Technologies such as Artificial Intelligence (AI) and Machine Learning (ML) have been slowly but surely transforming the world over the past few decades, and their influence is only set to grow. Indeed, the AI market is expected to grow at a whopping annual rate of 33.2% during the period 2020-2027. 

One of the many aspects of modern life that have been revolutionized by AI and ML is online search. The effectiveness of search engines has been steadily improving thanks to AI-based search ranking algorithms. Such “smart” search engines rank search results according to a set of criteria that they learn by applying ML-based techniques to vast amounts of data.

Such ranking makes search results more relevant and useful for users, thus reducing the amount of time and effort that users have to expend dealing with low-quality search results.

Earlier, search ranking was primarily based on something called keyword-based (or string-based) matching. A search engine using this approach first finds results that match each individual word and the various sub-strings in the query string, and then ranks these results based on the number of times a word or sub-string was present in the documents associated with each result. 

For example, if a user searched for the word ‘fashion magazine’, such a search engine would return pages that contained the strings ‘fashion’, ‘magazine’ and ‘fashion magazine’, and then sort these pages as described above. 

AI-based search ranking, however, is a game-changer that delivers much better results than this older paradigm, and also solves many of the problems associated with it. So let’s take a deeper look at AI-powered search ranking.

What is AI-powered search ranking?

Keyword-based matching typically fails to consider the user's intent, resulting in irrelevant or inaccurate results, which can be expensive for businesses: 68% of online shoppers would never revisit a site that had provided them with a poor search experience. That is where AI-powered ranking comes in.

AI-powered ranking overcomes this problem by using ML algorithms to analyze past search queries and click data to better understand what users are looking for. It then optimizes the order of search results to display the most relevant results at the top.

AI-based ranking can also rely on other kinds of user-specific data, such as their location and the type of device they use, to determine relevance. By improving search relevance and efficiency, it also increases conversion rates.

AI-based ranking vs legacy/statistical ranking systems - what has changed?

AI-based ranking systems are powered by machine learning and consider ranking factors that traditional statistical models overlook to produce more accurate and relevant results for a search query. These systems can learn from past user behavior to better understand what type of content is likely to be most suitable for a given query.

For example, Natural Language Processing (NLP) is a form of AI that enables machines to understand natural human language better. It can help search engines better understand user intent and provide more relevant search results. More advanced technology, like neural networks and evolutionary algorithms, will continue to improve the way search works in the future.

For now, let's try to understand how AI ranking works.

Understanding AI ranking - how it works

AI ranking relies on something called 'Learning to Rank' (LTR).

LTR is a technique used in information retrieval, which uses ML algorithms to rank the websites returned in response to a search query. It takes the usual results of the search engine and re-sorts (i.e. ranks) them according to the user's preferences or needs. The goal of LTR is to improve the relevance of the search results for the users.

In LTR, an AI model is trained to predict a relevance score for websites returned by a search engine using data from user interactions. The model is then used to create a ranked list of websites based on relevance scores. With LTR, the model can intuitively learn to rank websites on its own, thereby increasing the accuracy of the search engine result.

The learning can be done in three ways as stated below.

1. Supervised learning

In supervised learning, the model is trained by humans who categorize and label datasets to help the algorithm learn. For example, humans can categorize women's clothing products in different colors to teach the algorithm. But this method is time-consuming and suffers from cognitive bias. Some clothes could have more than one color, or the labels may have different opinions about the color of a particular garment.

2. Unsupervised learning

To remove this cognitive bias, ML algorithms can undergo unsupervised learning. Here, the machines learn to rank results on their own based on the dataset available. This approach is more prone to precision and recall errors.

3. Reinforcement learning

The third type of learning is called reinforcement learning. Here the algorithm learns how to rank based on the feedback it receives on its results. When the model receives positive user feedback like clicks, visits, conversions or sales, it helps the model make incremental improvements in its search ranking.

Using these three Learning to Rank methods, AI can learn how to rank search results in a way that displays the most relevant results to users first. This, in turn, improves user experience and increases conversions.

Dynamic ranking and Intelligent search

Dynamic ranking goes a step further than static ranking. In dynamic ranking, search results are reordered in real-time based on user interaction. The search engine can analyze user behavior and modify the ranking accordingly. This helps to personalize the search results to the user's preferences and offer them more appealing results.

Personalized search can be based on a variety of factors. These could include the user's past search history, click behavior, the content they have interacted with, geolocation, demographic information, and so on. The goal of personalized search is to provide a customized set of search results optimized to the individual user's interests.

Another method to optimize search is analytics-based ranking. This method collects data from various sources like web analytics and search engine optimization to measure user engagement. The model can identify the most important web pages with this information. The data is then used to rank individual pages or entire websites according to the criteria most relevant to the user. This type of ranking provides more comprehensive results than traditional ranking methods.

Using these methods can help businesses provide users with an enhanced and smarter search experience. These techniques improve the user's experience by ensuring that search queries are accurately addressed, and that irrelevant results are eliminated.

Conclusion

AI search ranking is the natural next step for businesses looking to improve the search experience on their websites. With AI search ranking, users get a smoother and more efficient search experience that’s tailored to meet their needs.

Zevi can help improve business outcomes with an AI-driven search and discovery solution. Its powerful AI solutions guide your prospects from discovery to conversions with an intent-focused and easy-to-integrate website search engine that helps boost engagement and sales. Book a demo with us today.