Conversational AI, or the use of natural language processing (NLP) in messaging apps, digital assistants, and chatbots to create personalized user experiences, is a transformative technology that is changing the way businesses communicate internally and with their customers. The first ‘chatbot’, ELIZA, was created in the 1960s by Joseph Weizenbaum. ELIZA utilized a script to act as a psychotherapist. Before long, people thought that chatbots would be just as capable as human speakers and that machines would soon replace human operators. However, “Weizenbaum rejected the notion that machines could replace human intellect – that computers’ understanding of language was entirely dependent on the context.”
Still today, digital assistants and chatbots alike struggle to apply context when interpreting a user’s written or spoken word. Conversational solutions have made some major strides in recent years. For example, Amazon’s Alexa has been programmed to recognize a toddler’s pronunciation: “Alessa” and “Lexa.” The use of NLP in sentiment analysis by marketing teams is another example of how far AI solutions have come since ELIZA.
Although the use of NLP by artificial solutions like chatbots and virtual agents is, to some degree, limited, the application of these tools far outweighs their limitations. More than anything else, AI solutions need the information to perform an assigned task most effectively. The more data provided to an AI assistant, the more context it can provide for itself. If there’s one thing businesses are never lacking in, it’s data. Putting an AI assistant to use in these data banks is changing the way employees acquire and use data analytics, how customers are served, and the way marketing is performed.
Cutting Through The Monotony With Conversational Tools
Sifting through vast data banks, such as ledgers and inventory records, is where artificial solutions truly shine in the enterprise. With a cloud based ERP system, employees have the ability to make complex voice queries to a digital assistant-employing AI to analyze financials for anomalies and trends in order to make business decisions in a matter of minutes. As if a member of the team, the conversational AI responds with insights that would take human operators hours of data analysis to conclude. The continuous “virtual close” of the books allows for complicated financial analysis and helps business operators make financial decisions with confidence.
With the increase in flexible working hours and remote employment, it’s more important than ever to be connected to work from any location. Employees can also use the digital assistant to submit expense reports and procurement fulfillment from anywhere, at any time. Businesses can be sure the use of conversational AI solutions in the ERP cloud is always secure, no matter where the employee is – bringing peace of mind to businesses who share sensitive information over the cloud.
Conversational AI allows employees in the onboarding and training phase to easily make queries, which reduces training time, increases employee knowledge, and helps new hires quickly adapt to the use of your business tools and ERP system. This reduces training gaps as new hires may not need to ask another employee repetitive questions if they don’t fully grasp the concept. By providing a conversational solution, new employees will be more likely to make that query, bringing them a more complete understanding of the process and increasing the business’ return on investment.
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Digesting The Data With Conversational Analytics
Have you ever wondered what happens when you ask Alexa to order laundry soap? Mysteriously, the item ordered just seems to show up! Unfortunately, it only looks that simple from the customer side of things. When a customer makes an inquiry like, “Hey Alexa, order dog food,” an immense amount of data processing goes into that query. Businesses pull in data from many sources and store it in various systems. When a query is made, AI sifts through that data to compile it into a digestible form.
With conversational analytics, enterprises are able to find customer information like your name, credit card information, address, etc., in order to streamline the order process, bundle it all up, and ship off your dog food. If the system is missing necessary information from your profile, the conversational AI can easily ask you for that information, too. Utilizing conversational analytics has made the eCommerce process very fast, hence two-day shipping. The speed and accuracy at which businesses can operate with these tools would require an army of analysts and be nearly impossible with only human operators.
Conversational AI, NLP, and a Shift in Marketing Data
Conversational AI and natural language processing (NLP) go hand in hand. Language is filled with nuance, sentiment, and context – making NLP crucial to the function of conversational AI. Artificial solutions are not only making formative changes to internal business operations and customer interactions, but to business marketing as well. By employing NLP, businesses have the ability to scour online mentions of their brand and gain insight into how people feel about their brand, not just what they’re saying. This allows businesses to mitigate negative views and for brands to seek out potential buyers more effectively. By searching through online mentions, businesses can predict where customers are in the sales funnel and engage personalized marketing to specific buyers with a high potential of purchasing their product.
The Potential to Increase Your Marketing Scope
The use of NLP in marketing is improving its ability to process nuance, sentiment, and context when digesting language. For decades, this has been the major downfall of NLP. This shift could open the door for many more uses of NLP, such as collecting data from customer’s use of conversational AI for queries about products. If customers are using conversational tools to find more information about products and these conversations are stored in businesses data banks, that data could theoretically then be scoured by an AI assistant – in the same way as online mentions. The possibility to obtain marketing insights from consumer conversations with chatbots, regardless of slang, nuance, or context, has a great deal of unrealized potential.
A More Genuine Understanding
Marketers are more likely to find genuine information about what customers are asking about in regards to their products in a chatbot conversation than in online mentions. When customers write reviews, their opinions on the product have already been determined. Therefore, bias is already present. If businesses could understand what customers are asking about their products before they even make a purchase, they could make changes to personalized marketing, product descriptions, and even rebranding efforts well before customers buy the product and write reviews.
Connect to a Larger Audience with Conversational AI and Machine Translation
As NLP evolves in its ability to understand nuance, sentiment, and context, its scope of use will evolve along with it. The ability to translate customer queries and online mentions will allow translation in conversational AI to develop, as more data is collected and analysis is performed. Breaking down language barriers and expanding the reach of products to places where language barriers restricted the capabilities of an English speaking business to connect its products to customers in a foreign country.
The ability to translate personalized marketing materials in real-time into different languages provides marketers with a greater reach. The use of machine translation integrated with sentiment analysis will give a great deal of insight for marketing in countries that speak another language, helping to inform marketers where products are needed or wanted.
Although conversational machine translation is far from perfect, the capability is on the horizon. With translation of sentiment, slang, nuance, and context are still difficult for NLP, the ability to have seemingly genuine conversations with real-time translation lags behind in comparison to other advancements in conversational AI. However, the potential for consumers to ask virtual agents questions about products or services in another language is there.
Conversational AI has undergone a lot of changes since Weizenbaum created ELIZA in the 1960s. As the abilities of NLP continue to grow, the changes in conversational solutions are sure to grow as well. One has to wonder if Weizenbaum ever imagined that conversational tools would be this close to understanding the different nuances of language.
Many businesses are already taking advantage of these tools. With automated customer service and FAQ pages utilizing conversational AI, it seems that it won’t be long before more expansive capabilities, like real-time translation, connect businesses to an even larger audience, too.