August 17, 2021
How Natural Language Processing (NLP) Is Helping Salesforce To Provide The Highest Level Of Support
Nowadays, natural language processing or NLP technology surrounds us in many aspects of our everyday life. To begin with, you can find that NLP technology is the sound base in your home assistant and most of the social media platforms you use. Additionally, NLP supports your writing by suggesting words and phrases that optimize readability when writing, whether in Microsoft Word or Google Docs.
As modern-day computer technology advances and becomes more and more intelligent, we take NLP technology for granted and how much we rely on it. To put things into perspective and give examples, artificial intelligence and natural language processing can be found in not only applications for optimizing text readability and word suggesting but also in text translation and even full-text generation based on available product data or attributes.
In this blog post, we’ll focus on how NLP is helping salesforce, marketing, and customer service departments across prosperous organizations to provide the highest level of support for customers. Nevertheless, before we get down to our four concrete use cases on how NLP is helping salesforce troops, let’s begin by explaining why human languages are so difficult for computers to understand and the role NLP plays in this field to help companies achieve top-level results in customer experience and support.
Natural Language Processing: Why Is It So Difficult?
Language, if you only think about the words alone, is only a part of how humans communicate. Body language, the tone in a person’s voice, inflection, stress, rhythm, etc., all play a vital role in understanding each other. But despite advances in computer algorithms that can simulate NLP’s abstract concepts on a human level, it is still complicated for computers to understand human communication fully.
The Complex Process Of Understanding Human Language
The ability to modify our behavior to unique situations, social interaction and our imagination gives us humans the ability to formulate and understand a vast range of situations that vary in complexity and levels of abstraction in human languages. Nevertheless, how these concepts are generated and processed in the human mind still remains a mystery. Regular people spend decades, and even their entire lifetime, developing the skills mentioned earlier. Still, they do it without full knowledge of the language speaking process, reflecting on today’s artificial intelligence and its subcategories as technologies dedicated to understanding and comprehending human languages between humankind and computers.
One of the biggest challenges in utilizing NLP support for salesforce and customer support is the ambiguity and complicated context understanding process of human languages. For instance, the word “shoot” comes with several meanings. With context in mind, the word shoot’s meaning can vary from shooting a photo to shooting a basketball and even shooting a person. So, if the word’s context is changed and becomes a joke or includes a pun or both, this can create a problem for the computer because it achieves language understanding by analyzing word for word. In addition, humans use various subsets of words interchangeably. So, while we understand what other people are trying to communicate, it can create problems for computer technologies to understand it the way we do.
As one can tell, communication between humans can never be taken for granted. In addition to human language ambiguity and context, people also love to use the so-called “meaning techniques” like paraphrasing. This way, the meaning of a complex or ambiguous text can often be conveyed using different words. For example, if we say: I purchased a scarce book at an antique shop, and I bought my Gold Raven at an antique shop, a human can figure out, partly from prior knowledge or research when ambiguity arises, that it’s books we are talking about and not a rare golden bird in the second sentence. Even though this makes complete sense to humans, it’s a challenging process to program computers in order to understand “meaning techniques” as regular people do.
Another significant group of challenges is related to grammatical errors, typos, and spelling mistakes. Since the 1990s, people have grown accustomed to automatic corrections when writing articles, instant messaging, and performing various other tasks on their smart devices. But often, mistakes do not interfere with the transfer of meaning because, to a large degree, our mind compensates and figures out the correct spelling or sentence structure for even severe mistakes. Moreover, many such mistakes have even become jokes that improve our communication process. But when a spelling error involves more than a single letter, for example, the computation time needed to correct it, if it is even possible, becomes enormous. Without knowledge of the context, such problems become very difficult for computers with a much lower tolerance for errors in the communication process than humans. Luckily, this is where natural language processing kicks in.
What Is NLP And How It Works
NLP is a subdomain of AI technology that trains a computer to register, process, understand, and generate human languages. Whereas NLP initially refers to a system's ability to read, it's since matured into a colloquialism for all computational linguistics. Subcategories comprise natural language generation (NLG) the computer's capacity to create communication of its own, and natural language understanding (NLU)—the ability to learn slang, misspellings, mistakes, and other variables in human linguistics. Natural language processing's groundbreaking technology empowers machine translation services, virtual assistants, speech recognition, spam detection, document summarization, autocomplete, voice assistants, search engines, predictive typing, and many other intelligent systems, providing genuine NLP support for salesforce and marketing departments throughout the most successful organizations across the world.
NLP mainly works through another application of AI called machine learning, or ML, that provides systems the ability to learn and improve from experience without being explicitly programmed automatically. Instead, machine learning concentrates on the evolution of computer programs that can access data and utilize it to discover for themselves. Put into practice, machine learning systems gather and store words and the ways they come together, just like any other type of data. Sentences, phrases, expressions, and sometimes full books are put into machine learning engines where they’re processed using grammatical rules and people’s real-world linguistic habits. Below, we’ll showcase three real-world use-cases of how NLP is helping salesforce teams achieve their goals and provide their customers with superior care and support.
How NLP Is Helping Salesforce To Provide The Highest Level Of Support?
NLP Supports Complex Search Systems
You’re probably familiar with the saying that goes for the search engine giant Google: If you can’t find it on Google, then it doesn’t exist. But, in eCommerce and any other type of business that involves selling products or services, if the customer can’t find it, chances are, they won’t buy it either.
In fact, for most businesses today, finding information is crucial to both the user and the company providing a service. The need to know what customers are looking for has prompted the development of dynamic search engines that sift through products and texts to return a correct and relevant response to a search query. NLP algorithms, such as word vectors, decompounding, lemmatization, phrase identification, and the use of word embeddings (for semantic search), can improve search systems in different ways and provide genuine NLP support for salesforce efforts. Let’s deep-dive and learn more about the two NLP techniques and how they can help you navigate search systems to provide a better experience.
It’s common to get suggestions when searching for products, but they are often not well implemented in the search system or very useful. The good thing here is that NLP can improve your organization’s search suggestions in a relevant way. For example, if someone types “weber gas bbq” maybe you want to also suggest gas for bbq or beer can chicken roaster rack.
Sometimes a customer tries to find precise information, for example, the recipe for Carbonara spaghetti. Suppose the company does not have the needed information on precisely that dish. In that case, we can guess that the user is interested in cooking Italian food, and we can suggest dishes in the same genre. A typical search engine cannot make such an inference, but we can improve the search result thanks to NLP and aggregation.
Search engines use keywords, tags, and other metadata to return a relevant and correct response to a search query. However, one problem in this kind of solution are synonyms. For example, suppose a prospective customer is searching for jeans, but your metadata only contains the word denim. In that case, the search engine will return a negative response because it doesn't know these two words are synonyms and should provide the same subset of relevant documents. By implementing NLP-powered software like Y Meadows' solution, you can easily use word vector translation to automatically check if two words have a similar meaning and better serve your customers.
Recognizing The Voice Of The Customer
Companies should always remain interested in what customers think about their products, and for that reason, they now collect more customer-related data than ever. Examples of valuable customer data include:
- Customer reviews
- Feedback from social media
- Interactions with customer support representatives
All of this data most often results in vast amounts of text to read. So, how can you reasonably expect to review and identify the critical insights contained in that data? And while this data is often unstructured to be easily analyzed, it’s ideally suited for automation with another NLP technique called text analytics, especially customer sentiment analysis.
Sentiment analysis is contextual mining of text which identifies and extracts personal information in the source material. Given an extensive database of millions of documents, sentiment analysis applies an emotion analysis of the text. The technique looks for positive or negative words and relates them to defined emotions like happy, sad, scared, angry, etc. Underpinning sentiment analysis is a broad range of subjective analysis tools, representing another authentic way of how NLP is helping salesforce provide a more efficient level of support when dealing with different customers.
Automatic Classification Of Product Information
Handling product information systematically and in a structured way is often a challenge for many companies that regularly generate many documents. In order to be successful, documents should be stored or handled by classification or tagging, for example. Unfortunately, such a process often involves creating a hierarchy of classes which is commonly performed manually.
In such a situation, the creation of the metadata is crucial for correct classification. Automation provides an advantage as adding the metadata is fully automated and ensures a consistent understanding of taxonomy. Therefore, using automatic metadata generation will improve your product classification and enable intelligent archiving, for example, and improve search accuracy and precision. What is involved in metadata generation is using a pre-defined hierarchy and an algorithm that sorts the data into the defined categories without changing a document’s metadata.
Thanks to an NLP technology called “named entity recognition”, document, and product data can now be automatically classified. With named entity recognition, one can, for instance, resend mail to the proper person based on the mail content in real-time. Using named entity recognition, you can also improve your product data quality by automatically adding metadata to the product, making it easier to find (on the website, for example), so your company can do a more accurate analysis of sales data. Automatic classification of product information is another excellent example of how NLP is helping salesforce teams to provide clients with better support and reach pre-set goals of customer acquisition and retention.
Natural Language Processing is one of the most efficient techniques to handle and generate value from enormous volumes of unstructured data. Here are the three concrete use cases describing how NLP technology can be used to support your sales and marketing:
- It can help your customers find the information they are looking for and inspire them with new perspectives - in other words, guide them to purchase and repurchase.
- NLP can help you detect customer satisfaction more widely and precisely, analyzing vast quantities of unstructured data. This also generates more possibilities to use customer feedback in product development to stay relevant to the market.
- Using NLP techniques to automatically classify product data and create a stable and fundamental database of your organization's most valuable data assets will allow you to be more successful when building intelligent applications and analyses.
All in all, Y Meadows' easily integrable NLP-enhanced model can help you address all of the issues mentioned above and provide genuine NLP support for salesforce teams within your organization. So, if you want to find out more about our product, or try out our demo, contact our sales department and start implementing artificial intelligence in your business processes today!