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What CIOs Can Learn from Airbnb’s Official ‘Party Pooper’

Every organization has behaviors they are trying to discourage or stop. So what can we learn from Airbnb’s use of data and AI to achieve overall business goals?

string of lights over an outdoor party at night
Shutterstock/BLUR LIFE 1975
A story from CNBC grabbed my attention this past week. The headline read: “Meet Airbnb’s official party pooper, who reduced partying by 55% in two years.”

Here’s how the story begins:

“Naba Banerjee is a proud party pooper.

“As the person in charge of Airbnb’s worldwide ban on parties, she’s spent more than three years figuring out how to battle party ‘collusion’ by users, flag ‘repeat party houses’ and, most of all, design an anti-party AI system with enough training data to halt high-risk reservations before the offender even gets to the checkout page.”

I urge you to read the entire story, but let’s jump ahead to some of the results: “There was a global 55% drop in parties reported on Airbnb between August 2020 and August 2022, according to the company, and since the worldwide launch of Banerjee’s system in May, more than 320,000 guests have been blocked or redirected from booking attempts.”


So where am I going with this story? This case study is full of lessons for public- and private-sector leaders to implement. Here are a few:
  1. It’s all about the data — and new tech: Faced with hosts’ fears of property damage, lawsuits and even people dying at parties held at Airbnb rentals, Banerjee and her team applied AI to look at patterns in the data and flag potential problems before they happened.
  2. Take incremental steps to improve: “The first step, in July 2020, was rolling out a ban on high-risk reservations by users under age 25, especially those who either didn’t have much history on the platform or who didn’t have good reviews from hosts.”

    The trouble they ran into was that older friends or family members often made reservations for those under 25 to get around the bans. Even a “global party ban” meant virtually nothing until they had technology to back up the ban.
  3. Use real-life experiences to guide development of AI algorithms — and be willing to take risks: “Banerjee’s team had a new goal: Build the AI equivalent of a neighbor checking on the house when the high-schooler’s parents leave them home alone for the weekend.”
  4. Use deep learning with a variety of factors to determine risk score: “The AI models look at hundreds of factors, including the reservation’s closeness to the user’s birthday, the user’s age, length of stay, the listing’s proximity to where the user is based, weekend vs. weekday, and whether the listing is in a popular location.”
  5. When results aren’t clear, flag for human review: There will always be some gray areas, so sending unclear results to trained experts is part of the process in rare cases. But ensure that the factors used are measured and utilized to improve the mode going forward for other cases.

No doubt, this Airbnb system is not perfect and will continue to be refined as more data is available and situations change. Nevertheless, this story can be applied to thousands of similar business problems globally.


I hesitated to share a story in my blog with the somewhat off-brand title of “party pooper.” But my reasons for this may surprise you.

Back in 2006, I wrote an article for CSO Magazine entitled, “Are You the Party Pooper?”

The focus of that piece is how chief security officers (CSOs) and chief information security officers (CISOs) are famous for saying “no” to new technology innovations and advances in business productivity. Over the years, the cyber industry has grown to believe that CISOs need to “Get to YES” with the right level of security.

And I still believe that many CISOs are known for saying no, no matter what the question is. (Read my article to understand the answer to that issue.) So the goal was to not be the “party pooper” in your organization.

But Banerjee is the proud (and official) “Airbnb Party Pooper” who is also a business champion helping the company bottom line and innovating with AI. I believe that using data in innovative ways with AI technology will create many new proud tech titles that may have been previously thought of as negative.


I want to congratulate Banerjee for her team’s innovative approach to solving an unpleasant problem using data and new technology. No doubt, they are not “done,” and more enhancements are coming for Airbnb.

Nevertheless, I hope we can all learn from her experiences, while applying these lessons (and others that you’ll find in her story) to unique business situations that need to be fixed.

I am confident that more AI data stories are coming that will enhance business processes globally.
Daniel J. Lohrmann is an internationally recognized cybersecurity leader, technologist, keynote speaker and author.