AI chatbots to drive conversational commerce

Conversational AI chatbot technology is a subset of artificial intelligence that works to simulate human conversation between chatbots and people.  

This technology is reliant on using machine learning and Natural Language Processing (NLP) to both understand human speech and craft appropriate responses. 

These intelligent chatbots are designed to create natural conversational interactions for a variety of purposes, from customer service to marketing and sales.

But not all chatbots are alike. Conversational AI chatbots differ from simpler keyword chatbots by end-user interaction as well as the complexity of design. 

Specifically, keyword or rule-based chatbots are built to provide pre-programmed replies to specific customer inputs. More inputs can be programmed in or taken away as needed, but the basic function of a keyword chatbot is to provide a specific reply to a specific customer input. 

Conversational AI chatbots, however, are designed to have natural conversations with customers – as the name implies.

According to Gartner, by 2027 chatbots will be the primary customer service channel for around 25% of organizations. 

Already, respondents to a Gartner survey said that 54% of respondents were already using some form of chatbot, VCA or other conversational AI platform for customer-facing applications. 

Consumers expect to interact with chatbots and will willingly engage in conversations with them if they help them achieve their customer goals. 

Businesses that don’t invest in this segment of customer service can expect to be left behind those making strides in conversational commerce. 

Behind the scenes, conversational AI chatbots use machine learning algorithms to further enhance their performance over time.  

By interacting with more customers and receiving more feedback, chatbots learn to recognize patterns in customer behavior to improve the accuracy and relevance of their responses. 

Over time, this has helped conversational chatbots evolve from answering frequently asked questions to providing end-to-end assistance at every point across the customer journey. 

In 2022, the world was introduced to ChatGPT from OpenAI.

The GPT-3 model took the world by storm, and in 2023 we were introduced to the even more advanced GPT-4 model. 

Applications of GPT-4 went far beyond having natural conversations. For example, in March 2023, Microsoft confirmed that the latest version of their search engine Bing was running on GPT-4. 

This shows that conversational AI chatbot technology has evolved beyond recognizing user intents to provide business replies, and moved into solving highly complex user prompts.  

Like the name suggests, conversational commerce chatbots are automated chat programs that use NLP and AI to communicate with customers in a conversational manner. 

These chatbots can be integrated into messaging platforms and websites, and are designed to provide customers with end-to-end personalized shopping experiences. 

Conversational commerce chatbots are designed with the intent of assisting customers with a variety of tasks ranging from answering product questions, providing recommendations, and processing orders. 

This also includes handling customer service queries, like order tracking and providing information on returns. 

A key advantage of this specific type of conversational chatbot is the ability to provide a conversational experience, as opposed to a keyword menu.  

This improves customer experience and boosts customer engagement. And since there is no human interaction required, they can support customers 24/7 and at scale by handling virtually endless concurrent interactions.

Developing a conversational AI chatbot differs from the design of a rule based or keyword chatbot.

A conversational chatbot needs to understand a customer’s intent. Designing this experience is based on extensive customer research. 

Designing a conversational chatbot requires a multidisciplinary approach.

This is because the design of a successful chatbot requires deep understanding of both the technology, as well as conversational copywriting and sales messaging. 

This is a laborious process that can require teaching a conversational chatbot a wide variety of phrases per customer intent. To make this manageable for enterprises, there are expert services available from most chatbot providers for this. 

Like people, conversational AI chatbots also need to grow, learn, and evolve. When developing a conversational AI chatbot, you need to start small. Commonly, from a chatbot that answers simple FAQs, that progresses over time into a conversational chatbot. 

This helps the conversational chatbot to better understand human psychology and create more genuine connections with customers. 

To design an effective conversational commerce chatbot, there are several key factors to consider:

This approach, coupled with NLP and machine learning, helps the conversational chatbot to better understand human psychology and create more genuine connections with customers. 

In practice, choosing the optimal channel for conversational commerce chatbots relies on several variables. Businesses need to evaluate which is best for engagement taking into consideration the following:

After considering these points, businesses need to look at channel-specific elements and capabilities to drive the most traffic.  

These channels will more than likely include: 

Integrating a conversational AI chatbot into every one of these channels may be a tall order. It’s best to analyze interactions with customers and see which channels give you the highest engagement and go with the top channels. 

Conversational AI chatbots offer a wide range of benefits for businesses, including:

Chatbots can provide instant and personalized replies to customer service queries. This allows businesses to provide 24/7 customer service, meaning outside their regular business hours.  

In addition, chatbots can also handle large volumes of concurrent customer queries. This cuts down on waiting times and improves time to resolution KPIs, positively impacting customer satisfaction.

Chatbots provide seamless and highly-engaging, end-to-end customer experiences with personalized interactions that feel like a natural conversation.  

This helps businesses to build stronger relationships with customers, improving brand loyalty and advocacy. 

Business can reduce costs by automating repetitive tasks by using chatbots to handle frequently asked questions or processing orders.  

This frees up agents to focus on more complex tasks requiring a human touch. 

In addition, chatbots run 24/7, eliminating the need to hire, train, and equip additional staff outside regular business hours.

Most conversational chatbot engines are natively multilingual. This lets them detect, interpret, and use conversationally just about any language.  

This can give your business access to a global audience that may have previously been inaccessible. 

Finally, and most importantly for conversational commerce:

Chatbots can help businesses increase sales by providing personalized recommendations and detailed product information to customers.  

Analyzing customer data allows chatbots to suggest products that customers are likely to be interested in, or provide detailed information based on what the customer says they’re looking for. 

And since chatbots can work around the clock guiding customers through end-to-end transactions, you can sell outside regular business hours, boosting your sales revenue.

In addition to the benefits of conversational AI in general, chatbots offer several specific advantages for businesses.

When it comes to implementing conversational AI chatbots, businesses need to consider several factors to ensure their chatbot strategy aligns with their overall business goals. 

Here are the key factors to consider:

Throughout their evolution, AI chatbots have played a few roles of ever increasingly complexity. 

One of the first broader applications of AI chatbots in commerce was to handle frequently asked questions and high-volume queries.  

This is still a valuable application, since it improves customer satisfaction by reducing wait times and accelerating time to first resolution. Simultaneously, this helps optimize customer service by letting human agents focus on lower volume, higher value customer service requests. 

A newer application for conversational AI chatbots in commerce is the shopping assistant. 

I recall when I bought my first car and needed to buy new winter tires. Tires were an enigma, and I didn’t know the first thing about them. But after a few days of researching everything I could, I ended up making a purchase… and bought the wrong size for my car for way too much money.  

Conversational AI chatbots can take away the mystery when it comes to shopping by providing highly personalized recommendations based on customer profiles, behaviors – or just a simple conversation. 

Not only do conversational AI chatbots recommend products, but conversational commerce tech can integrate payments into chat apps to complete transactions. This allows for entire customer journeys to take place within a single conversation. 

There are other exciting examples of how conversational AI chatbot are driving commerce.

Some everyday examples of conversational AI chatbots are virtual assistants like Alexa, Siri, Google Assistant, Cortana, etc. Users engage these assistants using natural language and intents to receive personalized responses and even resolve more complex queries. 

ChatGPT is another example of a conversational AI, and businesses are looking to implement it into their processes to provide fast and intelligent responses to customer queries. 

In addition to improving CSAT, conversational chatbots helped luxury eyewear retailer Salmoiraghi & Viganò boost sales for a limited-edition eyewear release. 

And it’s not just in retail. The life insurance department at Covea Group had a huge 11% conversion rate for the industry off the back of an end-to-end omnichannel conversational marketing campaign. 

Brands can also engage create personalized direct, branded engagement with customers using chatbots in their brand’s (or brand spokesperson’s) voice. This boosts customer engagement through the roof.

The benefits of conversational AI chatbots are applicable across several industries. These include:

The most common retail use cases for conversational AI chatbots is in customer service. Conversational chatbots can handle customer service enquiries of varying complexity, such as delivery enquiries. 

As conversational AI chatbots evolve, their role in retail changes from a customer service one into an advanced shopping assistant.  

Not only that, conversational AI chatbots are used at multiple points throughout a customer’s shopping journey to help facilitate purchases within the same platform. 

Customers are now used to interacting with conversational chatbots to provide product recommendations. 

AI chatbots can quickly fetch and analyze customer data across all touchpoints to provide personalized recommendations and complimentary products based on customer history, behavior, and searches.

Just like other verticals, conversational AI chatbots used in the finance industry help provide faster customer service, speeding up time to resolution. 

In addition, conversational chatbots help customers gain quicker access to customer files and collect their stored data – or even help clients find the branch or ATM nearest to them. 

The most important use case for conversational chatbots in the finance industry deals with supporting personal financial management and providing advice. AI chatbots can quickly access and fully analyze a customer’s finances to provide personalized financial advice.

The most common use cases for conversational chatbots in the transport and logistics industry start with customer service. Specifically, customer requests regarding any delivery issues. 

Another use case is booking transportation or requesting a delivery service. The cases are similar in that the chatbot can gather relevant customer data, communicate with internal systems, provide availability information in real-time, and confirm orders. AI can also authenticate relevant information on the spot – specifically, pickup and delivery locations, times, pricing, etc. 

Real-time shipping tracking is another exciting use case that keeps customers up-to-date – without the need to enter long tracking codes. Instead, customers can simply message an AI chatbot for instant updates.

Dealing with patients in the healthcare industry is an intensive task at the best of times. Medical staff are expected to schedule and reschedule appointments, provide over-the-phone diagnoses, and all other manner of tasks in addition to performing their respective roles in the industry. 

Conversational AI chatbots alleviate a lot of this pressure from med teams so they can focus on providing top medical care. 

One of the most practical use cases for AI conversational chatbots in the healthcare industry is appointment scheduling. Implementing an AI chatbot assistant streamlines what used to be a very time-consuming process full of frustration for both medical staff and patients. Thanks to AI chatbots, patients can view available times and quickly schedule the appointment that best suits their needs. 

AI chatbots can also provide instant patient assistance at scale. Similar to finance industry chatbots, AI chatbots in healthcare can quickly access and analyze patient data to provide personalized care and support. 

Woebot, developed by a team of Stanford researchers, is a conversational AI therapy chatbot and a great example of the future has in store for healthcare chatbots. 

Conversational AI chatbots are changing how businesses and people communicate. The technology is eliminating barriers in terms of language, time, and volume. 

Interactions are occurring at a faster pace with more detailed information than ever, creating a positive impact on CX at a scale with no historic precedent. 

Fueled by machine learning and NLP, conversational AI chatbots are on course to outpace traditional development in transforming customer interactions and enhancing business operations. 

As these technologies continue to evolve, we can expect to see even greater advances in conversational AI. This will lead to even more personalized, efficient, and satisfying interactions between businesses and people – thanks to technology. 

These are exciting times and the future looks bright for businesses that have or are preparing to embrace this transformative technology.