🎯 Netflix's $1 Million Prize

Why Netflix paid a group of hackers $1 million in 2009

June 21, 2023

Read time: 3 minutes 2 seconds

Welcome to the 8,354 (!!) strategy nerds who have joined us over the last 2 weeks!

“There are 33 million different versions of Netflix” – Joris Evers, Director of Global Communications @ Netflix.

Netflix’s entire business rides on its personalisation algorithm.

80% of content their users watch is recommended to them by Netflix.

Their recommendation engine is so efficient at preventing user abandonment, Netflix believes it saves them more than $1 billion per year.

But how did they develop such a powerful algorithm?

In the early days, they came up with a novel strategy involving nothing more than a good ol’ fashioned $1 million prize pool…

Netflix’s $1 Million Prize

Chess Move

The what: A TLDR explanation of the strategy

In 2006, Netflix announced they would pay $1 million to anyone in the world who could improve their recommendation algorithm by 10%.

Netflix open-sourced a dataset of user ratings and asked participants to predict how those users would rate movies they hadn't seen yet.

Thousands of teams tackled the problem, sharing their results and algorithms along the way.

After 3 years, a group of Austrian, Canadian, American, and Israeli hackers called “Bellkor's Pragmatic Chaos” developed a solution verified to be a 10.05% improvement, and took home the grand prize.

Breakdown

The how: The strategic playbook boiled down to 3x key takeaways

1.  Prize economics

The genius of this strategy is the way it plays into human psychology: by aligning competitive incentives, the ‘Netflix Prize’ was a faster, cheaper, and less risky alternative to in-house research and development.

For the hackers working on the problem, $1m created the allure of a windfall gain.

Netflix made it no secret that to them the $1m was a colossal bargain for a landmark product enhancement.

A public leaderboard added fuel to the fire by creating visibility between the progress of contestants.

If no solution was found, Netflix didn’t spend a penny (on the cash prize or in time-invested).

Takeaway: Maybe offering a prize (to your users, clients, employees, partners) would get things done with less cost/time/risk?

2. Tapping into the global brains trust

By crowd-sourcing the problem, Netflix were able to tap into a global community of the world’s best data scientists and machine learning experts.

Attracting the best minds to work on your business problem is wildly difficult and expensive. Not only did the contest grant Netflix access to talent beyond its employee base, it also helped generate interest among the data science community in working with Netflix, helping them hire top talent down the line.

Takeaway: Your employees are great, but sometimes it’s worth exploring cost-effective options to leverage outside talent.

3. ‘Free’ Publicity

Yes, they had to cough up $1 million to the winning team… but this was a bargain deal for Netflix.

As an added bonus, the initiative generated a buzz of publicity. The competition was covered extensively in the media, boosting Netflix’s profile and attracting new users to the platform.

Netflix’s eye-grabbing financial commitment also increased the perceived value and sophistication of their product.

Takeaway: All publicity is good publicity!

The ‘Netflix Prize’ still stands as one of the best win-win-win scenarios out there.

Hackers won a million bucks. Users won better recommendations. Netflix won an even bigger moat for their best-in-class product.

Rabbit Hole

The where: 3x high-signal resources to learn more

[13 minute read]

The internet’s best end-to-end account of the ‘Netflix Prize’ saga. Hands down.

For all the bizarre details on the last-minute race between 2 groups of hackers, cross-team espionage, the privacy lawsuit that followed, and why Netflix didn’t end up implementing the winning algorithm, this is your go-to resource.

[2 minute read]

With over 13 million data scientists using the core platform, Kaggle built a spin-off product that lets businesses run competitions that leverage their user base to solve predictive modelling problems.

Note to self: always ask ‘Could this strategy be a standalone business?’

[16 minute read]

Aakash (@aakashg0) is a friend of the newsletter, and a ‘must-follow’ in the product/strategy niche.

In this 4,000-word breakdown, he goes over the entire history of Netflix, and the tactics/strategies/experiments/decisions that led them to 487x growth over 20 years.

That's all for today’s issue, folks! Optimise your business social feeds by following on Twitter [@tomaldertweets] and LinkedIn [/in/tom-alder]

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