Why kids know less these days than their parents did

Internet means distributed knowledge.

Just as companies no longer store all their customer, order, sales, billing, product, etc. information in one database (but rather split it up over many with pointers to allow table joins), nowadays people don’t need to store all information inside their heads. It’s inefficient. Rather, they store pointers to the information. The internet is the latest and greatest extension of this heuristic.

Take couples, for example. It has been shown that couples will naturally share the information-bearing responsibility across partners since it is more efficient for the woman to remember all say work related information, while the husband may store all the child-care related information. Instead of storing two sets of information duplicatively, couples will naturally split the responsibility and know to call the other if they need to draw on information outside of their own local database (the pointer).

Such is with the internet, or really any external body of knowledge. Before, it used to be about books and libraries and knowing which book to look in for information. Now it’s merely knowing how to query that information from the internet, since the largest library that ever existed is literally at our fingertips.

In the future, classes will be less focused on teaching material, and the best students will be masters not in storing and retaining information, but in querying and applying it.

So, yes, kids these days are storing less information inside their heads than their parents did. Instead of a vast encyclopedia of knowledge, they store a small subset of that knowledge along with pointers to where their various gaps can be accessed. It is up to us to ensure our educational systems can adapt and teach our children more on how to apply information and less about retaining it.

When will channel debundling happen?

Answer: When Disney has accumulated enough direct subscriber relationships to cut out the CableCos and go direct to consumer.

Nielsen Channel Availability

Ever wondered why the cheapest cable package with ESPN starts at $80/mo and includes 100+ channels, most of which you don’t watch? (Note: This Nielsen study suggested the average household watches just 16 or 13% of the total average 130 channels available)

It’s because of competitive feature bloat, contractual obligations, and the inconvenient fact that if most users were given the choice of what they’d pay for, they’d drop 7/8 channels immediately.  The key to this puzzle is that the channels themselves are often owned by large conglomerates.  So to offer ESPN, you not only have to pay $5 per month for ESPN, but another $5 per month for ESPN2, ESPN3, ESPN Classic, ESPN U, ESPNews, oh, and by the way, all of the Disney channels too, since Disney owns ESPN.  Add it up and that’s about $15 right there, just to offer any single one of those channels.  Then consider there are another 100 channels that the average user has access to and you can see why it all adds up to a hefty bill.  Typically CableCos* end up paying $50-60 per user per month just in content costs to be able to offer the channels.

 

Example Viacom channels

 

So what’s been happening? A few things:

  1. Consumers beginning to get used to consuming content OTT (OTT= Over the Top) and pay for it with services like Netflix/Hulu/Online streaming (still <5% of the US that have cancelled their subscriptions instead of supplementing)
  2. CableCos trying to satisfy trends in multi-device viewing and increasing devices/user by TV Everywhere initiatives. They are not tech companies (in the modern sense) but now find themselves in the business of online streaming content (or having someone do it for them)
  3. High demand and premium channels have begun to allow CableCo subscribers to create an account and get free digital access to their content on whatever device they wish

 

What’s interesting is that the “help” the content owners like Disney are providing to the CableCos by creating their own streaming services that CableCo subs can plug into and access, is the same tool that will bring about the CableCos’ eventual downfall (at least from the TV business).

See by “helping” the CableCos to offer content as “TV Everywhere”, big content conglomerates like Disney and Viacom (owns Nickelodeon) are slowly but surely obtaining direct digital relationships with the end user, the cable TV sub.  Previously, the CableCos were the distribution channel for the content owners to reach end users, and that model stood for 50 years. But the internet is the new distribution channel, and as soon as the content owners can ensure they can reach the same number of subscribers directly, they would be irrational not to cut out the middleman.**  When this happens, all hell will break loose for the CableCos, since their TV model is based on bundling together a bunch of stuff that no one wants with some stuff that everybody wants.  When the stuff that everybody wants comes out, all of a sudden you’re going to need to:

A) create something new that everyone wants and craft your bundles around it

This could theoretically happen, though a more viable alternative would be to acquire several key networks to prevent this.  Still, for many consumers, ESPN, Disney, Nickelodeon, Nature/History, and a handful of other channels are real anchor channels, which are already owned by large conglomerates.

B) begin debundling by offering a la carte or “build your own bundle” options

To be fair, some CableCos have already experimented with this, but there is a reason why none are eager to push this strategy on more than 5-10% of their subscribers. When people can pay for what they want, they purchase way fewer channels.  That means less revenue for CableCos and is when the bubble they’ve been able to create by forcing people into bundles that they wouldn’t normally pay for… POPS.

Goodbye 25% of a $100B/year industry.  The end result will be a bit better for the content owners (since they don’t have to pay that pesky toll-booth fee to the CableCos), a lot better for consumers (since they are paying for what they want), and a lot worse for the CableCos.

I can’t really blame the CableCos for fighting this change, but I do think their actions will make the inevitable sting all the more when they abet the growth of content owners’ digital channels via their trendy TV Everywhere initiatives, and exacerbate the pricing bubble by continuing to force people to stomach these unnecessarily large bundles without alternatives.

PS, the “Premium” channels like HBO and Showtime are all going this way too.  What will be really interesting is to see whether the content owners attempt to begin pulling a similar play in other countries.  Since they mostly have nothing to lose (little to no subscriber base), my gut is that, YES, this is definitely in their longer term strategy.

*CableCos for the purposes of this article also include some of the traditional telco providers that have also ventured into video meaningfully like AT&T with U-Verse and Verizon with FiOS

** This of course assumes that current trends will continue and that a comparable number of people will be reachable e.g., with internet-enabled TVs, which looks to be the case based on current data and projections.

What the kale? The story (&data) behind kale’s meteoric rise

Surely you’ve noticed it…  

It’s taken over our restaurant menus, it’s in our 7/11s and Trader Joes, it’s on the Whole Foods bags, people have bumper stickers, in fact, it’s probably even in your house right now…

Kale.

Courtesy of Chef Kristen @ cookbakenibble.com

Credit to Chef Kristin @ cookbakenibble.com

What gives? Not that I have anything against you, but seriously, where did you come from?

This topic has come up several times among my friends and no one has a good answer.  Maybe we’re all just hallucinating and it’s always been around?

Negative.  2012 saw a 6x increase in articles written about kale.  This is something and it’s big (and leafy).

Kale Searches

So I thought, easy-peasy, I’m sure someone knows why kale has become so popular.  Maybe some famous chef made it trendy, or Whole Foods decided to stand behind it.  But a couple searches on “why is kale popular” (note: google autocompleted my query mid-sentence to “why is kale so popular”) brought up a ton of information on the health benefits of kale but not a single hypothesis for the cause.

So other people have noticed but no one knows why, not even Google.  Why do I see this veggie all over the place these days? Surely there’s gotta be a reason.

Challenge accepted.

Let’s get down to some numbers.

Whoever is Kale's publicist should be given a promotion

So looking at some search trends, I noticed that searches for healthy foods have risen pretty steadily across the board for a few years. (On a side note, searches for healthy foods double between December and January every year like clockwork.  Gotta love all those new years’ resolutions). Compared against Spinach, we see Kale’s meteoric rise from 1/6 that of spinach to nearly par, or almost 5x over the past several years.  In particular, in the four year period starting in 2010, Kale more than tripled in popularity, twice its increase during the four year period ending in 2009. If this were the same trend, I would have expected it to begin to level off as Kale reached parity with Spinach, as the headroom for maximum share of the leafy green market is limited and begins to decrease the more Kale rises.  But this was not the case.  Kale not only kept increasing but increased at an increasing rate.  And I believe this occurred during the end of 2009 or beginning of 2010.

OK, but WHY?

Good question, and mine too. Let’s look at the related hot search terms over time in descending order of popularity.

KaleSearchTerms

In descending order of popularity by year

Well that’s odd.. starting in 2009, kale chips came out of nowhere and quickly rose to the top of the list by 2011, even exceeding all other search terms for kale. This may be no surprise for those of you that love these things, but I had no idea (plus the ones I’ve had before tasted pretty disgusting).

Remember when these started showing up on supermarket shelves?

Interestingly, the chips only came on to the scene in 2009, then in January 2010, immediately TRIPLED in popularity, then in January 2011, TRIPLED in popularity again, and in 2012 TRIPLED yet a third time.

Note the annual uptick each January

Note the annual uptick each January

So there we have it… kale grew from neglected stew vegetable to big hitter in just a few years.  This growth was beyond the normal growth in healthy eating. There was something else.. kale chips, which shifted this growth into overdrive mode, causing hippies and rednecks alike to incorporate kale as a daily staple in restaurants, supermarkets, and dinner tables everywhere, from Vermont to Alaska (with the exception of South Dakota, which either has no idea what the big deal is or there weren’t enough people there between 2004-2013 for Google to have any data).

Final Assessment: The rise of kale was fueled in part by a trend toward healthier eating habits, then driven through the roof by the advent of the healthy and widely available snack food alternative- kale chips, which arrived on the scene in mid-2009.  A perfect storm, if you will.

Going Forward: I also predict that kale will continue to heat up but loose steam as the 2013 growth rate was far lower than the historical 300% between December ’12 and January ’13. There is only so much leafy vegetation that people will eat, trends only last so long (although I do love all these kale and apple salads I’ve been seeing), and kale has already attained near parity with spinach. Oh, and for 2014, my money’s on Quinoa.

 

Snacky + Healthy = Winner

Snacky + Healthy = Winner

PS I enjoy the intellectual challenge of this stuff.  If you have any other questions that you can’t find answers for, tweet at me and I may be intrigued.

4 reasons why smartphones will kill off fitbit and nike+

Over the last two years, interest in Fitbit has tripled EACH YEAR.

Google Trends

 

On other fronts, Nike+ marketing and sales have been robust, while Jawbone had strong initial traction with Up and has recently released a revamped version with a much improved interface.

Also from a UX perspective, the Nike+ interface is quite simply one of the most brilliantly executed interfaces for displaying complex data that I’ve ever seen.

So why do I predict that these will be rendered obsolete by smartphones?

Simple.  They can’t compete.  Here’s why:

1) Always on, charged, and carried.

I sometimes get out of the shower and forget to put on my nike+ fuel band or leave my fitbit on my other pair of jeans.  Sometimes I forget to charge these devices for a few days or misplace the charger/docking station.  One day (or even gym trip) of lost data means a ruined streak.  But I charge my cell phone every single day (sometimes multiple times) and never leave the house without it.

2) Shorter upgrade cycles.

Upgrade cycles for smartphones are on average much shorter than static devices.  With a device cost subsidized by wireless operators in the US (and several other countries), consumers can afford to replace a more complex device more frequently.

3) More advanced tech.

Because of their multi-purpose nature and subsidized device cost, our smartphones are increasingly packing more advanced technology.  Coupled with a shorter upgrade cycle, our smartphones will, on average, be technologically more advanced than our fitness devices

4) Thriving, open software ecosystem, AKA, “There’s an app for that”

What smartphones can’t do today, they will do tomorrow.  Search for sleeping apps on the iphone and you will find 15 apps all purporting to track sleep via the built-in iPhone accelerometer.  Google Now can differentiate between your runs and your bikerides using GPS and wifi triangulation data… and that’s only the beginning.  There will increasingly be services to help us track the data that we are generating ambiently and additionally services to help us tie it together (e.g., IFTTT, zapier).  Devices like fitbit and nike+ are generally closed ecosystems that resist attempts to take the data off their respective platforms.

Combined with slower tech, replacement cycle, and non-essentiality vs. smartphones, I worry that this industry may be relegated to the “temporarily supplemental” category of fitness tracking devices over the next 5 years.

Going from 0-60 in Big Data

Why we need to bring back the every-day driver when it comes to analytics…

After spending the last decade or so figuring out how to track and store a ton of data, now companies are increasingly asking, “ok, so now what?”

What can we do with our data, now that we have it?

It’s hard to go to a conference these days without hearing the term “big data” bandied about, or browse a job site without seeing a post for a company desperately looking for a “data scientist”, the proverbial gatekeeper to this big data.

Google Trends - Data Scientist

 

I love data, big and small.  I love listening to it for clues about how the world works and uncovering patterns hidden in the noise to help me validate things I knew or discover things I didn’t know I didn’t know.

While I am excited whenever we data geeks get more street cred in the business world (or politics or sports if those are your things), I am also a little bit worried about the sudden shift we’ve seen recently for companies suddenly attempting to go straight from a beat up ’89 Chevy Blazer approach to a 2013 Ferrari 458 with no in-between.

 

Why a Ferrari makes a poor daily driver

NOT a good daily driver

For one, it takes driving skills, training, and practice to go from driving a clunker SUV to a performance sports car, and this doesn’t happen overnight.  But even once you’ve adjusted to the changes in style and performance, you are still faced with the reality that the Ferrari is designed for the race track and not your daily commute.  It gets poor mileage.  It is expensive to maintain.  It performs poorly in adverse conditions.  It is so powerful that it literally tempts you at every turn to cut corners (too close) and fly way too fast for most roads.

But in big data science, there are no police to keep you in check.  And traffic congestion can easily be overcome just by throwing more cloud resources at the problem (instead of improving your traffic-skirting technique and finesse).  So it’s a slippery slope- one that many companies fall prey to.  Crashes do happen, and easily, though you often don’t notice them until much after the fact. 

Lately, we’ve seen a new crop of analytics services, powering everything from straight analytics as a service, analytics api aggregation as a service, text analysis as a service, and data science as a service.  These services are really neat and solve some important use cases.  But they also present a double-edged sword…

More often than not, the folks with the technical expertise to build and maintain these systems (the engineers) are very different from those doing the analyses (the statisticians or data scientists), and still different from those that should be shaping the analyses and distilling insights from them (the business leaders).  

The question is, who is driving?  

If it’s your expensive new data scientist, you may very well be answering specific questions really well.  Just as a muscle car does a really great quarter mile.  But you also may be ignoring the big business questions that really matter, which could be answered with a dramatically simpler approach, say in Excel.  

When it’s snowing, a 10 second quarter mile rear-wheel drive Ferrari doesn’t really matter.  Give me something with 4 seats and all wheel drive for my daily commute.  It’s not as fast or flashy, but it works for 95% of my use cases.  For the other 5%, there’s always the track…  

What makes google successful?

One of the largest and most successful companies ever to exist.  And also one of the most quirky.  What makes Google so different from other tech companies?

Here’s one: They use data to validate what they know.

What?

Sometimes testing what you know can be more important than what you don’t know. Once upon a time, Google set out to define the ideal characteristics of a boss.  They’d done some research and realized that the bosses that performed the best were not only more productive themselves, but made their team more productive…  by more than TWO TIMES vs. lesser managers, in fact!  These bosses were, in effect, multipliers, increasing the productivity of their whole team by virtue of managerial adeptness.

Now when Google began this research, they weren’t looking to upset the whole management industry. On the other hand, they weren’t averse to it. But they wanted to test it anyway.

Do you know what the ideal qualities of the super-boss were?

1) Have a clear vision and strategy for the team
2) Be consistent
3) Help your team with career development
4) Be productive and results oriented

Obvious? Yes.

But you know what happened?

In validating what they thought they knew, Google also ruled out the other hypotheses they’d come up with, like “you need to be technical” or “you need to have an Einstein IQ” that were really distractions.

The power of so deeply embedding data into the very fabric of your company is that you are not afraid to ask the obvious questions.  Sometimes you will get obvious answers, and sometimes you will find the unexpected.  But validating what you think you know can allow you to eliminate distractions so that you can focus on what matters.

In Google’s case, they were able to focus on these findings to achieve a statistically significant improvement for 75% of their lower performing managers.  And when you consider a bad manager can actually diminish the productivity of his team by 50%, this means a dramatic improvement in the overall organization.

Not bad for something everyone took for granted already.

Why Netflix’s $100m content investment wasn’t optional

Or: The Story of the Gift that Keeps on Giving

At the beginning of the month, the Atlantic came out with an informative article discussing the economics of Netflix’s $100 Million new show.  It is a good write-up and I suggest a read.

To paraphrase, they cited:

  1.  $100 million really ain’t that much when you’ve got a user base of 33 million subs
  2. HBO can do it and they only get $7 per sub (and this number is actually far higher than most other “premium” networks despite similarly expensive content)
  3. It saves Netflix from having to repurchase from content owners each year
  4. Exclusive content is an excellent acquisition and retention tactic

gotta catch 'em all...

 

Although they skirt the topic in #3, the heart of the matter is that:

Netflix must begin to produce and control its own content.  

Period.

At nearly any cost this is a no-brainer.

Imagine fighting a war where your enemy and your arms dealer happen to be twin brothers.  Now imagine if you start winning the war, what happens? Prices go up.  And up.  And up.  Until either you stop winning or you bankrupt yourself.  This is exactly the situation in which Netflix found itself about a year and a half ago. The problem is that ancient alliances take time to dissolve. And in this game, there are losers – namely, the cable companies, which are sitting nervously on a $100B/year industry in the US.

But to Netflix, $100B is peanuts… only the beginning… since the total addressable market throughout the rest of the world is many times that.

And in the old paradigm, Netflix had to negotiate rights to stream every show with every owner in every corner of the world in which they wanted to offer it.  And renegotiate when the contract expires.  Which holds Netflix hostage to whatever “new and improved” prices the owners choose to impose (c.f., the Starz debacle). And let’s be clear- there is little that users hate more, by the way, than selling them one thing and then changing the terms on them.

Having worked with practically every large cable and media conglomerate, I say, way to go Netflix! Controlling the streaming rights to your content is absolutely critical to effectively enter new international markets without delays for negotiations and pricing squabbles (which, by the way, is a favorite pastime of the cable industry). This content is furthermore a fixed cost renewable revenue stream (think amazon, itunes, potentially even licensing to other networks outside the US).  And last, and not least, it sets a valuable precedent for the content owners and producers to think twice next time they jack the prices up.

Owners better play nice, or in a few years, you may find yourself licensing your best content from Netflix.  And it won’t be pretty.

All your video would belong to Netflix…

Fun with dating and network matching models

 

Recently, I got into a discussion via twitter with Ryan Hoover on the key metric for success for @TinderApp, a new entrant in the game of matching romance seekers via online social networks.  What got me started was looking at the following equation as an attempt to create the “one metric that matters” (his post here is generally very insightful aside from my one bone to pick):

The number of people connected driven by:
(# of profiles viewed) x (# of profiles liked) x (% of mutual likes)

This didn’t seem quite right to me, so I naturally started to draw. Here is what I came up with:

And what lovely rounded corners!

What a conveniently sized piece of scrap paper…

What this means is that, following Acquisition and Activation (topics for another post), Retention is defined basically as whether you can get them to stay long enough to generate a match or reward.

The time it takes to find a match is directly proportional to:

  1. The score distribution of each population (e.g., what % of men are perceived as very attractive by the opposite sex [hint: it’s 40%*])
  2. The acceptability threshold (what is the typical level of attractiveness needed for someone of the opposite sex to rate a “match” [note it would be fascinating to see how this differs between TinderApp and Bang With Friends]), and…
  3. The average # of views you are able to get out of a user

Sadly, only the last item is controllable by the application, so if we were to choose, it is this statistic that would get the “ONE METRIC THAT MATTERS (TM)” badge.  That and whether people are actually hooking up, I guess.  But that’s determined outside of the app.  

… for now… 

*Source: Hot or Not.  Seriously.  Some guys from Dartmouth wrote a paper on the score distributions a while back which I found fascinating.  Check it out here.

Why long-tail services will usher the next generation of big data

(Repost from an entry I wrote for tenXer here)

First there was data
Then there was big data
And now is connected big data

aka Big Data Triangulation

In a recent quantcast whitepaper on online advertising, the author discusses RTB (real-time bidding) and mentions the advantages of using an integrated, algorithmically optimized strategy vs. a mix-and-match approach, where “integrated data, algorithms and bidding produced a two to seven times lower cost per action (CPA) than the independent approach” (1).

Let me repeat: “integrated data, algorithms and bidding produced a two to seven times lower cost per action (CPA) than the independent approach”.

2-7X is a lot. This got me thinking about data. Specifically how “big data” might not always be enough on its own.

Working with dozens of fortune 500 companies, I’d always wondered at the petabytes of accumulated data that were sitting, untouched in various organizational siloes. Sooner or later, some analytics guru or consultant would come in, whip up an analysis, and unlock value from the dataset, but having once been that person, I believe that was only 80% (at best) of the “true” value of the data. The problem these organizations face is that their data, though big, is still siloed. Siloed across divisions and almost always siloed within the organization. Yet what would happen if you connected these siloes and started performing the same empirical analyses on them, but in concert? A 2-7x improvement perhaps, as with Quantcast?

Target's 'Pregnancy test'

Of course online marketers would be among the first to figure this out and embrace the principle. After all, improvements in their trade directly improve their companies’ (or clients’) bottom lines. Another industry that has already embraced connected big data: finance. We need look no further than the August 2 $440 million trading glitch to realize that high frequency trading has quickly become nearly autonomously algorithmic (save for the “off” button) and incorporates a mind-numbingly large plethora of both external (non-finance) and internal inputs.

So what? Isn’t that what big data is about anyway?

Well, yes and no. We are definitely in the era of big data. Thousands of companies now collect many terabytes worth of customer, transaction, usage, research, product, competitive, etc. data. But only a few have begun to connect that data and do things with it. The few that are (we’ve seen it first in finance, advertising, e-commerce, and, most recently-elections) are using connected big data (CBD) to sell better. But almost none are using CBD to operate better. Mainly because a) it’s really hard to pull a bunch of huge data sources together gracefully, and b) there’s not the same clear ROI for doing so as with sales functions. This, however, is changing. With the rise of long-tail internet connecting services like ZapierIFTTTElectric ImpWork.comFacebook Open Graph or white-label service providers like RapleafFactualSemantria, etc. – the barriers to pulling together disparate data sources are evaporating. Imagine being able to predict and prevent inventory shrinkage, anticipate positive or negative customer sentiment, identify your next blockbuster product before it’s built, or figure out who are your most productive employees? While perhaps a bit pie-in-the-sky for now, in the world of connected big data, these are just table stakes.

From Paul Graham on ideas he’d like to fund: “Now that so much happens on computers connected to networks, it’s possible to measure things we may not have realized we could. And there are some big problems that may be soluble if we can measure more.”

Remember: A dot in one dimension may appear identical to an oncoming locomotive. Embracing big data triangulation will help you stay on the train and not in front.

-Dan @ tenXer
@stonemit

About the author: Dan is currently Mad Scientist and Strategist at tenXer, and an expert in residence at General Assembly. Prior to tenXer, he led teams advising clients at fortune 500 companies in the media and communications industries on how to leverage internal and external data sources to make informed, high-impact decisions for their businesses. He is well versed with a variety of advanced analytical techniques including data mining, cluster analysis, bootstrap forests, network routing and optimization, machine learning and automation, sentiment analysis, marketing impact allocation and optimization, and data de-siloing.

(1): “PROMISE UNFULFILLED? LESSONS FROM THE REVOLUTION: Six Things You Need to Know About Real-time Display Advertising”. Quantcast, 2012.

The Phantom 5-Day Workweek

(repost from an entry I wrote for tenXer here)

A story of using data to validate user feedback and make quick, informed decisions

tenXer is, at its core, a data-driven company.  We believe that access to the right data can help us be better.  But sometimes even the best data can be rendered meaningless when displayed in the wrong way.

tufte-worst-chart-ever-1-vdqi

Any users who’ve been with us since launch have witnessed a few different iterations of our visualizations.  While this is partially due to us building our own charting libraries, at the core of these iterations is the extreme challenge faced by our company – how to display useful information that extends across many use cases.

To exclude or not to exclude?

It is on this topic that a question recently came up based on a user’s suggestion: “Allow user to exclude weekends.”  Essentially, our user wanted us to eliminate Saturday and Sunday from his weekly charts so only activity between Monday and Friday was measured.

This was an interesting idea, and one we’d brought up before in passing, though the team was divided on it.  Some use tenXer only M-F for more work-related stats, say for something like increasing GitHub commits, while others, like myself, use tenXer both for work and non-work related metrics, like tracking progress toward “Inbox 0″ in Gmail, which take place both during weekdays and weekends.

Unlike mature companies whose biggest threat to survival may be something like starving in the wilderness, the biggest threat for a startup may be more akin to drowning in an ocean of possibilities.  Even decisions seemingly as trivial as the number of days in a week or how many emoticons to give users (E.g., Path) can give rise to heated debates lasting days or weeks.  In our case, as a proof case for how to short-circuit this pattern, we decided to turn to the data.  Although we expected services like Gmail and Twitter to be used evenly throughout the week, our more developer-centric services like Github and Pivotal Tracker  seem to be where the M-F preference typically originates.  So, without further ado, our quick and dirty analysis:

5DayWorkWeek

Conclusion: Keep the 7-day view

It turns out that about 40% of our user base performs the equivalent of at least one day’s work during the weekend, while about 25% of our users accomplish as much during a weekend day as during a weekday.  Although a portion of these users are open source and may be committing extracurricularly (as well as a few other caveats mentioned in the chart), Github is currently our most-connected service, so, based on the data, we decided to keep the 7 day workweek.

Key Question: How many of your users would likely be adversely affected by making this change?

Although this is by no means an “ideal” analysis and could be improved, it did a reasonable job at answering the key question of “how many of our users would likely be adversely affected by making this change?”  Note that we didn’t look at overall averages or medians across all users, since that approach tends to miss pockets of distinct types of users.  Instead we asked what proportion of users fall into each behavioral bucket and ended up finding a segment of about 15% of users that actually get more done during the weekends than during weekdays! (Possibly open source contributors?)

Of course, longer term, an option for the user to choose 5-day or 7-day is probably best, but part of the challenge of working in a startup is that there are a million things you’d like to do and only a few that you can. Using data when and wherever possible to help make objective decisions is not only better for your company, but for your team’s sanity as well.

-Dan @ tenXer