Do you know why most people are intrigued by the fortune-tellers? That is because they can predict the future, even though there is no particular evidence. If we know what is coming, we can prepare our plan of action. E-commerce industry is no different. If we can predict trends, we can better optimize the product sourcing,customer acquisition, user experience, marketing and supply chain. Fortunately, this is possible by analyzing an immense amount of data through machine learning. One area that can be significantly improved through machine learning is the site search. In fact, machine-learning is a driving force behind self-learning search.
But, What is Self-learning Site Search?
A self-learning search is a search that essentially uses algorithms to calculate values, trends and other parameters of certain things based on the historical data. It utilizes large and complex data to identify patterns, and enhances performance and predictions without further programming. The extensive information about e-commerce businesses allow site search to learn about the users, their shopping behavior, purchase history and make precise behavioral assumptions.
The key to all of this is that marketing agencies work hard to bring traffic to your site, but ultimately conversions depend on enabling them to find what they are looking for, and that is exactly what an intelligent search does. When customers begin a search, they are in the last phases of buying process with a high intent of placing the order. Making sure your customers find what they are looking for is essential for providing a high quality user experience and closing the deal.
How Machine Learning Makes Site Search Intelligent?
When your visitors make a search or click on search results, they implicitly indicate you what is most relevant. As they browse the product pages, download content, watch videos or contact your customer support team, their behavior provides information about the relevance of the content they come through. This search behavior as well as behavioral data, which provides invaluable information, is captured by search usage analytics.
E-commerce search powered by machine learning can benefit from search analytics to continuously self learn. This enhances the search relevance, and hence, the search experience of your online store. If the search is not intelligent or self-learning, store owners may need to refine the search manually, such as building listing rules, adding content, adding synonyms and so on. Since relevance is an ever-evolving process, the documents that were relevant today may not be relevant tomorrow. Thus, it is nearly impossible to manage these things, especially where the amount of products and content is enormous.
It vastly improves product searches
Search is hands-down one of the most important elements for the success of your e-commerce store. If your items fail to appear in the search results, how will your prospects know that you stock them? That’s where a self-learning search can help. It continually learns to improve product search results, boosting your product visibility, click rates and conversions.
A robust search backed by machine learning can help you get much more of the product search experience, and your potential customers are more likely to find the products or services they are searching. When machine learning is deeply integrated into your e-commerce site search, search results become more sensible, and direct customers towards what they actually need rather than what they just typed in the search box.
It offers better customer engagement
Since self-learning search engines are smart and efficient, gone are the days when you have to implement basic keyword-driven search, where online stores have to rely on native search. The modern self-learning search utilizes natural language processing and advanced machine learning capabilities that offers a far better solution to learn more about the phrases used in search. These highly efficient systems, then, deliver semantically-connected and highly relevant results through the search that is optimized for relevance, speed and performance.
By delivering highly relevant results as quickly as possible, and in a way that facilitates users buying decision, the online stores can connect with its customers more effectively. Shoppers feel like the store is working for them, with them, and hence, they get a sense that the store is actually willing to offer what they need. This approach drives trust, and turns one-time customers into returning customers.
Another engagement-related feature of self-learning search like Wizzy is its ability to match the most complex queries. Wizzy’ssite search automatically adds new synonyms and adjectives, which empowers the search with more contextual data. Moreover, with the data enrichment process, which is capturing more data relevant to the product, it enables you to deliver more accurate results. The more you enrich the data, the more user queries will match the products, and hence, better engagement.
It improves customer shopping journey
The quicker your customers are able to progress through an e-commerce site search to their desired products, the more likely they are to place the order. An intuitive search can be the quickest route to products. Implementing an advanced Magento search extension like Wizzy can make the entire shopping journey more convenient and more efficient. Starting from intelligent auto-complete to smart suggestions, relevant results and advanced facets to narrowing down the results to the most relevant ones, search can drive users through the process in a few key strokes.
Search boxes start displaying products as prospects type in, allowing them to locate products faster, without having to go through all the search results. With Wizzy, you can also integrate merchandising features, such as displaying product images, prices, exclusive discounts and so on, associated with each image. As this level of search optimization has become a norm, it can dramatically improveuser shopping journey.
It boosts conversion rates
Turning visitors into shoppers is critical for any e-commerce site. That’s why it is very common for you to be obsessed with conversion rates. Self-learning search has proven to deliver smarter results through natural language processing and understanding what users have typed in the search bar. The search will learn from the previous searches to see what users genuinelyinterested in, even when they don’t type the exact name or description.
Beyond smart search results, self-learning search also offers smart product recommendations. Self-learning search recognizes what customers search or buy, and the content they look for. When a user comes back to make another purchase, they are presented with similar products to those they have shown interest earlier. Amazon is a good example, as it comes up with a number of recommendations based on the items you have purchased recently or looked at. This further encourages customers to buy related accessories or better upgrades of the products they bought before.
It delivers personalized user experiences
Having a self-learning site search is no longer a nice-to-have feature in the e-commerce industry, but a critical part that help businesses streamline their processes. Smart e-commerce site search solution goes beyond findability and information access. Like a reliable friend, self-learning knows what products to display and what content users would find most valuable, simply providing everyone whatever they need and whenever they need.
Wizzy’s AI-powered search engine continually analyzes user behavior, search terms and shopping patterns in order to determine which results lead to successful outcomes. It then automatically enhances future search results, so the best performing products or content is displayed at the top. Besides optimizing search results, Wizzy’s smart search also offers relevant suggestions, such as correcting common misspells, active search query suggestion and auto complete search phrases without requiring you to put any extra effort.
The new e-commerce search from Wizzy not only makes it easier for you to implement robust site search, but it also introduces advanced machine learning capabilities that learn from previous user interactions with search to improve user experience as well as boost e-commerce sales.
It helps generate insightful reports
Self-learning search tool also stands out when it comes to the level of analytics they offer. While it uses the data collected from search queries to empower the search engine, the same data can be used to generate comprehensive reports. E-commerce owners can determine their best selling products by looking at the number of units sold, and can also identify which products are being searched, but not purchased.
This data report can further be used to identify suppliers and new product lines. Going a step ahead, predictive analytics can observe search data besides sales statistics to recognize buying patterns, trends and occasional offers, which can be useful in making purchase and stock decisions.
Bottom Line
Self-learningis an ongoing process. The more users interact with the search, the smarter and more accurate it will get. Self-learning search is becoming an important asset to e-commerce companies looking to automate their costly and manual processes in order to gain invaluable insights of their customers, and most significantly, their clicks, orders, repeat visits and positive reviews. Wizzy’s intuitive search responds relevantly and quickly to every query, recommends related content and predicts what’s important to each individual based on analytics.
