Artificial intelligence can do many amazing things, but one thing surely stands out: The ability to speak, listen, and, most importantly, understand human language.
Language is one of the several evolutionary developments that have made us intelligent beings, which means if AI masters it, too, it would be a revolution. This revolution would not the kind of that sci-fi movies predict, with robots taking over humans, but rather the one that allows humans to transform the way they do a lot of things in business.
Thankfully, we’re well on the way to achieve that with chatbots supported by Natural Language Processing (NLP) technology. Simply explained, it’s a technology that enables systems that understand human language and are able to extract meaning from it. Although NLP still has a long way to go to become a disrupting force in business, it’s already being used in a variety of ways.
Let’s describe the benefits of using the technology as well as some of the most important barriers that need to be overcome to continue the expansion of NLP in business.
Ask any recruiter or hiring manager how many resumes they have reviewed and saved for storage within a month, and you’ll be surprised by their answer. The sheer number of resumes, therefore, data, is truly astonishing, so new, more effective and efficient ways to process candidate data are needed.
For example, the data from Glassdoor paints quite a picture: an average corporate job attracts about 250 candidates. The hiring managers will contact only between 4 and 6 of them for an interview, so making a final decision requires going through tons of information and data.
Besides, HR has been quite slow in adopting analytical platforms and tools, so the job of strategic recruitment is quite difficult. For example, these professionals often use keywords to screen resumes, which, although offering some benefits in terms of effectiveness, is still an inefficient and inaccurate method to select candidates.
However, it’s all about to change with NLP. By automating the process of resume screening and processing, technology dramatically reduces the time required for these tasks. Moreover, NLP can rank and match candidates with particular job descriptions based on the results of the scanning, which also is a major benefit for hiring managers.
Although it doesn’t quite take the load off their shoulders here, chatbots can save tons of time and streamline many HR processes.
Challenges to Overcome with Resume Processing
NLP algorithms used to process and screen resumes are in the state of continuous development, so their accuracy improves all the time. It’s still a bit early to rely on NLP for selecting the best candidates — without a doubt, this requires human intervention — so its use should be limited to assess resumes at this point.
Conversational commerce is a major area where NLP promises to deliver big. A lot of companies have already adopted chatbots to converse with customers and provide customer service, and NLP has been a major success precursor.
A chatbot provides little to no value for both customers and businesses if it’s not equipped with language processing capabilities because it won’t understand the input from customers, therefore, won’t be able to provide meaningful and relevant answers.
“For example, it won’t tell the difference between “good morning” and “goodbye,” and treat them as ordinary text inputs,” says Ann Wiese, UI specialist from WowGrade. “On the other hand, equipping a chatbot with NLP technology allows it to process the input and come up with a fitting answer.”
For example, see how the Golden State Warriors’ chatbot understands the input from the user and generates meaningful responses.
As you can see, the chatbot beautifully imitates the conversation; in fact, it looks like the user is having a conversation with a human. This, of course, wouldn’t be possible without NLP.
In addition to supplying people with content, NLP-equipped chatbots can help businesses with handling customer service requests. According to the recent 2018 State of Chatbots report, the potential benefits of chatbots here are numerous and include the following.
Credit: 2018 State of Chatbots report
Evidently, the most common benefits of employing chatbots in customer service include the ability to get 24-hour service and instant response from a business, followed by getting answers to simple questions.
Of course, this is not to say that AI-enabled chatbots will replace humans because we’re still the best at providing detailed answers and the most effective support; this is here to suggest that chatbots can handle a lot of simple customer inquiries and make your customer service more effective and efficient.
If the request is too complex for a chatbot to handle, it can always redirect the customer to a human support agent. Here’s how the official chatbot Domino’s Australia does this.
As you can see, the chatbot reduces the work that otherwise had to be completed by a human. For example, it asked the customer to provide the details of the request and do other things to make sure that the support agent starts working on the problem resolution as soon as possible.
Challenges to Overcome with Customer Service
Even though chatbots have gotten pretty good at recognizing human input and generating meaningful answers, they still need some time to learn how to, well, “be more human.”
Naturally, a business shouldn’t treat chatbots as primary customer support agents; in fact, the most effective support is provided by a combination of a human agent and a chatbot.
This combination is ideal because it allows to quickly resolve simple requests without involving humans, but still provides the opportunity to connect with customer support teams for more complex issues.
Designed to collect and analyze sentiment, NLP is perfect for monitoring of customer opinion. By using an NLP algorithm designed for opinion mining — this is the actual name of the technique — a business can automate the process of sentiment analysis and:
● Define how customers react to the company or its products/services (negative, neutral, positive)
● Recognize emotions and mood (anger, satisfaction, sadness, happiness, etc.)
For example, this sentiment analysis done by Peter Min for a restaurant with a Python-implemented NLP algorithm processed Yelp data and revealed the following.
First, here’s the percentage of positive and negative sentiments concerning five specific aspects: ambiance, food, misc, price, and service.
Credit: Peter Min
As you can see, the price is the best-performing area here. By studying results like these, a business can improve its service in specific aspects, as required by customers.
The sentiment analysis performed by Min also defined the most popular themes mentioned in Yelp reviews by customers. Here are the results.
Credit: Peter Min
As you’d expect, the most popular aspect was food. By digging deeper into the results generated by the algorithms, you’ll be able to identify customer feedback on specific dishes, which could be very interesting. Since customers tend to mention specific dishes in their reviews, it would be easy for you to see which ones are getting positive feedback as well as the ones that might need some improvement.
Challenges to Overcome with Sentiment Analysis
The data generated by NLP algorithms might not be exactly user-friendly, so businesses looking to take advantage of this technology should consider hiring qualified professionals to make meaningful conclusions from the collected data.
Evidently, NLP holds major promise for businesses. From more optimized recruitment processing to better customer service and sentiment analysis, it’s reasonable to claim that every business out there can benefit from this technology. And, since NLP continues to develop at the speed of light, we should see even more exciting cause studies and use cases pretty soon.
Credits — Dorian Martin
Dorian Martin is a frequent blogger and an article contributor to a number of websites related to digital marketing, AI/ML, blockchain, and data science. He also runs a personal blog NotBusinessAsUsusal and provides training to other content writers