Fail Safe, Fail Smart, Succeed! Part Four: My Biggest Failure

Fail Safe, Fail Smart, Succeed!

My Biggest Failure

If you are a long-time Spotify user, you probably won’t recognize the interface shown in the photo below. In May of 2015, though, Spotify was very interested in telling the whole world about it. It was a new set of features in the product called “Spotify Now.”

I lead the engineering effort at Spotify on the Spotify Now set of features. It was the most extensive concerted effort that Spotify had done at the time, involving hundreds of employees across the world.

Spotify Now was a set of features built around bringing the right music for you at any moment in time. The perfect, personalized music for every user for every moment of the day. This effort included adding video, podcasts, the Running feature, a massive collection of new editorial and machine learning generated playlists, and a brand new, simplified user interface for accessing music. It was audacious for a reason. We knew that Apple would launch its Apple Music streaming product soon. We wanted to make a public statement that we were the most innovative platform. Our goal was to take the wind out of Apple’s sails (and sales!)

Given that this was Spotify and many of the things I’ve shared come from Spotify, we understood how to fail smart.

As we launched the project, I reviewed the project retrospective repository. I wanted to see what had and had not worked in large projects before. I was now prepared to make all new mistakes instead of repeating ones from the past.

We had a tight timeline, but some of the features were already in development. I felt confident. However, as we moved forward and the new features started to take shape in the product’s employee releases, there was a growing concern. We worried the new features weren’t going to be as compelling as the vision we had for them. We knew that we, as employees, were not the target users for the features. We were not representative of our users. To truly understand how the functionality would perform, we wanted to follow our product development methods and get the features in front of users to validate our hypotheses.

Publicly releasing the features to a narrow audience was a challenge at that time. The press, also aware of Apple’s impending launch, was watching every Spotify release exceptionally closely. They knew that we tested features, and they were looking for hints of what we would do to counter Apple.

Our marketing team wanted a big launch. This release was a statement, so we wanted a massive spike in Spotify’s coverage extolling our innovation. The press response would be muted if our features leaked in advance of the event.

There was pressure from marketing not to test the features and pressure from product engineering to follow our standard processes. Eventually, we found a compromise. We released early versions of the Spotify Now features to a relatively small cohort of New Zealand users. Satisfied that we were now testing these features in the market, we went back to building Spotify Now and preparing for the launch while waiting for the test results to come back.

After a few weeks, we got fantastic news. For our cohort, retention was 6% higher than the rest of our customer base.

For a subscription-based product like Spotify, customer retention is the most critical metric. It determines the Lifetime Value of the customer. The longer you stay using a subscription product, the more money the company will make from you.

With a company of the scale of Spotify, it was tough to move a core metric like retention significantly. A whole point move was rare and something to celebrate. With Spotify Now, we had a 6% increase! It was massive.

Now, all of our doubt was gone. We knew we were working on something exceptional. We’d validated it in the market! With real people!

On the launch day, Daniel Ek, Spotify’s CEO and founder, Gustav Söderstrom, the Chief Product Officer, and Rochelle King, the head of Spotify’s design organization, shared a stage in New York with famous musicians and television personalities. They walked through everything we had built. It was a lovely event. I shared a stage in the company’s headquarters in Stockholm with Shiva Rajaraman and Dan Sormaz, my product and design peers. We watched the event with our team, celebrating.

As soon as the event concluded, we started the rollout of the new features by releasing them to 1% of our customers in our four largest markets. We’d begun our Ship It phase! We drank champagne and ate prinsesstårta.

I couldn’t wait to see how the features were doing in the market. After so much work, I wanted to start the progressive roll out to 100%. Daily, I would stop by the desk of the data scientist who was watching the numbers. For the first couple of days, he would send me away with a comment of “it is too early still. We’re not even close to statistical significance.” Then one day, instead, he said, “It is still too early to be sure, but we’re starting to see the trend take shape, and it doesn’t look like it will be as high as we’d hoped.” Every day after, his expression became dourer. Finally, it was official. Instead of the 6% increase we’d seen in testing, the new features produced a 1% decrease in retention. It was a seven-point difference between what we had tested and what we had launched.

Not only were our new features not enticing customers to stay longer on our platform, but we were driving them away! To say that this was a problem was an understatement. It was a colossal failure.

Now we had a big quandary. We had failed big instead of small. We had released several things together, so it was challenging to narrow down the problem. Additionally, we’d just had a major press event where we talked about all these features. There was coverage all over the internet. The world was now waiting for what we had promised, but we would lose customers if we rolled them out further.

Those results began one of the most challenging summers of our lives. We had to narrow down what was killing our retention in these new features. We started generating hypotheses and running tests within our cohort to find what had gone wrong.

The challenge was that the cohort was too small to run tests quickly (and it was shrinking every day as we lost customers). Eventually, we had to do the math to figure out how much money the company would lose if we expanded the cohort so our tests would run faster. The cost was determined to be justified, and so we grew the cohort to 5% of users in our top four markets.

Gradually, we figured out what in Spotify Now was causing users to quit the product. We removed those features and were able to roll out to the rest of the world with a more modest retention gain.

In the many retrospectives that followed to understand what mistakes we’d made (and what we had done correctly), we found failures in our perceptions of our customers, failures in our teams, and other areas.

It turns out that one of our biggest problems was a process failure. We had a bug in our A/B testing framework. That bug meant that we had accidentally rolled out our test to a cohort participating in a very different trial. A trial to establish a floor on what having no advertising in the free product would do for retention.

To Spotify’s immense credit, rather than punish me, my peers, and the team, instead, we were rewarded for how we handled the failure. The lessons we learned from the mistakes of Spotify Now were immensely beneficial to the company. Those lessons produced some of the company’s triumphs in the years that have followed, including Spotify’s most popular curated playlists, Discover Weekly, Release Radar, Daily Mixes, and podcasts.

Part Five: Putting it into Practice

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