Today, audiences are taking in information at all times of the day from a number of Internet-connected devices. In addition, search engines are filled with countless sites competing to be the leading authority on many different subjects. In order to compete successfully, businesses and application developers need to formulate their information discovery strategy, rank well in search engine results and reach the right audiences. However, the world of search engines is continuing to change through the use of new technologies and artificial intelligence.
In order to reach target audiences and successfully compete online, it is vital that companies understand the past, present and future of search engines.
1st Generation: Browsing for Content
In the first generation of search, search engines were rather simple and users typically worked their way through a website via a click-oriented customer journey, which saw them navigating through websites page by page in search of what they wanted. Often, websites were structured as a product catalog around a category tree and early search was done through SQL queries. In this journey, the discovery gateway, which is the most common tool for users to find what they want, was found in the navigation menu of a website. Menus allowed visitors to click through a website’s pages until they found what they were looking for or came across something new that sparked their interest.
While applications and strategies have moved beyond this form of discovery, many legacy applications still use this strategy. And although website navigation menus continue to help provide structure and navigation for visitors, modern audiences and search engines no longer solely rely on them to find what they need.
2nd Generation: Searching for Content
Eventually, the growth and increased capabilities of search engines led to customers becoming free from the need for menu navigation in order to find what they are looking for on a website. Rather than a website’s navigation menu, users now found their discovery gateway in the search bar both within websites and in search engines, which enabled them to go directly to the information they needed, rather than through a pathway inside a website.
Search experts Ryen W. White, Joemon M. Jose and Ian Ruthven described web queries as “short, ambiguous and an approximation of the searcher’s real information need,” in their paper Implicit Contextual Modelling for Information Seeking. This form of keyword-oriented information discovery led to a type of trial and error experience that saw users searching for keywords and looking through multiple results pages until they finally found what they needed. In some cases, the trial and error nature of searching can cause frustration, but it can also lead to a faster and more fulfilling online experience, as users navigate directly to what they need, rather than slowly making their way through a website until they reach their end goal. The continued efforts to create more effective and accurate results has led to the next era of search.
The Next Generation: Predicting Relevant Content
Intelligent information discovery is seen as the next stage of search, which goes beyond simple database queries and uses ranking and relevance algorithms to better match results with user interests. Today, results are continuing to go toward user-based predictions, with search engine output using scored predictions for a more successful and tailored search result within both search engines and websites. As reported by Bloomberg, Google’s deep learning system RankBrain helps generate search engine responses with greater accuracy for users in a shorter amount of time. This is just one of the many efforts to improve today’s search.
Rather than having to find the information, products and services that they want, companies are directing users toward them before they even begin searching. This can be seen in the ways that a company like Amazon tailors its homepage to the product interests of users or how search engines are autofilling search results based on a user’s past actions. The advancement of predictive content means that the discovery gateway is everywhere. Applications show users the results they would like to see as soon as possible, with interest-oriented information discovery influencing the customer journey. Data science and machine learning are becoming increasingly important in business and search strategies today. Wired’s coverage of Google’s changing algorithms shows that programmers are having less control over how search results are calculated as neural nets begin to influence a larger amount of search engine results.
Finding Success in the New Era of Search
The essential challenge in adapting to search engines powered by machine learning is modeling your content on meaningful numbers so that the data collected is linked together and better understood by algorithms to create an accurate and useful search result. When calculating search engine results, consider using TF-IDF (Term Frequency-Inverse Document Frequency), which shows how often a term appears in the field you are reviewing, how rare the term is in the whole index and the length of the field where the term appears, in order to accurately score the field when predicting results scores. From there, you can remove stopwords, spellcheck effectively, predict non-text fields and more.
Successful efforts mean the creation of smart content delivery, with a discovery gateway that is everywhere, including front page search results, autofilling in search bars, push notifications, matching new content based on previous successful actions from a user and more in order to anticipate and influence the interests and actions of users. The result is an ever-improving intelligent information score that will benefit a brand’s online presence in the long term as search engines continue to evolve and focus more on delivering personalized predictive content.