Book excerpt: A win-win-win for customers, companies, and society

(This is an excerpt from drafts of my book, "Algorithms and Misinformation: Why Wisdom of the Crowds Failed the Internet and How to Fix It")

Everyone wins -- companies, consumers, and society -- if companies fix their algorithms to stop amplifying scams and misinformation.

Executives are often tempted to reward their teams for simpler success metrics like engagement. But companies make more money if they focus on long-term customer satisfaction and retention.

YouTube had a problem. They asked customers, “What’s the biggest problem with your homepage today?” The answer came back: “The #1 issue was that viewers were getting too many already watched videos on their homepage.” In our interview, YouTube Director Todd Beaupré discussed how YouTube made more money by optimizing their algorithms for diversity, customer retention, and long-term customer satisfaction.

YouTube ran experiments. They found that reducing already watched recommendations reduced how many videos people watched from their home page. Beaupré said, “What was surprising, however, was that viewers were watching more videos on YouTube overall. Not only were they finding another video to enjoy to replace the lost engagement from the already watched recommendations on the homepage, they found additional videos to watch as well. There were learning effects too. As the experiment ran for several months, the gains increased.”

Optimizing not for accuracy but for discovery turned out to be one of YouTube’s biggest wins. Beaupré said, “Not only did we launch this change, but we launched several more variants that reduced already watched recommendations that combined to be the most impactful launch series related to growing engagement and satisfaction that year.”

Spotify researchers found the same thing, that optimizing for engagement right now misses a chance to show something that will increase customer engagement in the future. They said, “Good discoveries often lead to downstream listens from the user. Driving discovery can help reduce staleness of recommendations, leading to greater user satisfaction and engagement, thereby resulting in increased user retention. Blindly optimizing for familiarity results in potential long term harms.” In the short-term, showing obvious and familiar things might get a click. In the long-term, helping customers discover new things leads to greater satisfaction and better retention.

Companies that don't optimize for engagement make more money. In a paper “Focus on the Long-Term: It’s Better for Users and Business,” Googlers wrote that “optimizing based on short-term revenue is the obvious and easy thing to do, but may be detrimental in the long-term if user experience is negatively impacted.” What can look like a loss in short-term revenue can actually be a gain in long-term revenue.

Google researchers found that it was very important to measure long-term revenue because optimizing for engagement ignores that too many ads will make people ignore your ads or stop coming entirely. Google said investing in cutting ads in half in their product improved customer satisfaction and resulted in a net positive change in ad revenue, but they could only see that they made more money when they measured over long periods of time.

Netflix uses very long experiments to keep their algorithms targeting long-term revenue. From the paper "Netflix Recommender System": “We ... let the members in each [experimental group] interact with the product over a period of months, typically 2 to 6 months ...The time scale of our A/B tests might seem long, especially compared to those used by many other companies to optimize metrics, such as click-through rates ... We build algorithms toward the goal of maximizing medium-term engagement with Netflix and member retention rates ... If we create a more compelling service by offering personalized recommendations, we induce members who were on the fence to stay longer, and improve retention.”

Netflix's goal is keeping customers using the product. If customers stay, they keep generating revenue, which maximizes long-term business value. “Over years of development of personalization and recommendations, we have reduced churn by several percentage points. Reduction of monthly churn both increases the lifetime of an existing subscriber and reduces the number of new subscribers we need to acquire.”

Google revealed how they made more money when they did not optimize for engagement. Netflix revealed they focus on keeping people watching Netflix for many years, including their unusually lengthy experiments that sometimes last over a year, because that makes them more money. Spotify researchers revealed how they keep people subscribing longer when they suggest less obvious, more diverse, and more useful recommendations, making them more money. YouTube, after initially optimizing for engagement, switched to optimizing for keeping people coming back to YouTube over years, finding that is what made them the most money in the long run.

Scam-filled, engagement-hungry, or manipulated algorithms make less money than helpful algorithms. Companies such as Google, YouTube, Netflix, Wikipedia, and Spotify offer lessons for companies such as Facebook, Twitter, and Amazon.

Some companies know that adversaries attack and shill their algorithms because the profit motive is so high from getting to the top of trending algorithms or recommendations. Some companies know that if they invest in eliminating spam, shilling, and manipulation, that investment will pay off in customer satisfaction and higher growth and revenue in the future. Some companies align the interests of their customers and the company by optimizing algorithms for long-term customer satisfaction, retention, and growth.

Wisdom of the crowds failed the internet. Then the algorithms that depend on wisdom of the crowds amplified misinformation across the internet. Some already have shown the way to fix the problem. If all of us borrow lessons from those that already have solutions, we can solve the problem of algorithms amplifying misinformation. All companies can fix their algorithms, and they will make more money if they do.

Many executives are unaware of the harms of optimizing for engagement. Many do not realize when they are hurting the long-term success of the company.

This book has recommendations for regulators and policy makers, focusing their work on incentives including executive compensation and the advertising that funds misinformation and scams. This book provides examples to teams inside companies of why they should not optimize for engagement and what companies do instead. And this book provides evidence consumers can use to advocate for companies better helping their customers while also increasing profits for the company.