How AI-NLP based search can help increase conversions on your online store

Modern consumers expect the product discovery experience to be as seamless, intuitive, and frictionless as possible. They want to quickly find the products that match their needs without searching through numerous pages of irrelevant results. 

Companies that provide users with personalized search results and product recommendations tailored to their needs stand to win big. Businesses must offer an intuitive and seamless search experience that displays accurate and relevant search results.

With the advances in AI and ML algorithms, ecommerce brands have access to a plethora of tools to optimize their site search experience.

The explosive adoption of AI and deep learning in ecommerce

ecommerce businesses are increasingly moving towards intelligent and automated systems and processes to increase efficiency and customer satisfaction.

These smart systems help businesses reduce errors, increase speed, and improve accuracy. They also allow businesses to scale operations quickly and control costs. Automated systems can help brands better understand their customers' needs and enhance user experience enabling them to remain competitive in the ecommerce world.

Natural Language Processing (NLP) and Artificial Intelligence (AI) have seen tremendous development in the last decade and are being increasingly integrated into various industries, from healthcare, finance, customer service, and retail.

Deep learning (a type of machine learning that utilizes artificial neural networks to learn from data) can further help to better understand the intent of searchers. It can help the search engine “learn” from data to recognize patterns and make predictions. This can be used to understand the customer’s intent and provide them with the most relevant results.

In ecommerce, AI and NLP are increasingly used for marketing, product categorization, pricing optimization, personalization and predicting customer behavior. Deep learning, on the other hand, is useful when it comes to customer segmentation and targeting.

These tools are helping ecommerce businesses of all sizes improve their sales, conversions, and customer experience.

Amazon is a prime example of an ecommerce business using AI and NLP to maximize sales. The ecommerce behemoth uses ML to optimize its processes ranging from product recommendations to logistics. Their search results are not simply a list of products that match the user's query. Instead, they are tailored to the individual user based on their past search history, purchase history, and other data points.

Here are seven ways AI and NLP and optimizing ecommerce businesses.

7 ways NLP and AI optimizations are elevating the ecommerce experience

AI-powered tools are transforming the way ecommerce processes work. They are used for everything from creating personalized customer experiences to optimizing search engine results. These, in turn, help businesses to increase sales and efficiency by automating tasks.

The following are some examples of the ways NLP and AI are transforming ecommerce.

1. Segmentation and advanced personalization

Customer segmentation is a crucial aspect of personalization. It allows businesses to categorize customers into distinct groups based on shared characteristics. Then, the different segments can be targeted with different products, discounts or marketing strategies. These characteristics can include demographics, language, geolocation, etc.

Advanced personalization takes segmentation a step further. Brands can provide each customer with a tailored experience to meet their needs. This is based on the customer's past behavior and could include customized product offerings, personalized content, product recommendations and more. AI algorithms study a user's preferences and interests by analyzing their searches, clicks, website visits, interactions across the internet and more. 

2. Automated, intelligent customer service (Chatbots)

Providing customers with personalized customer support can help brands build trust and loyalty with them. It can add a human touch to an otherwise digital experience. 

Chatbots are an efficient and cost-effective way to provide this service to customers. Chatbots are not the monosyllabic robotic experiences that would frustrate more than delight anymore. Today, chatbots are based on NLP algorithms. They can understand and interpret the user's intent and respond in a human way by personalizing the conversation, asking questions, providing recommendations, and providing helpful information. NLP allows chatbots to interpret free-form human inputs and generate meaningful, human-like responses without programming or scripting.

3. Boost search performance with

• Dynamic suggestions - An optimal search experience is one that enables customers to find the products they are looking for easily and quickly. Dynamic suggestions are one way to boost search performance. Dynamic search suggestions display appropriate and relevant products while the user is entering a query. The search engine can even suggest related terms to broaden the scope of the user's search.

• Personalized search results - They are tailored to match each user's interests, preferences, and past search behavior. For example, a user who typically searches for tennis-related items will be shown tennis paraphernalia when attempting to search on a sports website instead of equipment related to football. By providing personalized search results, the search engine can make it easier for users to find the information they need quickly.

• Boosting certain products and categories - Product and category boosting can help customers find the most relevant results from a search by giving certain products and categories higher weight in the algorithm so that they appear at the top of the results. This can be achieved by using meta tags, such as product and category tags, to help search engines accurately identify and display relevant results.

4. AI re-ranking

AI re-ranking uses algorithms to re-order search engine results by relevance. ML algorithms analyze past search queries and click data to better understand what users are looking for. The search engine then optimizes the order of search results to display the more relevant and contextual search results at the top.

AI re-ranking relies on past user behavior and contextual data, such as the location and type of device used, to determine relevance. When the users can navigate the search results faster, and with more efficiency, it increases conversion rates.

5. Dynamic pricing

Dynamic pricing relies on the cutting-edge technologies of ML and predictive analysis. It is a data-driven technique that analyzes customer data points, like past behavior, average order value, and product page visits, to direct a brand's pricing strategy. 

By studying customer data, dynamic pricing can determine what price points will achieve maximum profit. This data-driven approach can adjust prices based on demand or customer segment.

6. Studying and predicting customer behavior

Predicting customer behavior may seem like an impossible task, but it is possible today thanks to AI. Predicting behavior involves analyzing factors like a customer's purchase history, buying habits, preferences, and product categories most commonly purchased. These kinds of insights can help companies sell more efficiently. 

Predictive analytics is a data-driven technique used to make predictions about future outcomes. By studying customer data, AI and ML algorithms can uncover patterns and trends that can help it target customers more effectively by identifying what tactics will impact customer behavior most. Predictive models help brands identify customers likely to purchase a particular product and help them tailor their websites accordingly. 

7. Smart and intuitive product recommendations

Product recommendations tailored to each customer's preferences and needs using algorithms can be very effective in increasing sales, customer loyalty, and customer satisfaction. Smart product recommendations can account for customer behaviors, such as past purchases, reviews, product searches, interactions with the website, and more.

Conclusion

Having AI and NLP at the core of the search experience can catapult conversions for ecommerce businesses. They can enable companies to provide a smoother search experience to their customers by understanding their intent and providing relevant search results. AI and NLP can also allow a more personalized experience and customized recommendations which can help businesses to increase conversions. 

Zevi's AI-powered solutions can help ecommerce businesses improve their business outcomes with an AI-driven search and discovery solution. Using Zevi, brands can deliver a better user experience and maximize profitability and conversions. See how Zevi can help you transform your business. Visit Zevi to book a demo.