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“Ads are a core of Yelp’s business,” says Jenny Lin, a data scientist at crowd-sourced review app Yelp. “And artificial intelligence has been a game changer for us.”

The company has more than 3,000 sales reps,  and because ads are central to Yelp’s bottom line, those reps are also the company’s unsung heroes, and among the primary focal points of Yelp’s AI initiatives. As the maxim goes, a sales rep can only be as effective as the time they spend selling — and artificial intelligence can maximize that time by curtailing the administrative side of the job, streamlining the sales process, and increasing their likelihood of adding value to their customers.

“AI can help them build more authentic relationships with our advertisers, help them understand advertisers’ needs more, advertisers’ goals, the environment in which the advertisers are operating — all the things that make the business unique,” Lin explains.

Artificial intelligence is also essential for personalization, Lin says, and it’s another powerful tool for their sales reps, allowing them to sell to their advertisers the opportunity to deliver to their customers that feeling of being known and understood. With AI and deep learning, Yelp can optimize photos on a business’s listings, for instance, and serve up the most relevant image for the browsing potential customer.

It’s immensely powerful and the ROI is enormous — and it’s not plug and play, Lin warns. “The short answer is there’s no one AI system,” she says. “There are a million parts to it.”

Yelp uses a wide variety of common AI tools, from Amazon Web Services to access cloud computing power, to SQL databases for storing data, and Python for data ingestion and machine learned modeling. Then there’s scaling the model and the delivery of the results to both salespeople and users — what you see on the app or on the website, which is a complex process supported by a wide variety of systems, including their own open source parallelization tool, MR Job.

But it always boils down to the data. Kicking off an AI initiative requires more than setting up Elastic MapReduce, flicking the switch on Apache Hadoop, or firing up TensorFlow.

“Your data has to be big in order to leverage the power of AI and machine learning,” Lin says. “There’s no point in using heavy duty machine learning or AI if your data is bad, you haven’t organized it well, or you don’t have much of it.”

You also have to have good data ingestion, good pipelining, and good engineers to monitor the data and structure it in a way that can be fed into an algorithm. And then there’s the human capital and the specialized expertise that knows how to leverage the insights, knows how to run tests and troubleshoot, knows how to parallelize when it’s needed, knows the machinery, and knows what to bring into play, when and where it’s needed.

“There are a million ways you can do any model, there are a million decisions you make for every model, so there are a million ways to get it wrong — but there aren’t many ways to get it right,” Lin says.

Lin points out that artificial intelligence is a force to be reckoned with across industries — in banking, for instance, traditional statistical tools can’t compare to the power of AI analysis. Smart banks are deep in machine learning trenches, converting all their old-time series models to the new predictive decision tree models. AI can turn forecasting from art into a science, and the kind of opportunities that can unlock goes far beyond the financial world.

“There’s a lot that AI can still do to power sales organizations,” Lin says. “I think in the future it’s going to help businesses understand both their customers and themselves to a degree that they couldn’t have dreamt of decades ago.”

To learn more about how AI can upend your sales organization and super-charge your reps, how to build an AI-powered company, and what kind of real-world ROI major brands are realizing from machine learning, catch up on this VB Live event.

Get free access on demand right here.

Attend this webinar and learn:

  • AI fact versus fiction when it comes to sales
  • How to build a data- and AI-friendly sales organization
  • How leading brands build real results and how they do it
  • Which AI tools actually bring results and which are still in development
  • What’s next for AI and sales?


  • Rick Winslow, VP, Head of Digital Innovation & Transformation, Capital One Commercial Banking
  • Jenny Lin, Data Scientist, Yelp
  • Ksenia Kouchnirenko, Head of Business Systems, SurveyMonkey
  • Marlene Jia, COO & CoFounder, TopBots
  • Rachael Brownell, Moderator, VentureBeat

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