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Quick update: Quoted in WSJ on dating apps, recent podcast interview, plus recent essays

Screenshot 2015-06-10 18.13.50

Couple quick things that I wanted to batch up in a single post.

A quote from me in the Wall Street Journal today
First, there’s a quote from me in the Wall Street Journal today, for an article covering the opportunities/challenges of dating apps. I’ve been told the story will be on page B1 of the paper edition, but here’s the link for everyone who loves trees: The Dating Business: Love on the Rocks by Georgia Wells. The quote was pulled from a recent essay of mine on why investors are often skeptical of dating startups, which you can read: Why investors don’t fund dating.

Podcast interview with Codenewbies
Last week, I did a fun, casual interview with Codenewbies, a podcast targeted at people learning to code. In the interview, I talk about how taking a year of Computer Science in college, plus internships as a Software Engineer, helped me break into my first post-college job, at venture capital Mohr Davidow Ventures. And I also have a short story about the first real program I ever wrote, in GW-BASIC back in fifth grade, where I managed to blow up one of the Mac Plus computers in class.

Let’s meet up in person (San Francisco)
I’m kicking off a series of small-group gatherings to grab drinks/food in SF – something like ten people, in SOMA. If you’re based in the area, I’d love to catch up and meet. I plan to include friends/guests from top startups and tech companies in the Bay Area to join us- here’s how to register.

Follow me on Twitter
Just a reminder- if you’re not already following me, here I am: @andrewchen

Recent essays, if you missed them
Finally, I wanted to include a list of some of my recent essays in case you missed them. I’m pretty happy with how the last couple have turned out, so I hope you enjoy.

Thanks for reading!

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New data shows losing 80% of mobile users is normal, and why the best apps do better

Exclusive data on retention curves for mobile apps
In a recent essay covering the Next Feature Fallacy, I explained why shipping “just one more feature” doesn’t fix your product. The root cause is that the average app has pretty bad retention metrics. Today, I’m excited to share some real numbers on mobile retention. I’ve worked with mobile intelligence startup Quettra and it’s founder/CEO Ankit Jain (formerly head of search+discovery for Google Play) to put together some exclusive data/graphs on retention rates** based on anonymized datapoints from over 125M mobile phones.

Average retention for Google Play apps
The first graph shows a retention curve: The number of days that have passed since the initial install, and what % of those users are active on that particular day. As my readers know, this is often used in a sentence like “the D7 retention is 40%” meaning that seven days after the initial install, 40% of those users was active on that specific day.

The graph is pretty amazing to see:

retention_graph_average

Based on Quettra’s data, we can see that the average app loses 77% of its DAUs within the first 3 days after the install. Within 30 days, it’s lost 90% of DAUs. Within 90 days, it’s over 95%. Stunning. The other way to say this is that the average app mostly loses its entire userbase within a few months, which is why of the >1.5 million apps in the Google Play store, only a few thousand sustain meaningful traffic. (*Tabular data in the footnotes if you’re interested)

Ankit Jain, who collaborated with me on this essay, commented on this trend:

Users try out a lot of apps but decide which ones they want to ‘stop using’ within the first 3-7 days. For ‘decent’ apps, the majority of users retained for 7 days stick around much longer. The key to success is to get the users hooked during that critical first 3-7 day period.

This maps to my own experience, where I see that most of the leverage in improving these retention curves happen in how the product is described, the onboarding flow, and what triggers you set up to drive ongoing retention. This work is generally focused on the first days of usage, whereas the long-term numbers are hard to budge, no matter how many reminder emails you send.

Note that when we say that these DAUs are being “lost” it doesn’t mean that users are suddenly going completely inactive – they might just be using the app once per week, or a few times per month. Different apps have different usage patterns, as I’ve written about in What factors influence DAU/MAU? with data from Flurry. Just because you lose a Daily Active User doesn’t mean that you’re losing a Monthly Active User, yet because the two correlate, you can’t sustain the latter without the former.

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How do the best apps perform? Much better.
The second graph we’ll discuss is a comparison of retention curves based on Google Play ranking. The data shows that there is a very clear and direct correlation:

android_retention
The top apps have higher D1 retention rates, and end with much stronger absolute D30 numbers. However, interestingly enough, the falloff from D1 to D30 is about the same as all the other apps. Another way to say it is that users find the top apps immediately useful, use it repeatedly in the first week, and the drop off happens at about the same speed as the average apps. Fascinating.

Bending the curve happens via activation, not notification spam
To me, this is further validation that the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit. That way, users set up themselves up for success. Although the data shown today relates to mobile apps, I’ve seen data for desktop clients and websites, and they all look the same. So whether you’re building a mobile app or something else, the same idea applies:

  • For a blogging product, you might want users to pick a theme, a name, and write their first post, to get them invested.
  • For a social service, you might want users to import their addressbook and connect to a few friends, to give them a strong feed experience and opt them into friend notifications
  • For a SaaS analytics product, you might want users to put their JS tag on their site, so that you can start collecting data for them and sending digest emails
  • For an enterprise collaboration product, you might want users to start up a new project and add a couple coworkers to get them started

Each of the scenarios above can have both a qualitative activation goal, as well as quantitive results to make sure it’s really happening. Whatever you do, sending a shitload of spammy email notifications with the subject line “We Miss You” is unlikely to bend the curve significantly.

I hate those, and you should too.

(Thanks again to Ankit Jain of Quettra for sharing this data and assisting me in developing this piece. More from them here, which examines app-by-app retention rates for messaging apps)

*Tabular data

0 1 3 7 14 30 60 90
Top 10 Apps 100 74.67 71.51 67.39 63.28 59.80 55.10 50.87
Next 50 Apps 100 64.85 60.31 54.13 49.48 44.81 39.60 34.50
Next 100 Apps 100 48.72 42.96 35.93 30.79 25.45 21.25 18.98
Next 5000 Apps 100 34.31 28.54 21.64 17.43 13.62 10.74 8.99
Average 100 29.17 23.42 17.28 13.11 9.55 6.82 3.97

 

**Methodology
Some notes on methodology below, shared by Quettra:

Quettra software, that currently resides on over 125M Android devices worldwide, collects install and usage statistics of every application present on the device. For this report, we examine five months of data starting from January 1, 2015.

Since we exclusively consider Android users in this study, we exclude Google apps (e.g. Gmail, YouTube, Maps, Hangouts, Google Play etc.) and other commonly pre-installed apps from our study to remove biases. We also only consider apps that have over 10,000 installs worldwide.

A note on privacy, which is very important to us: All data that we collected is anonymized, and no personally identifiable information is collected by any of our systems. From our understanding, this is the first time ubiquitous mobile application usage has been analyzed at such large scale. Quettra does not have a direct relationship with any of the apps or app developers mentioned in this report.

PS. Get new updates/analysis on tech and startups

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Photos of the women who programmed the ENIAC, wrote the code for Apollo 11, and designed the Mac

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Ada Lovelace, an early computing pioneer, featured prominently in Walter Isaacson’s book The Innovators

Innovation is messy, and too easy to oversimplify
After finishing Walter Isaacson’s biography of Steve Jobs, I eagerly devoured his followup book, The Innovators. This book zooms out to focus not on an individual, but on the teams of collaborators and competitors who’ve driven technology forward, and the messiness of innovation. The stars of the story turn out to be the often unappreciated women who contributed to computing at key moments, and the book fittingly begins and ends with Ada Lovelace, an 19th century mathematician who defined the first algorithm and loops. I recommend the book, and here are the NYT and NPR write-ups which you can check out as well.

Isaacson’s book resonated with me because once you know how messy the success stories are, it’s obvious why the media always chooses to simplify things down to just a few characters with simple motivations. As a result, we know what kind of shoes that Steve Jobs wears, but forget the names of the people around him who worked for years to make the products we love.

The stories from the book have been floating around in my brain for a few months now, and coincided with two other pieces that went viral on Twitter/Facebook. First, there’s been some great photos of Margaret Hamilton who led the software development for Apollo 11 mission. Second, there was a discussion on Charlie Rose on the women who worked on the original Macintosh. I did some research and put together a few photos of these key pioneers from computing history. Seeing their faces and names help make them more real, and I wanted to share the photos along with some blurbs for context.

If you have more photos to send me, just tweet them at @andrewchen and I’ll continue to update this article.

Women of ENIAC
One of the most interesting backstories in Isaacson’s book is the fact that women mostly dominated software in the early years of computing. Programming seemed close to typing or clerical work, and so it was mostly driven by women:

Bartik was one of six female mathematicians who created programs for one of the world’s first fully electronic general-purpose computers. Isaacson says the men didn’t think it was an important job.

“Men were interested in building, the hardware,” says Isaacson, “doing the circuits, figuring out the machinery. And women were very good mathematicians back then.”

In the earliest days of computing, the US Army built the ENIAC, the first electronic general purpose computer in 1946. And women programmed it – but not the way we do now – it was driven by connecting electrical wires and using punch cards for data.

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Left: Patsy Simmers, holding ENIAC board Next: Mrs. Gail Taylor, holding EDVAC board Next: Mrs. Milly Beck, holding ORDVAC board Right: Mrs. Norma Stec, holding BRLESC I board.

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Left: Betty Jennings. Right: Frances Bilas

ca. 1940s --- Computer operators program ENIAC, the first electronic digital computer, by plugging and unplugging cables and adjusting switches. | Location: Mid-Atlantic USA.  --- Image by © CORBIS
Jean Jennings (left), Marlyn Wescoff (center), and Ruth Lichterman program ENIAC at the University of Pennsylvania, circa 1946.

More photos here.

Margaret Hamilton and Apollo 11
Twitter has been circulating this amazing photo of Margaret Hamilton and printouts of the Apollo Guidance Computer source code. This is the code that was used in the Apollo 11 mission, you know, the one that took humankind to the moon. This is how Margaret describes it:

In this picture, I am standing next to listings of the actual Apollo Guidance Computer (AGC) source code. To clarify, there are no other kinds of printouts, like debugging printouts, or logs, or what have you, in the picture.

There are some nice articles about this photo on both Vox and Medium, which are worth reading.

Here it is, along with a few other photos of her during this time:

Margaret_Hamilton

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Margaret_Hamilton_in_action.0.0

A side note to this that I found pretty nerdtastic is that the the guidance computer used something called “core rope memory” which was weaved together by an army of “little old ladies” in order to resist the harsh environment of space.

To resist the harsh rigors of space, NASA used something called core rope memory in the Apollo and Gemini missions of the 1960s and 70s. The memory consisted of ferrite cores connected together by wire. The cores were used as transformers, and acted as either a binary one or zero. The software was created by weaving together sequences of one and zero cores by hand. According to the documentary Moon Machines, engineers at the time nicknamed it LOL memory, an acronym for “little old lady,” after the women on the factory floor that wove the memory together.

Here’s what it looked like:

Screenshot 2015-06-03 21.21.02

Susan Kare, Joanna Hoffman and the Mac
Megan Smith, the new chief technology officer of the United States, was on Charlie Rose recently and referenced the fact that the women who worked on the Macintosh were unfairly written out of the Steve Jobs movie – you can see the video excerpt of her speaking about it here. I tracked down the photo she was referring to, and wanted to share it below.

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Macintosh team members: Row 1 (top): Rony Sebok, Susan Kare; Row 2: Andy Hertzfeld, Bill Atkinson, Owen Densmore; Row 3: Jerome Coonen, Bruce Horn, Steve Capps, Larry Kenyon; Row 4: Donn Denman, Tracy Kenyon, Patti Kenyon

On the top of the pyramid photo is Susan Kare, who was a designer on the original Mac and did all the typefaces and icons. Some of the most famous visuals, such as the happy mac, watch, etc., are all from her.

01_macicons
One cool thing to add to your office: Some signed/numbered prints of Susan’s most famous work. I have a couple – here’s the link to get your own.

Here’s a May 2014 talk. Susan Kare, Iconographer (EG8) from EG Conference on Vimeo.

Another key member of the original Mac team, Joanna Hoffman, isn’t in the pyramid photo but I was able to find a video of her talking about the Mac on YouTube, and embedded it below. Here’s the link if you can’t see the embed. She wasn’t in the Steve Jobs movie from Ashton Kutcher, but it looks like she will be played by Kate Winslet in the new Aaron Sorkin film coming out.

Here’s the video:

 

Bonus graph: Women majoring in Computer Science
Hope you enjoyed the photos. If you have more links/photos to include, please send them to me at @andrewchen.

As a final note, one of the most surprising facts from Isaacson’s book is that early computing had a high level of participation from women, but dropped off over time. I was curious when/why this happened, and later found an interesting article from NPR which includes a graph visualizing the % of computer science majors who are women over the last few decades.

The graph below is from the NPR article called When Women Stopped Coding – it’s worth reading. It speculates that women stopped majoring in Computer Science around the time that computers hit the home, in the early 80s. That’s when male college students often showed up with years of experience working with computers, and intro classes came with much higher expectations on experience with computers.

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PS. Get new updates/analysis on tech and startups

I write a high-quality, weekly newsletter covering what's happening in Silicon Valley, focused on startups, marketing, and mobile.

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