Data growth is exponential today and there is no point in SMBs being rich in data but poor in information. Companies need to drive as much value as they can from data to understand customers better and meet their expectations.
Receiving insights from data can help in many ways, such as getting customers to remain loyal to the business. Consumer data could become one of the biggest differentiators in the next few years as whoever can unlock it and use it strategically is likely to be more successful.
Growth of unstructured data
Far more unstructured data exists than structured data. Structured data is formatted and organized in a way to make it easily searchable in databases. Unstructured data is much more difficult to process and analyze because it does not have a predefined format.
Unstructured data comes from social media platforms, like Twitter, Facebook, Instagram etc. It also comes from public online data sources, such as government websites. All enterprises also have some form of unstructured data in the form of emails etc.
What if every process, transaction or decision in a business could be optimized at the point of impact without requiring every person to be an analytical expert? This is where natural language processing has major benefits.
What is Natural language processing (NLP)?
Text analytics can be used to analyze unstructured text. It applies natural language processing to extract insights from data. NLP comes under the umbrella of artificial intelligence and is used to translate human language in text and voice from and into commands that computers can process. What comes naturally to humans is difficult for computers due to a lack of formal rules and the absence of context or intent.
Some examples of NLP you may have come across are the use of voice text messaging, spam filters, Siri, Alexa, or Google Assistant and spam filters.
Sentiment analysis for improved customer service
While sentiment analysis can sound daunting, a tool using NLP can scour customer interactions, such as reviews or social media comments to see what’s being said. Analysis of these interactions can help a business to determine how well a marketing campaign is going and much more. It can help with monitoring customer issues that are trending before making a response or offer insights to improve customer service.
If a business is running a marketing campaign, for example, sentiment analysis could give an idea about how people are reacting to it. Every minute nearly 100,000 people are tweeting. There is no way a business could hire enough people to review the tweets.
Using sentiment analysis would enable a business to find out whether people were tweeting about the campaign and the nature of their tweets – positive or negative. Using NLP gives businesses an idea of what people are feeling which is extremely helpful in guiding their decisions.
In a service-oriented business that is subscription-based, for example, it is helpful to be able to predict which customers are at risk of leaving. Reaching out to them before they leave could help to reduce churn and make the difference between them leaving or staying.
Research shows that it is possible to improve customer interaction and reduce costs by using chatbots. Most of us have interacted with a chatbot at some point or another and they are particularly handy on websites where they can make communication with a customer very interactive.
These digital employees can work 24/7 and do not get tired or need a vacation. A Gartner study predicted that by 2020, chatbots will handle about 85% of customer service interactions.
Rule-based and self-learning chatbots
Some chatbots are rule-based and answer questions based on rules they have been trained on. These bots can handle simple queries.
There has been a transition from rule-based bots towards self-learning chatbots. A self-learning retrieval-based bot can select a response from a library of predefined responses to specific questions. It looks at the question, tries to understand what type of question it is and then goes and finds the best answer within a set of answers and gives that back to the user.
A self-learning generative bot is more intelligent than a retrieval-based bot because it can generate answers by taking word by word from the query.
Many of the chatbots being used at present are a combination of all three types.
Some use cases of chatbots
Chatbots are capable of working in many different areas, including marketing, payments and processing. However, they excel in customer service. In the future, we may find chatbots answering questions that we would normally find on a Q&A page and even scheduling appointments.
Insurance companies were some of the earliest adopters of chatbots. They use chatbots to educate customers and update them on the status of their claims.
Bank of America’s chatbot is an example of an advanced chatbot that is capable of handling any customer query. By using predictive analytics, the bot is able to anticipate customers’ needs and guide them through complex banking procedures.
Thanks to voice recognition, smart assistants are able to infer meaning and provide a useful response. The difference between a chatbot and a voice assistant, like Alexa, is that it takes an audio conversation, converts it into text and then analyzes the text. These voice assistants understand contextual clues and can even answer questions about themselves and respond with humor.
A final word
At present, consumers may still feel that chatbots are too “unnatural” and would rather speak to a sales rep than a machine but this is likely to change as chatbots become more sophisticated.
As humans become more dependent on computing systems to communicate and perform tasks, so does AI and machine learning gain more momentum. There are numerous applications of NLP and the list will grow as businesses start to see its value.