Having a growth mindset means learning from your product mistakes and the product mistakes of others. In this new Age of Product, it is essential that you learn from both the triumphs and the failures of other product managers and leaders. So, what should be avoided when attempting to grow your product? Fanatics Betting & Gaming VP of Data Maddy Want shares insights on multiple product launches, what went wrong, and why.
Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.
On Why it is Crucial to Learn from Others’ Product Mistakes
Thanks to technology and the knowledge of those who came before us, we have mentorship and sage advice at the touch of a button. We can tap directly into the best product practices and learn from the worst product mistakes. In order to be innovative, a product manager must know what works and what doesn’t. And they must use that knowledge to drive their product forward. This is why it is critical to learn from others’ product mistakes:
“An important part of product management is reflecting on problems or failures of other PMs because if we learn from others’ product mistakes, we can avoid making them.”
“If we reflect on how other product launches and scales went that didn’t go well and learn from that in a way that lets us improve on the next venture, that’s free learning without having to pay the cost of failure.”
“Take a look at other companies and leaders’ product mistakes and draw out insights that could be useful. Identify the way that they failed. And ask yourself, if you’re passing that same test right now.”
On Learning from the Product Mistakes of the app Tracetogether
The pandemic propelled us into the Age of Product we are in today. Many companies launched apps related to COVID-19 that only stayed on the market for a short period of time. A Singapore contact tracing app called Tracetogether was a brilliant idea but failed in its execution. The company was correct to recognize that Singapore would make a great place for health-based tech innovation. There was high smartphone penetration throughout the country – about 80% of Singaporeans had smartphones, and there was a very high trust in the government – about 75% of Singaporeans trusted the government to handle their personal data correctly. The developer, Jason Bay, and his team were even able to get Google and Apple to collaborate which was an unprecedented achievement. But it never got the traction it needed. Here are some reasons why:
“Oxford University estimated about 60% of the adult population would have needed to download this app in order for it to be effective in an epidemic socket circumstance like this. Reaching those numbers is really hard, they couldn’t achieve the adoption numbers.”
“The concerns the public had about privacy took a lot of time and effort that the team didn’t have. The pandemic was raging by this point, there was no time. And so they lost some adoption because of those concerns too.”
“Too many notifications came through to keep up with and this is something that we saw in other countries as well. Public health authorities, even with the tools at their disposal, were overwhelmed by the number of contacts.”
On Learning from the Product Mistakes of the Standford Vaccine Algorithm
During the pandemic, many companies rushed pandemic-related products to market. All hoping to turn pandemic into profit. But many product mistakes were made along the way. Stanford University Hospital wanted to answer the question of which health care workers would get the vaccine first. They came up with an algorithm to help them make the decision. They created a vaccination sequence score that prioritized staff members based on their age, their job, and the number of tests and positive COVID cases in that department. The intention was to remove bias and subjectivity from vaccine prioritization by creating an algorithm to evaluate each staff member’s needs. But there were some crucial missteps along the way. These are some of their product mistakes:
“The algorithm was divorced from reality, and it created undesirable outputs. For example, prioritizing people under 25 and over 65 makes sense if you think about who’s most at risk. However, if you think about who is on the front lines, it’s workers between 25 and 65.”
“The algorithm was rushed out and was not properly tested. Only 7 out of 1300 physicians who were working the front lines were scheduled in the first round of vaccines. If that algorithm had been more sanity-checked, somebody would have noted more than 7 of the frontline workers should receive the vaccine.”
“While I do believe that there’s potential to use algorithms for decisions like this in the future. I think that you would have to start developing now, and not wait until a scenario arises to try and solve it specifically.”
On Common Ways to Fail a Product
According to Harvard Business School professor Clayton Christensen, there are over 30,000 new products introduced every year, and 95 percent fail. To develop and launch a successful product requires innovation, creativity, strategy, and luck. Product development requires you to have the ability to see from multiple perspectives. And product mistakes can be made when product managers and leaders are unable to juggle the variables and objectives. Here, Maddy walks us through 3 product launches and failures and outlines what went wrong. Check out what Maddy had to say about these common product mistakes:
“Quibi, [a video streaming app,] was too late to the market markets already saturated with short-term video. And it wasn’t unique enough, there are already plenty of places to go and watch videos.”
“CNN+, [a premium CNN news plan,] folded after less than 180 days. The offering was too duplicative. Why would I pay extra to see more of CNN when I can just leave CNN running in my living room all day? Especially considering that the price point was quite high.”
“Google Glass, [a wearable computer,] was way too early to market. It was a bad-looking piece of hardware and was quite faulty. It generated a range of privacy concerns that the market, at the time, was way too immature to handle. There was no conversation about privacy and computer vision, facial recognition, or anything like that. And there just wasn’t a compelling sort of ecosystem of situations in which to use these glasses.”
About the speaker
Madeleine Want is currently VP of Data at Fanatics Betting & Gaming. She was previously a product manager in various roles across media, entertainment, ad tech, and e-commerce.