Yelp is a leading online review platform that connects consumers to local businesses, with over 100 million recommended reviews. Revenue is generated primarily from the sale of advertising on the Yelp website and mobile apps to local businesses, followed by transactions.
Artificial Intelligence (AI): A Solution for Productivity Woes?
Despite successfully carving out a profitable niche in the consumer technology space, Yelp has one of the lowest revenues per employee among publicly traded tech companies [1,4]. With a high-touch sales force, Yelp has struggled to scale its advertising revenue as productively as its peers. Sales representatives account for about 65% of its workforce, compared to less than 30% for Alphabet . In 2017, Yelp revenue per employee was less than $150,000 , compared to over $1M for Netflix, Apple, Facebook, and Alphabet . AI represents a significant opportunity for Yelp to scale beyond high-intensity sales.
Industry-wide, technology companies have invested in AI to increase customer lifetime values and retention. Netflix’s search and recommendation algorithms powered by AI is estimated to save the company over $1B annually in recovered subscription cancellations . While total US digital ad spend grew over 20% in 2017, Yelp barely registers in spending market share, despite being a top media property . Yelp’s low margins and low revenue scalability have been linked to high account churn, representing a significant area for improvement . Furthermore, headwinds from stagnant to negative visitor growth  has created a need for highly personalized content and recommendations to reinvigorate user engagement. Spotify saw daily engagement increase 20% and long-tail “artists listened to” improve 65% with the launch of AI-powered personalization features such as “New Releases for You” .
Incremental Improvements and Building Engagement Moat
Historically, Yelp has taken a fragmented approach towards artificial intelligence but made strides in adoption as recently as this year. In 2015, the Search and Relevance team launched features to classify user-generated photos, helping users make faster decisions on where to dine based on food, interior, exterior and menu photos . In 2017, nearly two years later, incremental changes in photo classification quality was launched . In 2018, more teams started adopting machine learning into their respective contexts: from new ad products for businesses , to better search and browse experiences for consumers . By July 2018, Yelp officially launched artificial intelligence tools for its sales teams to streamline the sales process, cross sell ad products, and optimize ads for businesses .
Yelp is still in the nascent years of scaling out new products and business lines, with revenue from non-ad products at less than 10% of overall revenue . These new products will prove crucial to the gathering of both greater volumes and increased frequency of data to serve more relevant content, recommendations, and services to customers and businesses alike . One year after the Grubhub partnership was announced, “popular dishes” powered by machine learning launched to help consumers solve decision fatigue . One year after the Nowait acquisition , Yelp launched waitlist kiosks and the ability to join a waitlist remotely, saving consumers time and providing new insights to restaurant owners, allowing restauranteurs to open up more seating and drive greater revenue .
Siri, Yelp Me the Future
To continually ship AI-powered products that are truly compelling, data and talent reign king. New mediums of engagement are needed to widen the data funnel and increase frequency of data acquisition to foster new product development . In addition to deepening its engagement moat, Yelp could consider investments into industries adjacent to its local business core competencies: for instance, mapping/navigation and deeper integration with IoT devices (specifically, autos and voice) where consumers are spending increasing share of online time.
Organizationally, Yelp should make AI a strategic priority and build team capabilities across business development, product, engineering, operations, and sales to execute AI-driven features faster. Attracting and retaining talent remain key , as evidenced by Yelp’s own “AI winter” from 2016-2017 when significant talent left for rival companies. To build new revenue, Yelp needs to change its mindset from “AI for operational improvement” to “AI for new products” that connect more deeply to consumers and businesses than ever before . Google’s approach to AI as a way to “save users time”  can be helpful in thinking about how to create a deeper moat around increased user engagement. By being the most efficient way to achieve a task or to research information, Google is betting they can remain the default choice for consumers.
In the face of well-capitalized competitors that have had a near monopoly on data and talent, is Yelp too late to the AI game? Where and how can Yelp outmaneuver industry heavyweights like Google, Facebook, and Amazon?
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