Evidently there are plenty of hooligans in my neighborhood looking for an excuse to start drinking and yelling at a TV around noon in my favorite pub. This was a little surprising to me, since I live in a yuppy downtown Seattle neighborhood which is full of software geeks and otherwise respectable people.
Now that it’s all over with, I decided to see if there was a broader trend in RescueTime’s data. Time spent on the computer dropped about 4% and productive time dropped a full 10% here in the US on the day of our first game vs England. More people than usual checked the news, which managed to grab a 5% bump despite the drop in total time. Evidently no one was watching the game on their computers, since Entertainment (including sports) stayed flat.
The effect was even more pronounced in the UK. Productive time dropped 13%, total time dropped 7%, and instead of reading about the upset in the news like their American counter parts, the English were apparently watching it live with an 5% bump in Entertainment.
All that’s interesting, but that game took place on a Saturday, when most people aren’t supposed to be working anyway. When the US squeaked out a tie during the final minutes of their next match against Slovenia on Friday, our American users spent a little more time than normal on the news, but it wasn’t enough to cause a significant change in productivity.
Here is a graph of all the days of the World Cup, compared to a typical week* to help see if there was real trend here.
It’s obvious that productive time was consistently down during the entire World Cup. The US’s game dates are circled in red. It’s interesting that you can see after we were eliminated by Ghana, things picked up a bit, but still didn’t quite make it back to normal. This might be because we have more international users than members in the US. Total time spent on computers was down 4% and productive time was down 3% over all the working days in the tournament.
There are a couple other interesting points in that graph, particularly the 18% drops in productivity over Fathers Day and Fourth of July weekend. People seemed to come back pretty slowly after the 4th, and didn’t manage to get back into full swing until the end of the week.
When you look at it from RescueTime’s perspective, it’s pretty clear that the world cup does matter.
*A typical week is the average from the 28 days before the World Cup began (Memorial Day was tossed out).
RescueTime provides a time management tool to allow individuals and businesses to track their time and attention to see where their days go (and to help them get more productive!). We have hundreds of millions of man hours of second-by-second attention data from hundreds of thousands of users around the world, tracking in real time both inside and outside the browser.
Microsoft just launched Office 2010 to great fanfare, and quietly slipped in a new free online version. It looks like they may have finally realized that if they don’t cannibalize their core business with a web based offering, Google will. Has the sleeping giant over in Redmond finally awoken, and can they defend their biggest cash cow from the future?
Some analysts say Google’s online offering can’t compete with Microsoft’s. They have no idea.
We’ve been tracking the usage habits of hundreds of thousands of our users over the last two years, and you can clearly see that Google has managed to increase their daily reach from around 59% to 79%. On the other hand, Microsoft Office has been steadily shedding users, losing about 9% of our population.
To get an idea of how relatively important each application in these suites are, here is a graph showing the full gamut.
Communication makes up about 18% of all computer usage. Google proved you could do email in the cloud not only competitively, but for free. Outlook and Gmail dominate these two companies’ suites in terms of unique daily users. Gmail managed to increase their slice of the pie about 3%, while Outlook lost about 6% of the total. That’s a 21% relative decline for Microsoft vs 7% relative growth for Google in arguably the single most important software sector. Microsoft loses its integration advantage when people stop using big pieces of the suite, which may help explain the synergistic decline of Outlook and Excel. It’s also interesting to note that Word and PowerPoint have been relegated to a tiny fraction of our users who seem to greatly prefer Google Docs.
If that was the whole story, things might look pretty grim for Redmond, and it’s no wonder they’re being forced to respond to web based offerings. However, there is at least one more way to consider the data, and that’s in terms of the amount of time spent in particular applications, not just the number of people using them.
It’s clear again that email is the most important component in both companies portfolios, but even though Gmail has about double the users, the smaller population of Outlook users spend more time emailing than all the Gmail users put together. Today, Outlook is the preferred weapon of choice for heavy users, but if I were an exec at Microsoft, I’d be paying very close attention to the direction those blue and red lines move from here on out. You might also notice that in terms of spreadsheet usage, there is really only 1 option.
About the data:
RescueTime provides a time management tool to allow individuals and businesses to track their time and attention to see where their days go (and to help them get more productive!). We have hundreds of millions of man hours of second-by-second attention data from hundreds of thousands of users around the world, tracking in real time both inside and outside the browser. We selected annual date boundaries for this set, to help reveal seasonal variations in usage, like the holiday dip in productivity.
About our software:
If you want to see how productive you are vs the rest of our users, you should check out our product tour. We offer both individual and group plans (pricing starts at FREE).
When Google launched its Pac-Man logo on Friday, we immediately heard amused groans in our tweet-streams. “Well, so much for my morning,” said one. “Google’s Pac Man logo just ruined millions of dollars in productivity today, nationwide,” said another.
Here’s what we all saw on Friday:
Here are two of the tweets we saw in response:
Given our repository of hundreds of millions of man hours of second by second attention data, we figured there’s no one better than RescueTime to tell the world about the cost of Google Pac-Man on that fateful Friday. Here’s what we learned.
The first thing to understand is that Google does not result in a lot of active usage, in terms of time. Yes, we all use Google. But a Google search only requires a few seconds, and we’re all pretty well trained to click one of the first few links. Add to that the fact that many people use Google as a navigation tool (“Googling “IBM” instead of typing in “www.ibm.com”). Nonetheless, it might surprise you that our average Google user spends only 4 and a half active minutes on Google search per day, spread over about 22 page views. That’s roughly 11 seconds of attention invested in each Google page view. It doesn’t sound like a lot, but next time you do a search, count to 11- it’s a long time.
This weekend, we took a hard look at Pac-Man D-Day and compared it with previous Fridays (before and After Google’s recent redesign) and found some noticeable differences. We took a random subset of our users (about 11,000 people spending about 3 million seconds on Google that day) The average user spent 36 seconds MORE on Google.com on Friday.. Thankfully, Google tossed out the logo with pretty low “perceived affordance” – they put an “insert coin” button next to the search button, but I imagine most users missed that. In fact, I’d wager that 75% of the people who saw the logo had no idea that you could actually play it. Which the world should be thankful for.
If we take Wolfram Alpha at its word, Google had about 504,703,000 unique visitors on May 23. If we assume that our userbase is representative, that means:
- Google Pac-Man consumed 4,819,352 hours of time (beyond the 33.6m daily man hours of attention that Google Search gets in a given day)
- $120,483,800 is the dollar tally, If the average Google user has a COST of $25/hr (note that cost is 1.3 – 2.0 X pay rate).
- For that same cost, you could hire all 19,835 google employees, from Larry and Sergey down to their janitors, and get 6 weeks of their time. Imagine what you could build with that army of man power.
- $298,803,988 is the dollar tally if all of the Pac-Man players had an approximate cost of the average Google employee.
I hope you’ve enjoyed our Pac-Man data journey as much as we have. Next up in our on our data-hacking list, we’ll be digging in to find the laziest and most productive countries and cities in the world. Where do you think yours ranks?
About the data:
RescueTime provides a time management tool to allow individuals and businesses to track their time and attention to see where their days go (and to help them get more productive!). We have hundreds of millions of man hours of second-by-second attention data from hundreds of thousands of users around the world, tracking both inside and outside the browser. The data for this report was compiled from 11,000 randomly selected Google users.
About our software:
If you want to see how productive you are vs the rest of our users, you should check out our service. We offer both individual and group plans (pricing starts at FREE).
Since we’re a gang of egotistical guys hanging around all day, we’ve always assumed we’re the crème de la crème here at RescueTime. Turns out, we were right. Our team is regularly in the 90th percentile or higher for weekly productivity. We figure it’s because we’re productivity guys, it’s what we do. To get some answers with a little more data, and little less ego, I’ve started digging through the hundreds of millions of man hours in our database. From what I can tell, the 23rd chromosome has a pretty amazing impact on the way people use computers. Full disclosure: I happen to be a man.
The 4,000 women sampled managed to rack up an astounding 87,585 hours on social networking sites, which accounts for about 6.4% of their time. Their male counterparts, on the other hand, spend 39% less time drinking from the fire hydrant of virtual friendship. It’s not that men are less interested in being social either. In fact, in our population, more men use social networks than women (72% of men vs 69% of women). When it comes to shopping online, women spend 63% more of their time picking out their goodies than men do. Men have their distractions, too. They spend about 15% more of their time reading the news than women.
These switches can be anything from one email to the next, or to something completely… OMG, hold on a sec, Tony just posted new pics of his recent getaway… Oh, sorry, back to work.
The average guy spends pretty close to 50% of his computer time doing things he considers distracting. No wonder our information economy is being eaten piecemeal by developing countries where people still have a work ethic. Wait, what? You thought I said men work harder, but they spend half their time distracted? That’s right, women only manage to be productive with about 43% of their time.
Evidently, there’s a reason they are called “man” hours. On average, male information workers spend 14% more time per day working on their computers than women do.
About the data:
RescueTime provides a tool to allow individuals and businesses to track their time and attention to see where their days go (and to help them get more productive!). We have hundreds of millions of man hours of second-by-second attention data from hundreds of thousands of users around the world, tracking both inside and outside the browser. The data for this report was compiled from 8,000 randomly selected men and women.
About our software:
If you want to see how productive you are vs the rest of our users, you should check out our tools. Better yet, get your entire team signed up and put the rest of those slackers to shame. It’s not really that hard. Our data shows that your coworkers are probably taking it even easier than you are, since you at least made it over here to our blog.
[note: some data in this post is missing– given that we work on and troubleshoot our own software, sometimes we don’t get to log ALL of our time in a week, but it’s consistent enough that I don’t think it skews these results in a big way. We also had a vacation in each of the months in question for 1 team member]
So about a month ago, the RescueTime product team decided to experiment with working from home to see how it would effect how we spend our time. The initial plan was to run the experiment for a week, but we realized that we were paying too close attention to the affects of the experiment and would let it “bake” for a few more weeks to get some better data. The data (4 weeks of it) is in, and there are a few surprises.
The control – Team of 5 Working from Work (in the office!)
Total computer time logged: 582h 20m
Dev, Design, or writing time: 224h 20m
Communication/meetings: 225h 10m
Efficiency Score: 1.33 (RescueTime calculates this score based on the ratio of self-identified productive activities versus distracting ones)
Productive apps/sites: 504h 50m
Distracting apps/sites: 61h 15m
Neutral apps/sites: 16h 15m
The experiment – Team of 5 Working from Home
Total computer time logged: 657h 50m
Dev, Design, or writing time: 287h 20m
Communication/meetings: 223h 20m
Efficiency Score: 1.30 (RescueTime calculates this score based on the ratio of self-identified productive activities versus distracting ones)
Productive apps/sites: 543h 20m
Distracting apps/sites: 72h 28m
Neutral apps/sites: 42h 02m (much of this is Google Chrome for the Mac, which RescueTime currently doesn’t track sites for– likely split between productive and distracting)
So the ratio of activities doesn’t seem to be meaningfully different. There are less meetings (“drive by” meetings and formal ones are both tracked) but there’s a lot more IM and email. That’s not what we could’ve expected.
But what seems to be hugely different are the totals. Take out the commutes and the longer lunches, and the totals are quit different.
Here’s a chart:
It doesn’t look like much, but 5 people logged an extra 75 hours in a month, with the vast majority of those extra hours being productive development or design hours (about 63 extra dev/design hours were logged in the working from home month).
How we FELT
Obviously, working from home isn’t just about the hours logged. When talking to the team, feelings on the experiment were pretty mixed:
- Most people felt like we weren’t working as hard from home and it felt like a better work/life balance. Turns out we were working a fair bit harder, but the time reclaimed made it feel more relaxing.
- The team felt a bit less energized… The synergy that you get when people are bouncing around ideas is pretty cool– we had a bit less of that (though we had wednesday lunches that helped a bit here).
- People worked odd hours. Working from the office forces you into the 8-6 mode and makes it awkward to tune out in the afternoon if your heart just isn’t in it. Conversely, when you put in your 9+ hours at work, you’re a lot less inclined to work in the evening (even if you were spinning your wheels all day). I think it’s better to work when you feel like it than to force an artificial schedule.
- People were lonely, but dealt with it. We all joked how excited we were to see our wives when they got home. I personally made a much greater effort to be social with friends. This was a lot better than the “I just want to get home and veg out” instinct that I tend to have after a long day at work.
Working from home gives folks a lot more time in front of a computer, if that’s what they are after. With commutes, associated setup/teardown time, getting coffee from starbucks, lunches, and people dropping into the office, we’re all losing hours. To be clear, all work and no play is a bad idea… The really interesting thing about working from home is that we felt like we weren’t working as hard, but were actually logging about 22% more development and design hours.
What we’re going to do Next
A lot of us have expressed that, despite all of this, we kinda miss the office. We’re talking about next steps. I’m personally interested to try a hybrid approach.
Discussed here is a mixin that extends Rails to provide an easy method to switch your database session between clean and dirty reads, otherwise known as transaction isolation level.
( jump to the code )
At Rescuetime, our most interesting analysis of tracked time requires a lot of complicated data munging on the database side. We extensively optimize the structure of the datastore and the plan of queries to produce quick results. However, there is some, however small, amount of row scanning and index hunting that is inevitable. Some of these tables are also subject to rapid, row overlapping, simultaneous insert loads.
In general, reporting or analytical access has no worries about data being up to date to the nearest microsecond, although as near real time as possible is highly desired. This near real time goal rules out an ETL type solution. Additionally, there is the cost factor. If we can make this work on one database, why build two?
In the quest for minimal stress for the online system, we introduced a method for flagging database work to be dirty reads, thus preventing any kind of locking (especially index locking) on the rows in question, and applied these where possible. This is simple enough in straight SQL, but we wanted to expose it to Rails framework in a consistent manner.
What this code does is:
1) Provide stubs in the abstract database adapter for “dirty()”, “clean()”, “reallyclean()”
2) Implement them in the MySQL adapter
3) Expose them to ActiveRecord::Base as a class method, prefixed with “isolation_”
On #1, #2: the choice of really clean versus clean simply describes the read locking strategy used. EG if you have multiple selects in same transaction on same rows, “clean” returns same result from same snapshot. However “reallyclean” will return newer rows if they exist on the later selects.
On #3, the prefix “isolation_” is added (yielding “isolation_dirty() etc.) since there is already some semantic in place for “dirty” in ActiveRecord.
For MySQL we set:
dirty = READ UNCOMMITTED
clean = REPEATABLE READ
reallyclean = READ COMMITTED
See their reference.
Here is the code:
result = Person.find_by_name params[:name] # some crazier query here