what you should consider when it comes to UX:
- How easy is it to use? The ideal platform will feature an intuitive interface, making it easy for developers — and less technical users — to set up and manage the chatbot.
- How customizable is it? The best chatbot will be highly customizable. Brands should be able to adjust the chatbot’s appearance, personality, and tone. That approach ensures it aligns with the brand voice, providing a seamless (and natural) user experience.
- What are its NLP capabilities? The most successful platform should also support advanced NLP, allowing the chatbot to understand and respond to user queries more efficiently and provide lifelike interactions.
- What about its response speed? A top-performing platform must respond quickly. Few things are more critical than fast response times in meeting or exceeding user expectations.
- Does it offer multi-channel support? Finally, the best-performing platforms will allow brands to deploy chatbots across multiple channels, from websites to social media to mobile apps. And it must do so without threatening the integrity of the user experience.
Integration
Here’s what you should consider when it comes to ease of integration:
- Does it work with existing CRM and customer support tools? Whichever platform a brand settles on must be able to integrate seamlessly with the systems already in place. Tech stacks usually include a CRM tool (such as Salesforce) and customer support platforms (like Zendesk), allowing the chatbot to instantly find and update customer info.
- What about third-party APIs? To ensure seamless operations, brands must look for a platform capable of integrating with various third-party applications for better service.
- Does it feature omnichannel integration? The chatbot should also be able to operate across disparate messaging platforms — whether it’s Facebook Messenger or WhatsApp — all while maintaining consistent customer interactions.
- What kind of data analytics and reporting can it handle? The platform should also use analytics tools that track user interactions, identify consumer trends, and improve chatbot performance.
Other considerations
While the previous are the most crucial components of a robust chatbot platform, brands must also consider the following:
- Cost (and ROI). This is always a primary consideration — especially for the C-suite. Brands must assess the start-up costs against the expected ROI, including evaluating the different pricing models, maintenance costs (if any), and downtime caused by updates (again, if any).
- Compliance. It might seem obvious, but brands should consider whether a vendor complies with relevant privacy laws, such as GDPR. Robust security protocols are also a must to keep customer data safe.
- Maintenance and support. Remember, this also includes communication after the sale. Brands need a vendor to continue providing responsive customer service, support, and training throughout the relationship.
- Scalability. Can the vendor keep up with your projected growth? And can it stay ahead of increased customer use?
- Vendor reputation. This point is as critical as the UX or price. When deciding on a chatbot platform, it’s essential to check their background and reputation in the industry. Things worth looking at include client testimonials (or case studies). They paint a clear picture of how reliable and effective their solutions are.
Manychat Solutions
Manychat offers solutions for brands and content creators alike. Find solutions tailored for specific channels — such as Facebook Messenger, Instagram, and WhatsApp — where users can easily pull in high-quality leads, convert them into sales, and stay on top of customer conversations. It’s more cost-efficient than a virtual assistant and much more effective.
We also help brands and influencers send automated product and service links and updates. Our customers can engage and quickly follow up with their customers and prospects without redirecting them. Content creators we’ve worked with have enjoyed boosts ranging between 50 to more than 800 comments per post.
Chatbot automation is upending how brands talk to their consumers in the ever-evolving social media landscape. By integrating advanced AI capabilities — such as natural language processing, sentiment analysis, and predictive analytics — automated chatbots make seamless, personalized interactions possible.
As brands prepare for an AI-powered future, landing on the right chatbot platform is critical. With platforms like Manychat leading the way, chatbots aren’t a luxury anymore. They’re a must for standing out in a growing crowd of brands and influencers.
