Taking Conversational AI to the Next Level With Unstructured Data

The contact center is undergoing a major transformation. Traditional customer service models simply aren’t working for many customers. Customers are using chat, voice, and digital channels to interact with businesses in new ways. But current AI chatbots are too static, and lack the ability to learn and adapt on the fly. The question is... Why?

The need for a more human-like experience with customer service is critical. Customers want conversations that are more personal, and they want businesses to understand their problems before calling or visiting in person.

To achieve these goals, businesses will need data technologies that can serve as the foundation for building human-like capabilities into their customer service methods. It’s not enough to have contact center data — you’ll also need access to external conversations that your prospects and customers are having around you, captured in their own words. A data engine that can combine these multiple sources and facets of human interactions is a first step to building next-gen customer experiences we all aspire to deliver.

Let’s explore conversational AI, its impact on customer engagement and its challenges. Then we’ll provide a view of the future of conversational AI with a more human approach to customer service for contact centers.       

What are some examples of conversational AI?

Conversational AI is all around us. Consider Amazon’s Alexa. Amazon employs a team of researchers and developers to build one of the world’s best personal assistants. You can ask Alexa anything you want, from how many stars are in the galaxy to what the weather will be like over Memorial Day weekend.

Another example is Facebook Messenger. Messenger is programmed to understand questions, provide answers, and execute tasks — whether it’s scheduling a hangout or finding restaurants nearby. Apple's Siri, Google's Assistant, and Microsoft's Cortana are also examples of conversational AI.

What is the Difference Between a Chatbot and Conversational AI?

A chatbot is a computer program that offers responses to users in text or speech. Non-AI chatbots simply listen for keywords and phrases from its users. Once it identifies those keywords, it will offer some type of relevant response.

Conversational AI (CAI) is a broader category of technologies that use natural language processing and machine learning to understand user intent and provide responses — but not via simple if-then statements. Instead, users can interact with CAIs dynamically, as if they were talking to a person. In this sense, CAIs can power chatbots to be more human-like. 

Consider these examples: A business could create an AI bot that serves as the customer service representative for new users on their platform. Or they could leverage an image recognition tool to identify issues with their products based on images captured by customers during product usage. In both cases, the businesses are leveraging technology to improve customer satisfaction through AI.

Why is conversational AI challenging?

Building conversational AI involves a lot of steps, including:

• Preparing dependencies in programming languages like Python
• Importing classes
• Training models on large, diverse datasets
• Designing conversational flows
• Integrating the models into a chat interface
• Deploying the models in a scalable way

As you can see, there’s a lot to take on — and the steps above only include the high-level technical aspects. For businesses that aren’t experts in programming or data science and don’t have teams of developers at their disposal, building this technology might seem like an impossible task. On top of all that, integrating it into your chat interface could mean having to write integration code for every new page or bot they want to deploy.

For businesses that lack the resources to build their own CAI systems from scratch, it’s often easier and more cost-effective simply to use an AI platform as a foundation. These platforms offer pre-built models for common tasks such as sentiment analysis and language processing, so you can focus on other parts of your conversational experience instead of building these components yourself. By taking advantage of these platforms, you can launch your chatbot much more quickly without risking any gaps in functionality.

Conversational AI for Contact Centers  

The contact center is fast becoming a vital source of business insights. As more and more people are turning to chat messaging and voice calls, businesses are moving to AI-powered contact centers to address growing demand and increased volume of data. 

Conversational AI offers contact centers many benefits. They can serve more customers by allowing them to self-solve basic issues - like bill payments, order status, and account inquiries - freeing up real agents to focus on higher-value interactions. AI-powered contact centers can also reduce the time to execute on call to actions. For example, AI chatbots can speed up the sign-up or purchase process for campaign-related activities, thereby directly impacting business performance.   

Equally important, conversational AI helps monitor and enhance agent performance. By capturing and analyzing call scripts, the AI can extract insights around agent sentiment, keywords, and customer issues to provide opportunities for enhanced agent coaching and training. All of this feeds into better customer service and experience. 

Transforming Contact Center Conversational AI with Unstructured Public Data 

With AI-powered contact centers, businesses can amass more data and insight into each customer interaction. Companies gain visibility into why people call, how they need help, and what types of responses work best. This becomes templated to help inform and structure future interactions.

However, customer visibility only goes as far as the data it is built upon. Currently, contact center data relies mainly on call scripts, or internal private data. This reliance can be effective for businesses that operate in a vacuum, or have products, services or customers that do not change. But for businesses in dynamic conditions, where customer needs are ever changing, where competitive pressures are constant, and where customers are using and turning to outside channels to express opinions, offer feedback, and share experiences, internal data now starts to show a limited, one-sided view of the customer. 

As contact centers become the first line of engagement for innovative businesses, contact centers need solutions that tap into a holistic view of the customer journey, make sense of the data, generate actionable insights, and transform insights into business value.  

The best way to do this is with a massive dataset of customer interactions - across formats and sources - paired with advanced machine learning models that can identify patterns in the data and extract insights. This can give businesses the ability to connect with customers on a deeper level, create new products or services, or even automate processes entirely.

Commerce.AI's data engine is designed from the ground up with this approach in mind. It comes with data integrations to consolidate and unify data across sources, and uses pre-built machine learning models for sentiment analysis, trend identification, issue spotting, landscape analysis, merchandising, inventory management, and more. These insights can lead to deeper, more impactful interactions and positively impact company performance. 

Let’s explore the different use cases and impact of using a human-centered approach to conversational AI. 

Using Public Conversational AI to Extract Customer Sentiment

A key part of any AI-powered contact center is the ability to extract customer sentiment — and Commerce.AI has built AI models for that.

These models allow you to quickly and accurately identify overall sentiment, as well as identify positive and negative language across sources, including chat transcripts, product reviews, forum comments and other sources. This allows businesses to respond with personalized messages based on customers’ actual sentiment. 

You can also use these models to segment customers into different buckets based on their sentiment toward your brand or product by channel, allowing you to target marketing campaigns more effectively.

Customer sentiment is the foundation for many other AI capabilities. For instance, you can use sentiment to understand what products customers are interested in — and how to prioritize your merchandising, inventory or product development life cycles accordingly. 

Similarly, customer sentiment is central to the customer experience, and therefore your bottom line. Optimizing the customer experience and delivering on your brand promise is critical for retaining customers — and we’ve built models to help you do just that.

Using Public Conversational AI to Extract Competitive Mentions

Another key feature of a successful AI-powered contact center is the ability to identify competitive mentions.

Many product and service markets are highly competitive, so being able to know when your customers are talking about your competitors is critical. By extracting competitive mentions across channels - for example, from chat transcripts, online forums, and third party marketplace reviews - you’ll be able to quickly respond to customers who mention a competitor and find opportunities to drive brand retention and customer loyalty. This sort of AI-driven engagement is crucial to improving the customer experience, cementing your brand in the minds of customers, and ultimately driving revenue.

Building a competitive advantage takes more than knowing what your competitors are up to. You need a better understanding of what your customers want — and the data engine behind Commerce.AI can help you do just that.

Using Public Conversational AI to Gain Competitive Advantage

Businesses often want to gain a competitive advantage over other brands within their industry. They may do this by talking about topics that help differentiate their offerings from those of their competitors — but that could be risky if they end up losing business altogether. 

An AI-powered contact center gives businesses the tools they need to create trust between themselves and customers, while also gaining insight into what customers think about their competitors. In doing so, businesses can build trust with consumers and differentiate themselves from other brands in the market. This helps make sure that consumers always choose the brand they prefer over one of its competitors.

Generating Ideas and Content Based on Customer Needs

One of the most exciting aspects of AI-powered contact centers is how they can help businesses create new products and features based on customer needs.

For example, suppose you're a consumer electronics firm, and our product data engine highlights that many consumers are complaining about battery life on one of your product lines. You could use this data to create a new battery-saver feature, or even iterate the next product line to address the issue directly. And later create new marketing content to highlight this feature.

This sort of AI-driven innovation can have a huge impact on business growth and profitability. And it’s not just about solving problems — it’s also about moving the needle on customer satisfaction by delivering new features and benefits based on what customers want. 

Commerce.AI’s data platform gives you the ability to tie together contact center and public data in a centralized fashion, so you can easily access it from any application or system within your business. It also gives you the ability to transform all this data into actionable insights that help guide your business decisions as well as fuel growth opportunities for your business. 

Using Pre-Purchase and Post-Purchase Data

The traditional definition of CX, or Customer Experience, focuses solely on the post-purchase phase of the customer journey. However, there is plenty of important data during the pre-purchase phase that can be used by contact centers to deliver exceptional conversational experience.

For example, Commerce.AI’s data platform can help businesses gain insights into how their customers are using products in the market more broadly. By leveraging public conversational data, contact centers can now share use cases, offer solutions, and recommend products to customers during the purchase process, ultimately supporting customer acquisition by converting shoppers into buyers and driving business performance. 

During the purchase process, businesses can also share how customers are using their products after purchase. For example, contact centers can learn about features or attributes associated with repeat purchases and loyalty and share these points with shoppers. Alternatively, Insights into areas for improvement can also help contact centers better support customer decision making — ultimately impacting customer satisfaction and future purchases.

Public conversational data offers a wealth of information that is not available through traditional contact center data and access. By combining public conversational AI with internal contact center AI, businesses can gain a massive competitive advantage — and truly transform their customer experience.

The Future of Conversational AI

In one word, the future of Conversational AI is automation.

Consumers have become comfortable with the idea of conversing with a machine as they would a friend, and AI-powered contact centers are helping businesses welcome this new era. And once businesses begin automating customer interactions, the possibilities for innovation are truly endless. 

Businesses can now create experiences that go far beyond responding to requests for help. With a deeper understanding of the customer journey, businesses can proactively provide insights and solutions along the way, guiding customers through their problems so they never have to think about them again. And because these experiences are powered by AI rather than human agents, businesses can scale these capabilities across multiple channels at once, rather than having to wait for each channel to fulfill its own promises, and free up human agents to focus on more complex and high value customer interactions.  

As more and more customer agent tasks are automated, we will see a shift to AI-powered contact centers — and this will require businesses to rethink how they interact with customers. 

Businesses that embrace the future of AI-powered contact centers will set themselves apart from their competitors and gain a competitive advantage — just as companies like Amazon and Google have done in the eCommerce and advertising spaces, respectively.

Beyond automating simple Q&A tasks, empowering automation with data can go far beyond just answering questions. For example, businesses can use AI to help customers understand the differences between products so they can make better decisions when making purchases. By using both pre-purchase and post-purchase data, businesses can not only gain a competitive advantage, but also foster a deeper level of customer loyalty.

The combination of public data and contact center data is the future — and it’s already here. The time to start thinking about how you can leverage this powerful combination is now.

Takeaways

The days of the traditional contact center are numbered. Businesses that embrace the future of AI-powered and even automated contact centers will gain a massive competitive advantage.

With Commerce.AI’s data engine, you can gain deeper insights into your data, enabling the next level of Conversational AI.

For example, you can leverage your customer sentiment data to create targeted campaigns based on customer needs. You can also use this sentiment data to identify patterns in what customers like and dislike — and proactively reach out to help solve these problems in real time, without requiring action from an agent every time. Further, you could analyze competitive mentions to know exactly where you need to improve or focus your efforts. 

Commerce.AI’s data engine provides the power needed to start transforming your business with AI.