Every day we face an enormous volume of text and voice data. How can we systemize this information and decide on the response? The answer is Natural language processing – or simply NLP. Data science solutions can make an impactful difference in many spheres. Such technologies as NLP can divide the text into components so it could understand the context and the intent. The machine can then decide which command to execute – based on the results of the NLP.
There are a few ways that machine learning and natural language processing can create surprising value for your eCommerce business. Below, using NLP modeling project examples to illustrate how you might go about your own analysis, we’ll break down the basics of NLP and its applications.
E-commerce retailers can use NLP to categorize products into highly-specific corpora to develop intelligent search bars that help customers navigate to the exact product they’re looking for. While we’re yet to scrape the surface of how NLP apps could change the way we shop, some of the world’s largest online retailers are already exploring how they can combine AI-enabled tech to revolutionize online experiences.
eCommerce and retail sectors adopted Natural Language Processing (NLP) among the first. It started with chatbots and conversational interfaces and came to automating business processes and enhancing consumers’ experience. We will go through different applications of NLP that are trending in 2022 – and how NLP can be helpful for eCommerce companies.
Understanding User Intent
It is very essential for eCommerce businesses to recognize and analyze the requirements and behavior of their customers.With the help of Natural Language Processing, machines are able easily to pick on what phrases and words are generally used by humans while they are searching for a particular product. It helps in customizing the searches for users who are interacting with the system using a search engine.
One of the goals of online retailers is to keep improving the customers’ shopping experience. That includes product discovery (with search and category browsing) – which is the highest priority for improvement since it can always help customers find the products. On the other hand, customers are not that easy to impress. Most customers expect the search systems to fully understand their shopping intent – even when the search queries are not specific, to begin.
The problem with understanding the users’ intent is that at the beginning of their search they will use basic terms such as clothes or handbags, etc. This will, of course, give limited information about shopping intent. Such queries are a broad term and can provide half of the catalog. Nothing from this search query itself can help the search system determine which products should be displayed. Should shirts be displayed? Or maybe shoes?
If a customer is looking for an item with more than one meaning, we can show all of the results. However, showing all of those results can lead to the customer seeing a lot of irrelevant items. It can also make the customer frustrated.
Usually, a business would list all of the possible outcomes based on their search query. However, it is possible to do better than that. The better upshot would be to understand customers’ intent and show them what they are looking for. By analyzing the search sessions, (and the products that the customer has bought in the past), it is easier to understand what the customer is looking for. The next time they search for something, they will most likely get the relevant products – based on their previous searches.
Smart Product Recommendations
Product recommendations are usually keyword-based. What you type in is what you will get as a result. On the other hand, NLP can take in more factors, such as previous search data and context. These factors can help in search results being more specific.
It also helps the retailers to keep the visitors interested by recommending the right things to them. If you show products that fit the customers’ needs – it will reduce site abandonment and increase the number of purchases. Amazon has stated that the purchases made through the recommendation that their site gave increased their revenue by 35%.
Sometimes users can tend to get lost among hundreds and hundreds of products. It makes them feel like it is impossible to find the product that they want. With NLP, users will not feel overwhelmed – it will seem less of a burden for them to browse through products and items. It will make users’ experience much better and more enjoyable.
Even though this advanced process of recommendation does result in high efficiency, some challenges can occur. Some of these challenges include:
- data sparsity
- cold start problem
- predictable recommendations
- incorporation of content
- hybrid data and scalability to enable humanized services for complex commerce environments
- higher user demands.
Optimal information is not always available when it comes to these search engines. It is harder to extract item features and to suggest suitable items to users. That is why it is crucial to have a recommender system that is efficient and objective – and that essentially provides a strong foundation for e-commerce.
Semantic-Based Search
Most people today use the internet and online shops to find the products they are looking for. At least 60% of people use three words (or even more) in search queries.
Users use natural language when searching for items. However, the complexity of this, alongside typos can disorient textual search. Natural language is hard to understand for search engines, and it can not differentiate between product names and product descriptions. That is why sometimes it offers irrelevant or results – which can leave the user frustrated.
For example, if someone searches for red shirts under $40 the result will be a lengthy list. It will show every product that contains keywords such as shirt, red, under, and $40. Semantic search can pinpoint typos, longer search terms, and even recognize synonyms. That is because semantic search uses natural language processing and machine learning.
It learns to understand customers’ buying patterns and behavior. Based on it offers relevant products to that customer. Semantic search will re-rank the products – it will show the most suitable items at the top of the results. That way, the customer will still be interested and probably will not leave the site early.
Semantic search can also analyze the search history and predict the terms the user is typing. Auto-completion that the semantic search is doing, saves time for the customers and helps them find what they want faster.
Intelligent search functionality
Intelligent search helps the employees (but also the customers) to find the information they need faster and easier. Users can use intelligent search to view any information they require, which helps them save time. Without it, employees (and customers) would have to do things the old fashion way and search for the information they need without any help.
Customers know what they want, most of the time. They will at least have an idea or a product in mind that they are searching for. If someone does not know what they are looking for, they will not use the search option. They will browse through the objects displayed.
For the users that do have an idea of what they want to purchase, we can confidently say that the search bar is the most crucial tool. It is an easy way for them to get what they need.
However, for this tool to be of any use, an intelligent search function has to be integrated. Just a simple search bar will not use the full potential of its functionality.
One of the main problems when it comes to search functions is the errors they encounter. These search functions often can not tell that single and plural forms are the same thing, just different numbers. These kinds of issues are mainly why intelligent search is superior.
Intelligent search can also help businesses with their dealing with e-commerce or other types of work that they do. Businesses can not use Google or other popular search engines to find answers that are business-related. That includes questions such as why is the shipment delayed or even top customer searches for the past month.
Intelligent search is always trying to give specific answers for your business. It is different when compared to traditional search engines since they do not show these answers. So, how does intelligent search work? The answer to that is: understanding human language. Data that businesses use is updated all the time and written in domain terminology.
Natural language processing enables intelligent search to understand and also query digital content from various data sources. Semantic search helps intelligent search to break down linguistic terms, synonyms, and any relations in everyday language.
As we have mentioned, intelligent search tools can understand any document. One of the reasons that are responsible for that is machine learning. Machine learning enables one to learn the visual structure of a specific document. With this type of help, the intelligent search can identify fast elements such as headers, footers, tables, or charts. It also can recognize documents like contracts or purchase orders.
Deep learning and machine learning can create immediate query suggestions which can improve the search query result itself. It predicts the information that will be most valuable to the user.
Give your customers the right answer every time and provide a better customer experience. Customers want more than FAQs. Now more than ever, they want to fully self-serve on your websites and mobile apps — virtual agents and intelligent search allow your customers to achieve independence. Self-sufficient customers translate to reduced support costs and higher customer satisfaction.
Efficient Customer Support
The aim of customer support is to improve the reputation of customer service and reduce the number of dissatisfied customers. It has the speed and power which helps in boosting the purchasing cycle by sending alerts and intriguing offers based on certain patterns which are very valuable to retain the customer and convince them to revisit the apps time and again. Chatbots can be used to make social interactions and messages fully operational.
Natural language processing has been studied for over 50 years. However, it began to reach its full potential and accuracy recently, which provides real value. We can see that that in various ways. With interactive chatbots that can respond to customers automatically, and even voice assistants that we use in our everyday life. All this helps to improve the interaction itself between machines and humans.
A lot of companies and organizations have started to use chatbots. They have become a key asset in their customer service department. By using chatbots, companies and organizations have seen an improvement in their sales. It also helped their customers having a better experience overall.
The advantage of using these chatbots is that they are available for use 24 hours a day, 7 days a week. Chatbots can even work outside of business hours. They can address queries right away, not making the customer wait for hours and feel frustrated. It is possible for them to handle multiple requests at once and still respond to all of them. Additionally, they can learn specific terms (industry language) and answer specific questions from customers.
One of the most important things when having a business is customer feedback. The problem is that customers do not often respond to any type of survey or leave feedback (rarely even ratings). That is why conversational agents are being deployed so that they can determine customer satisfaction (or even frustration) with the services they were offered. This can indeed help to fix mistakes or flaws with any product and identify features that are not working properly or that customers are not satisfied with.
That is why a lot of companies are turning to machine learning and NLP, to get true customer feedback that is beneficial. In the end, companies depend on customer satisfaction, so their opinion matters and can help with improving business.
Today, a lot of IT help desks are overloaded. They are dealing with problems such as password resets or some common problems with the website. NLP agents are available to help with this. Companies and organizations are using NLP to handle a lot of these requests. NLP agents can understand the request and redirect the person to the right employee or department that can help them further.
By letting NLP handle these requests, you can save time. It is making a positive impact on the organizations. For instance, it allows companies to have better engagement and companies can understand customers better. That way, they can improve overall customer satisfaction. Even though there is still a lot of work to do before NLP has the same abilities as humans, it is becoming a helpful tool that people can rely on.
Sentiment Analysis
Sentiment analysis can provide data on customers’ opinions. It makes it easier for computers to understand simple interactions. However, complex responses can complicate the overall comprehension of machine learning. But several methods can be used in order to segregate the complicated words from complex sentence patterns to determine the accurate meaning of the sentences. Thus, high levels of precision can be achieved in predicting the phase in similar ways. It is mostly opinions and feelings for a certain product or service. That way the company has a better insight into its products and services. Based on that, they can improve their services and products. Today, sentiment analysis is one of the most popular NLP use cases.
E-commerce uses social media for monitoring, customer interviews, and reviews to get feedback on their products. However, this can not capture all the data that NLP can. It does provide a segment of it, but not all of it.
Most customers do expect a quick response from brands on social media. Having a quick response is very important for the brand since customers sometimes can not wait. That means companies need a large number of people working just as social media managers answering these questions. That is why they implement NLP solutions that can process enormous volumes of textual data found in emails, social media posts, chats, blogs, etc. It evades bias and spots even the smallest changes in customer behavior.
It can search for words and phrases that can analyze customers’ opinions on a certain product. NLP can identify emotions like happy, sad, or angry, and can categorize them as neutral, negative, or positive. By analyzing customers’ sentiment, companies can have a better understanding of what the market needs. This way they can improve their services and offer a more personalized experience. They can also predict market trends and stay ahead of their competition.
Conclusion
With the actionable insights that it provides, NLP is getting more critical for online businesses. These insights help organizations make decisions that produce tangible outcomes. It increases business efficiency and drives growth by automating various processes. In the future, we will see NLP become more important for any online business, and it will be hard to imagine an online business without it.
Source: BroutonLab.