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Natural Language processing and AI – AI technology for businesses is an increasingly popular topic and all but inevitable for most companies. It has the power to automate support, enhance customer experiences, and analyze feedback. While implementing AI technology might sound intimidating, it doesn’t have to be.
Natural language processing (NLP) is a form of AI that is easy to understand and start using. It can also do a lot to help propel your business forward.
This article will cover the basics of NLP to help you get started.
Read on or use the links below to “jump” to each section:
- What is NLP?
- Why should businesses use NLP?
- How can businesses use NLP?
- NLP usage examples
- Use Natural Language Processing to Boost Your Business
What is Natural Language Processing?
Natural language processing (NLP) describes the interaction between human language and computers. It’s a technology that many people use daily and has been around for years, but is often taken for granted.
A few examples of NLP that people use every day are:
- Spell check
- Voice text messaging
- Spam filters
- Related keywords on search engines
- Siri, Alexa, or Google Assistant
In any case, the computer is able to identify the appropriate word, phrase, or response by using context clues, the same way that any human would. Conceptually, it’s a fairly straightforward technology.
Where NLP outperforms humans is in the amount of language and data it’s able to process. Therefore, its potential uses go beyond the examples above and make possible tasks that would’ve otherwise taken employees months or years to accomplish.
Why Should Businesses Use Natural Language Processing?
Human interaction is the driving force of most businesses. Whether it’s a brick-and-mortar store with inventory or a large SaaS brand with hundreds of employees, customers and companies need to communicate before, during, and after a sale.
That means that there are countless opportunities for NLP to step in and improve how a company operates. This is especially true of large businesses that want to keep track of, facilitate, and analyze thousands of customer interactions in order to improve their product or service.
It would be nearly impossible for employees to log and interpret all that data on their own, but technologies integrated with NLP can help do it all and more.
There are a wide variety of different applications for NLP. Below are just three different ways that companies can use the technology in their business.
Improve user experience
NLP can be integrated with a website to provide a more user-friendly experience. Features like spell check, autocomplete, and autocorrect in search bars can make it easier for users to find the information they’re looking for, which in turn keeps them from navigating away from your site.
Chatbots are nothing new, but advancements in NLP have increased their usefulness to the point that live agents no longer need to be the first point of communication for some customers. Some features of chatbots include being able to help users navigate support articles and knowledge bases, order products or services, and manage accounts.
Monitor and analyze feedback
Between social media, reviews, contact forms, support tickets, and other forms of communication, customers are constantly leaving feedback about the product or service. NLP can help aggregate and make sense of all that feedback, turning it into actionable insight that can help improve the company.
Wonderflow’s Useful Reviews tab inside The Wonderboard, is especially useful for analyzing overall feedback:
In this area, you can view your most useful reviews. Wonderflow will then highlight the positive and negative statements in these reviews so you can quickly distill this information and evaluate how each of your products or services are perceived by customers.
Recently, Wonderflow was selected by independent research firm Aragon Research as one of the companies making an impact in document analytics. Check out the report here.
Natural Language Processing examples for Businesses
Below are a few real-world examples of the NLP uses discussed above. Some of these examples are of companies who have made use of the technology in order to improve their product or service, and some are actual software providers that make this technology accessible to businesses.
1. Form Spell Check
Spell check is a form of NLP that everyone is used to by now. It’s unobtrusive, easy to use, and can reduce a lot of headaches for both users and agents alike.
Not every user is going to take the time to compose a grammatically perfect sentence when contacting a help desk or sales agent. Salesforce knows this, so they made sure their contact form was equipped with spell check to make users’ lives easier.
This also makes their employees’ lives easier, too. Error-ridden customer messages can be difficult to interpret, leading to miscommunication and frustration for all involved.
2. Search Autocomplete
Search autocomplete is another type of NLP that many people use on a daily basis and have almost come to expect when searching for something. This is thanks in large part to pioneers like Google, who have been using the feature in their search engine for years. The feature is just as helpful on company websites.
Salesforce integrated the feature into their personal search engine. Users interested in learning more about a topic or function of Salesforce’s product might know one keyword, but maybe not the full term.
Search autocomplete will help them locate the correct information and answer their questions faster. This helps cut down on the likelihood that they’ll become disinterested and navigate away from the site.
3. Search Autocorrect
It’s easy to make mistakes when typing and not realize it. If the search engine on a website doesn’t catch that mistake and instead shows no results, then potential buyers might assume you don’t have the information or answers they’re looking for and may instead go to a competitor.
HubSpot reduces the chances this will happen by equipping their site’s search engine with an autocorrect feature. It catches errors and displays the appropriate results without requiring users to take any additional steps, the same way a Google search would.
4. Smart Search
With NLP, autocomplete isn’t the only way businesses can upgrade their on-site search.
Klevu is a smart search provider that is powered by NLP but is also self-learning. It works best for e-commerce because it learns by watching how shoppers interact with search on the store.
In addition to providing the basic autocomplete search function, Klevu automatically adds contextually relevant synonyms to a catalog that can result in 3x the depth of search results. The software also provides personalized search, offering products that customers previously interacted with or products that are trending.
5. Machine Translation
Globalization widens or opens up markets that may have been previously unavailable to companies, thus increasing the opportunities for growth. It’s definitely an exciting prospect, but less exciting is how to adequately serve and communicate with customers and potential buyers from different countries.
Lilt is a translation tool that integrates with other platforms, such as support software like Zendesk (who happens to be one of their customers), to make communication across language barriers quicker and cheaper than with a human translator alone.
The tool, which was developed by two former engineers who worked on Google Translate, is not totally automated, but in fact works with and learns from a human translator in order to become more effective over time.
6. Messenger Bots
Facebook Messenger is one of the latest ways that businesses can connect to customers through social media. NLP makes it possible to extend the functionality of these bots so that they’re not simply advertising a product or service, but can actually interact with customers and provide a unique experience.
In 2015, Uber launched its Facebook Messenger bot. The bot makes it quick and easy for users to order a car from within the Facebook Messenger app. This is especially useful if the customer has access to the destination address from within the app, as seen here:
The easier a service is to use, the more likely that people are to use it. Uber took advantage of this when they developed this bot and created a new source of revenue for themselves.
7. Virtual Assistants
In 2016, Mastercard launched its own chatbot that was compatible with Facebook Messenger, but compared to Uber’s bot, the Mastercard bot functions more like a virtual assistant.
The Mastercard bot is almost as good as having a bank teller in your pocket. It’s able to complete a variety of tasks for users, such as helping them get a bird’s eye view of their spending habits or letting them know what benefits are available to them from their card.
Best of all, it negates the need for customers to learn how to use a separate app, and also has the potential to cut down on Mastercard’s expenditure on developing another app.
8. Knowledge Base Support
By now, many people have seen chat boxes on websites where they can immediately ask an agent for help or more information. Chatbots can serve the same function as a live agent, freeing them up to deal with higher-level tasks and more complex support tickets.
Zendesk offers Answer Bot software for businesses and, of course, uses the technology on its own website to answer potential buyers’ questions. The Answer Bot helps users navigate the existing knowledge base, pointing them toward the right article or series of articles that best answer their questions.
If the user still isn’t satisfied, the Answer Bot will start a support ticket for the user and get them in touch with a live agent.
9. Customer Service Automation
Bots are useful for helping customers navigate knowledge bases, but can they be used to process unique support tickets? With NLP, the answer is yes
The customer service automation provided by DigitalGenius is a bit different from the Answer Bot provided by Zendesk. DigitalGenius uses their proprietary NLP and AI engine to generate answers to incoming questions and automatically fill case data.
Those with confidence ratings above a certain threshold—as seen above—are automated, while the rest get forwarded to a human agent. DigitalGenius learns from each interaction, making future support tickets even more effective. This kind of automated support doesn’t just save businesses money. It also expedites help for customers, who come away feeling more satisfied.
10. Alexa Skills
Alexa functions similarly to the messenger bots above, except with an almost unlimited number of possible skills. Companies can take advantage of this by developing their own skills that integrate with their products or access their cloud-based services.
Amazon’s developer site digs deep into the ways that companies can potentially profit off of building an Alexa skill, most notably with in-skill purchasing for premium content. Gal Shenar, an Alexa skill developer, claims to have an upsell conversion rate of 34% on one of his skills, which is higher than what he’d expect to see on mobile.
Amazon also financially rewards developers who create the most engaging skills, doling out money each month to those who generated the highest customer engagement in each eligible category.
11. Survey Analytics
NLP technology doesn’t just improve customers’ or potential buyers’ immediate experiences. It can improve the company’s experience, too. One the best ways it does this is by analyzing data for keyword frequency and trends, which can indicate overall customer feelings about a brand.
Despite the name, IBM SPSS Text Analytics for Surveys is able to analyze almost any free text, not just surveys. One reviewer took it for a spin by inputting files from his Twitter archive. The software can also translate text with a single click, so no feedback goes unanalyzed.
Although the software has several features that businesses would find useful, the interface is not exactly user-friendly. There are some other options out there worth looking at, as seen below.
12. Social Media Monitoring
Knowing what customers are saying on social media about a brand can help businesses continue to offer a great product, service, or customer experience. NLP makes monitoring and responding to that feedback easy.
Sprout Social is a social media listening tool that monitors and analyzes social media activity surrounding a brand. Unlike IBM SPSS Text Analytics for Surveys, Sprout Social has a more user-friendly interface and doesn’t need a ton of file input in order for it to run.
In the example above, the software is monitoring Twitter mentions for the imaginary Sprout Coffee Co. In this instance, there are a high number of mentions with the hashtag #sproutfail, which could be a sign to leadership that something needs to change. However, there are also a lot of mentions with “almond,” which might indicate that new products with almond milk or syrup might go over well with Sprout’s customers.
Another way that NLP can grow businesses is by improving their content marketing strategy.
MarketMuse is one such content strategy tool that is powered by NLP and AI. The software analyzes articles as you write them, giving detailed directions to writers so that content is the highest quality possible.
MarketMuse also analyzes the current events and recent stories, allowing users to instantly create content that is relevant and ranks in Google News.
13. Descriptive Analytics
Accumulating reviews for products and services has many benefits. Reviews can increase confidence in potential buyers and they can even be used to activate seller ratings on Google Ads. However, there’s another benefit of reviews that you should be tapping into if you’re not already.
NLP-equipped tools such as Wonderflow’s Wonderboard can pull together customer feedback and analyze it, showing how frequently different pros and cons are mentioned.
The Wonderboard doesn’t just pull this information from reviews, however. It can compile data from surveys, internal data, and more. This gives company leaders a solid overview of a product’s best qualities, and which product features might need more work. More information on our solution can be found here, or book a demo via the button in the top right of your screen!
14. Automatic Insights
NLP technology continues to evolve and be developed for new uses. Automatic insights are the next step.
This feature doesn’t just analyze or identify trends in a collection of free text, but can actually formulate insights about product or service performance that are presented and read in sentence form. It’s a valuable technology to return to when it’s time to develop the latest version of a product.
The Wonderboard makes automatic insights by using Natural Language Generation. In other words, it composes sentences by simulating human speech, all while remaining unbiased. So if someone has a question such as, “What is the most negative topic for this product and is it relevant?” Wonderboard can offer an answer by drawing upon the data accumulated earlier for analysis.
Use Natural Language Processing to Boost Your Business
Automation can help rapidly transform your business. When you improve a site’s navigation, make products easier to use with support from chatbots, or develop services by analyzing feedback, your business stands to grow.
NLP makes it possible to accomplish all those tasks and then some. The right software can help you take advantage of this exciting and evolving technology. For an all-in-one solution, check out how our AI-based technology is helping many enterprises become more customer-centric.