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3 Ways You Can Use Natural Language Processing Today
Natural language processing (NLP) has not emerged overnight. It has been around for more than 50 years, slowly growing in prominence, and only recently hitting mainstream. As the volume of unstructured data increases and new methods of extracting and analyzing data emerge, NLP is transforming businesses.
NLP is an exciting branch of artificial intelligence (AI) that combines the powers of AI, computational linguistics, and computer science. It enables computers to process, understand, and generate human speech and text. By bridging the gap between people and machines, NLP is changing the ways that businesses understand and analyze data.
Lead Capture and Nurture
Advancements in NLP and conversational AI have fueled the rise of chatbots. While chatbots still have a long way, they are already transforming businesses. AliMe, Alibaba’s chatbot, attends to millions of customer queries a day in Chinese and English. Hundreds of thousands of these queries are highly conversational. For businesses, one of the greatest benefits of chatbots is their ability to instantly respond to and capture leads. Time is of the essence when responding to customer leads. Five minutes is the oft-cited sweet spot; when salespeople wait longer than five minutes to respond to a lead, the odds of qualifying that lead plummet by 10x.
Chatbots are especially powerful in nurturing leads until they are ready to buy. This nurturing process is critical. According to Forrester, companies that excel at lead nurturing generate 50% more sales at a 33% lower cost. As leads are nurtured and chatbots detect a change in a lead’s buying intent, a human sales representative can be notified and a smooth hand-off can be facilitated. It's a win-win scenario for both customers and businesses. Customers are given immediate attention. Salespeople, on the other hand, avoid wasting time nurturing stale leads since chatbots assume responsibility for part of the nurturing.
Searching for more than keywords
NLP tools allow us to make sense of unstructured data at an unprecedented scale. One of the largest repositories of unstructured data in existence today is our digital communication data, including emails, chat messages, and social media messages. When we apply machine learning machine learning and NLP to our communication data, we can distill meaning and gain valuable relationship intelligence about our network.
The human language is very complex. There are an infinite amount of ways to convey the same message. What’s especially powerful about machine learning is that, by definition, it leverages statistical techniques to empower computers and other systems with the ability to learn without data. The more communication data we have access to, the more effective we’re able to parse it and extract meaning.
Affinity leverages NLP to analyze your communication data and understand the strength of relationships. For instance, how many of your LinkedIn followers do you personally know? How many have you talked to in the last 6 months? Affinity looks at not only cadence of communication but also analyzes the type of conversation and applying a score so others can better understand if you would be a solid source for an introduction. ”
With the masses of emails we receive each day, it can be very difficult to sort through the noise. With NLP, businesses are able to instantly classify an email, without needing to reach it. NLP technology has the capability to scan emails and determine the underlying intent, tone, and degree of urgency. For example, emails that contain words such as “tomorrow”, “urgent”, “response”, and “confirm” are likely more pressing and time-sensitive as compared to other requests.
Affinity helps users prioritize emails by proactively identifying emails that require a response. We’re constantly building new features that help you keep on top of your inbox.
NLP is unlocking valuable information for businesses. In effect, it is democratizing business intelligence, making it available to the masses. As we continue to make strides in making NLP more easily accessible and user friendly, the promise and potential becomes more enticing.
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