Once upon a time, a simple trip to the video store was the highlight of many Friday nights. The joy of browsing through aisles of VHS tapes, the agony of choosing between two favorites, and the ever-present threat of late fees formed the backdrop of our movie-watching rituals.
But then, two visionaries, Reed Hastings and Marc Randolph, saw an opportunity to change the game entirely. What if a company borrowed a brilliant concept from the shipping and logistics industries to reinvent itself as an online movie distribution powerhouse?
And so, Netflix was born.
Today, Netflix is a streaming juggernaut worth more than $146 billion. In That Will Never Work, Netflix CEO Marc Randolph describes the company as operating under a distribution model for service delivery. The idea for Netflix’s distribution strategy came from an employee with years of experience in shipping/logistics.
While the early idea of Netflix came from the distribution industry, distributors can learn a lot from the evolution of this technology-powered online movie distributor.
The Netflix Story
Netflix began in 1998 as a DVD-by-mail startup company. Their evolution follows the path of technology itself:
- 1996 invention of the DVD player
- 1998 Netflix sold DVDs via mail
- 1982 first ecommerce businesses
- 1995 Amazon launches online
- 1998 Netflix opens their online service
- 2000 Netflix introduces a personalized movie recommendation system
- 2004 Google launches personalized search
- 2007 Netflix streaming launch
Personalization Propelled Netflix’s “Big Bet”
In 1999, Netflix discarded its pay-as-you-go rental model and moved to a subscription model. While the concept of recurring revenue seemed brilliant for the bottom line, there was a problem.
Searching online was overwhelming unless someone knew what movie they were looking for. According to That Will Never Work, “One disadvantage of being an online store was that it made browsing difficult. If you knew what you were looking for, you could just search for it. Finding movies was surprisingly difficult.”
How could Netflix help movie-watchers easily find their next binge or film? Personalization!
Personalization was a groundbreaking concept at the time. Traditionally, distribution companies focused on standardization to increase operational efficiency. But that didn’t work in the search-heavy movie distribution world.
To solve this problem, in 2000, Netflix introduced its first personalized movie recommendation system, Cinematch. Cinematch used an early form of machine learning that used member ratings to predict how much a consumer would like a movie. By 2006, Netflix was gathering explicit and implicit data from subscribers, then using algorithms to connect members and movies.
Personalization algorithms were a big bet that Netflix (and other online companies) made well before artificial intelligence (AI) became a big deal in the software world. Personalization solves a huge problem for distribution customers by making it easier to find what they need even among hundreds of thousands of SKUs. These tools also increase the upsell factor with customized suggestions for buying additional products.
Personalization can also potentially maximize inventory for distributors by suggesting products that match customer needs—but that you also want to move off the shelves.
That Will Never Work put it this way:
“When users sat down to decide which movies to order next, we wanted them to see a list of films that had been customized to their taste—and optimized for our inventory. If we could show customers what they wanted to watch, they’d be happier with the service. And if we could also show them what we wanted them to watch? Win-win.”
What Distributors Can Learn from Netflix
Companies like Netflix, Amazon, and even Google have trained our customers to expect an effortless on-demand shopping experience. Whether looking for an online movie or buying a product, distributors need to consider how easy or difficult it is for customers to find what they want.
Early in Netflix history, a subscriber would browse hundreds of titles before finding the right movie. Then they would rate movies while waiting for their Netflix mailer to arrive, adding to the valuable data the company collected on their preferences. Today, 80% of the content consumed on Netflix is generated by AI recommendations. Twenty years from now, Netflix hopes to present the “just right” movie with no browsing required. Is this possible? Absolutely, when you consider Netflix subscribers went from choosing two percent of the movies the algorithms suggested in the early 2000s to 80% today.
Now place this within the context of a common distributor problem: How to pull customers into other types of sales or different product categories?
The trick, Netflix learned, was to use the technology to differentiate their company.
“From the beginning, we knew that our company couldn’t be tied to a shipping service or a mere product—because if it was, we’d be obsolete the second the technology changed. If we wanted any chance of surviving long-term, we had to convince customers that we were giving them something better than an online library and quick shipping. Neither the technology nor the delivery method mattered. What counted was seamlessly connecting our users with movies we knew they’d love. That would be relevant regardless of what direction future technologies took us. Easier said than done, of course,” according to That Will Never Work.
What distributors can extrapolate from this lesson is a way to stay current and avoid becoming obsolete by personalizing the customer experience. Anyone can sell bearings and lubricant, but not every distributor can make it easy to find what a customer is looking for and offer Netflix’s degree of “this company knows me.”
The final lesson from the Netflix story is: Bet on innovation! When Netflix started investing in AI, it was still theoretical. It would take years to prove AI worked to influence consumer behavior. But Netflix still placed their big bet on technology, which is still paying off. Plenty of case studies show that the right technology can unlock new possibilities to drive results that previously weren’t possible. Distributors can learn from this. Instead of shying away from new technology, the industry should embrace it.
Luckily, it won’t take years for distributors to see success with AI. Personalization software now has a track record of improving sales and the customer experience. R.S. Hughes, a distributor of industrial supplies with 50 North American locations, implemented AI, and said they’ve never seen such fast ROI from any technology.
Be Netflix: Why Personalization Matters to Your Distribution Business
To highlight just how tailored Netflix was to each user, Joris Evers, former Director of Netflix Global Communications said, “There are 33 million different versions of Netflix.” In 2022 terms, that number would be 230.7 million.
The point is that Netflix has personalized their customers’ experiences so that everyone enjoys the platform a little differently. It’s not just Netflix that is reaping the benefit of this tailored sales experience. The numbers show:
- Today, 80% of consumed content on Netflix comes from personalized, AI-powered recommendations.
- 35% of Amazon’s revenue comes from personalized, AI-powered product recommendations.
- 31% of all listening activities on Spotify are tied to personalized AI-generated playlists.
Distribution companies implementing AI personalization for their customers can expect the same benefits as R.S. Hughes, who said, “We’ve seen well over seven figures in attributed revenue and hundreds of thousands of clicks for attribution to engage on those recommendations.”
Netflix spent two decades perfecting its version of AI and machine learning personalization tools. Today, these proven tools are available to any distributor. But the real question is, will distributors follow Netflix’s lead?