Technology and search-answer company Yext introduced a suite of algorithms, crawlers, and other features that capitalize on Google-like search technology for brands to use on their own websites and ecommerce platforms — in what the company calls the most “significant” update in its history.
The features include Extractive QA, Website Crawler, Data Connectors, and Answers
Part of the rollout includes developer tools that the company says lay the foundation for a multi-solution search platform on a company's or organization's website.
The idea is to allow on-site search to deliver diverse experiences to consumers.
Marc Ferrentino, chief strategy officer at Yext, believes “modern, answers-led search” make it less expensive to run a sophisticated query engine on a website. This is shown in Yext’s revenue earnings.
For the company's fiscal first quarter that ended on January 31, Yext posted revenue of $92.2 million, up 13%. “Despite major headwinds caused by lockdowns, our full year fiscal 2021 revenue increased 19% year-over-year [to $354.7 million], and we drove significant efficiencies in our business,” CEO Howard Lerman said in a statement.
For the fiscal year ending Jan. 31, 2022, Yext projects revenue of $375 million to $380 million.
RBC Capital Markets Analyst Mark Mahaney maintains an outperform rating on the company’s stock. In a research note published in December, he wrote: “We continue to believe that Yext has the ability to return to 20%+ growth, despite the current deceleration, given its ability to penetrate different verticals with Answers and its tenured salesforce, from which it continues to see operating leverage."
The question still remains, Mahaney wrote, whether and when Yext’s investments in International markets and increase in its sales force will translate into accelerating growth.
Despite these unanswered questions, Mahaney remain optimistic, with the view that Yext faces a “very large [total available market] with a highly visible recurring revenue model."
The features in Yext’s release include document search powered by Extractive Question Answering (QA) that relies on natural-language processing (NLP) algorithms to deliver a better search experience. It draws from structured and semi-structured data, rather than traditional keyword search, and has the ability to answer complex questions.
For example, when someone queries with a specific question -- such as what the difference is between a 401(k) and a Roth IRA on a bank’s website or how to assemble a product on a retailer's site -- the extractive QA looks at the unstructured data from a business's webpages, blog posts, help articles, and product manuals in their unique knowledge graph to find the most relevant word, sentence, or paragraph, and then delivers a direct answer in the form of a rich snippet at the top of the results page.
It also supports data connectors such as website crawlers, and developers now have tools to interact with the Yext platform and build custom solutions on their own.