Color invites you to “creates new, dynamic social networks … wherever you go”. It’s getting a lot of attention at the moment, largely because of $41M VC funding. It’s even being hailed as having ‘a very good chance of becoming a large scale success like Twitter‘.

In case you have not yet heard of Color, here’s how Caroline McCarthy describes it for CNET

In Color, photos taken through the app are shared through proximity, something which amasses a list of your contacts through machine learning; in effect, you’ll be able to see all photos around you that were taken with Color. You’ll be able to see the Color photos of the guy sitting two tables away from you at Starbucks, but when he finishes his caramel macchiato and leaves the coffee shop, you can’t see them anymore. But if you spend a lot of time in proximity to someone–an office-mate, for example–that person’s photos will gradually begin to stay in your contacts list for longer.

Someone asked me this week whether I thought it really would be ‘the next Twitter’. I found it hard to say at first, because my first experience with the app had been so awful that I had to go back and try it again to see what I’d missed. It really is a rather hard app to pick up (and has been heavily slated in the App store reviews, often for being hard to understand) but it’s not hard to see that the idea of physical spaces having an invisible cloud of history and shared photos has potential; being able to see other angles you missed, knowing your friend was here yesterday, … you can imagine lots of fun stuff emerging from an experiment like this.

But no, I don’t think it’s going to be “the next Twitter”. Not at all.

Being based on physical proximity makes for a pretty tough first experience. Unless you happen to install and try it while you’re at a big event with at least a couple of other people using it, you’re left with a pretty unsatisfying starting point. Any app that requires you to be in the same place as other people using the same app at the same time is going to have a difficult bootstrap problem.

Most importantly though, Twitter is a platform with an open API allowing other apps to be built on top of it. Want to write your own Twitter client? Want to integrate Twitter into another app? Want to print out tweets that contain the word ‘snow’? Easy. Not so with Color. Want to make a site showing the most recent Color pictures taken in a particular place? You can’t. Unless you’re the Telegraph and you want to do a joint PR thing around the royal wedding (the sanity of which also raised some eyebrows).

That’s not to say that the situation won’t change. Instagram started closed and opened up an API after a few months. That move made it easier for people to make all sorts of really cool apps like Extragram, GramFrame, Instagrid, Instaprint, Instac.at and many more.

In fact, the most common use I’ve seen of Color so far has been that people will sometimes post a direct link to a picture to Twitter or Facebook. While that’s a useful feature (and in theory leads to more people discovering Color) it does mean that the whole local proximity and physical social discovery aspect of Color becomes optional; people continue to rely on those two tools to maintain their contact networks.

I think in its current incarnation Color is more of a photo sharing service, like Twitpic or Yfrog, with some additional features which might rarely get used. If they open up and offer an API (like Instagram did) they could become a much more interesting thing altogether, but only if it can get – and keep – users. Although I like its innovative approach, I think it’s going to be very tough for this app to become anything like mainstream.

I’ll give Color another chance, but I think I’ll also be looking out for the next next big thing.

Cross-posted to the W+K London blog.

With that in mind, I installed MeeTimer over the weekend. It’s a Firefox plugin which…

records where you spend your time online. It does it in a rather useful way, by allowing you to group websites into activities … so you can make sense of where your time is going. Finally, it accumulates time spent on a site over the course of a day…

I’ve been using it for 3 days and it’s giving some interesting food for thought.

You can even optionally set up ‘tab warnings’ on specific groups (sites you’ve labeled ‘Procrastination’, say) which will pop up with a nice overlay telling you exactly how much time you’ve wasted in this site, and others in the same category (though allows you to click through and ignore the warning just this once or for the current browsing session if you still want to). I’m already finding this feature useful on the handful sites whose feed I’m subscribed to but for some reason still find myself visiting out of habit. (For me, it’s Waxy links and Boing Boing. I love them, but I’d rather be reminded to enjoy them as part of my feed reading routine rather than browsing out of habit. I bet you have your own which make you ask is this really what you want to be doing right now?). A little reminder is really useful for habit-breaking here.

Mostly MeeTimer is just quietly keeping track of a bunch of per-site accumulators, cleverly based on whether Firefox has focus and which is the currently active tab. The results are already interesting. I realised that I was spending a bit less time on Twitter and Flickr, and a bit more time on work webmail, than I thought.

This is all very well, but I want more. Specifically, I wanted to get at the data. Not just the accumulated weekly/daily/monthly (etc) totals and averages, but the number of visits to each site per day. The raw visits. In as much detail as possible. I want CSV exports, or an API, or something. If I’m spending a daily average of 21 minutes on Twitter, how many visits comprise that time? MeeTimer simply doesn’t tell me.

Or does it?

Digging around my Firefox profile, I find a very interesting file at /Library/Application Support/Firefox/Profiles/{profile-id}/meetimer.sqlite. Ooh, I bet I know what that is. So I open up SQLite and start poking.

Sorry. It’s about to get a bit dull from here on in. Unless you get excited about the idea of being able to manipulate this data you’ll probably want to scroll down to the end. Honestly, I won’t mind.

They’ve gone? Right. Let’s get hacking.

$ sqlite
SQLite version 3.6.12
Enter ".help" for instructions
Enter SQL statements terminated with a ";"
sqlite> .restore meetimer.sqlite
sqlite> .tables
deterrent_stats  groups           log
deterrentlinks   groups_urls      url_maps
deterrents       ignored_urls     urls

Excellent. We’ve got tables with sensible names and everything. Let’s see what log looks like.

sqlite> .headers on
sqlite> select * from log limit 3;
url_id|startdate|duration|day|week
4|1238324612508|3|200987|200913
5|1238324617244|44|200987|200913
6|1238324647668|17|200987|200913
sqlite> select * from urls limit 3;
id|url
1|mail.google.com
2|www.google.com
3|www.google.co.uk

Lovely. Easy enough then. The groups and groups_urls tables do what you’d expect too. For now, let’s make url_id more meaningful by doing a join with the url table.

sqlite> select
  url_id, duration, day, week, url
from log
left join urls on log.url_id=urls.id
limit 5;
url_id|duration|day|week|url
4|3|200987|200913|google.co.uk
8|40|200987|200913|meetimer.productivefirefox.com
4|16|200987|200913|google.co.uk
11|10|200987|200913|technorati.com
12|14|200987|200913|google.com/reader/

What if we wanted to show the number of visits, the total duration, and the maximum length of duration for visits to Twitter…

sqlite> select
  count(url_id), sum(duration), max(duration), url
from log
left join urls on log.url_id=urls.id
where url = 'twitter.com';
count(url_id)|sum(duration)|max(duration)|url
34|2712|455|twitter.com

Excellent. I wonder what the top seven URLs when ordered by the number of visits?

sqlite> select
  url_id, count(url_id), sum(duration), max(duration), day, week, url
from log
left join urls on log.url_id=urls.id
group by url
order by count(url_id) desc
limit 7;
url_id|count(url_id)|sum(duration)|max(duration)|day|week|url
9|34|2712|455|200989|200914|twitter.com
10|30|1075|249|200989|200914|search.twitter.com
1|22|2505|928|200989|200914|mail.google.com
4|20|206|57|200989|200914|google.co.uk
17|18|476|114|200989|200914|flickr.com
21|10|2480|2125|200989|200914|bbc.co.uk
39|8|13152|10212|200989|200914|webmail.bbc.co.uk

Twitter, with 34 visits. Sheesh. And for comparison, the top 7 sites by total duration of visit?

sqlite> select
  url_id, count(url_id), sum(duration), max(duration), day, week, url
from log
left join urls on log.url_id=urls.id
group by url
order by sum(duration) desc
limit 5;
url_id|count(url_id)|sum(duration)|max(duration)|day|week|url
39|8|13152|10212|200989|200914|webmail.bbc.co.uk
9|34|2712|455|200989|200914|twitter.com
1|22|2505|928|200989|200914|mail.google.com
21|10|2480|2125|200989|200914|bbc.co.uk
12|6|1355|633|200989|200914|google.com/reader/

13152 seconds (3.6 hours) on my work webmail between Sunday morning and Wednesday aftenoon. And all done in 8 visits. Yuck.

Ok. Let’s start thinking about daily summaries. Grouping by day, and then by URL (since I’m not very good at SQL, and don’t know how to limit it to 5 per day, I’ll just manually snip out all but the top 5 for each day for now)…

sqlite> select
  url_id, count(url_id), sum(duration), max(duration), day, url from log
left join urls on log.url_id=urls.id
group by day, url
order by day, sum(duration) desc;
url_id|count(url_id)|sum(duration)|max(duration)|day|url
1|2|306|228|200987|mail.google.com
9|6|296|217|200987|twitter.com
12|2|225|211|200987|google.com/reader/
28|1|128|128|200987|hunch.com
21|1|66|66|200987|bbc.co.uk
[...]
39|3|10222|10212|200988|webmail.bbc.co.uk
21|3|2155|2125|200988|bbc.co.uk
9|18|1494|235|200988|twitter.com
1|12|1003|185|200988|mail.google.com
10|14|777|249|200988|search.twitter.com
[...]
39|5|2930|2667|200989|webmail.bbc.co.uk
1|8|1196|928|200989|mail.google.com
9|10|922|455|200989|twitter.com
12|1|394|394|200989|google.com/reader/
21|6|259|151|200989|bbc.co.uk
[...]

And returning to the original question of just how many visits do I make to Twitter

sqlite> select
  count(url_id) as visits,
  round(sum(duration) / 60.0, 2) as total,
  round(max(duration) / 60.0, 2) as longest
from log
left join urls on log.url_id=urls.id
where url = 'twitter.com'
group by day
order by day;
visits|total|longest
6|4.93|3.62
18|24.9|3.92
10|15.37|7.58

So it seems that on Sunday I made 6 visits for a total of about 5 minutes and a single longest session of 3 and a half minutes. On Monday it was 18 visits for a total of 25 minutes including one session of nearly 4 minutes, while today, 10 visits so far (including one of over 7 minutes) have already added up to over 15 minutes.

.mode csv

in SQLite is handy too, because it changes that list format to look like

visits,total,longest
6,4.93,3.62
18,24.9,3.92
10,15.37,7.58

so it’s trivial to open it in a spreadsheet.

Even better will be something cunning and programmatic. Maybe in PHP or Ruby or something. Even this exploratory manual approach is fun though. It will obviously be better once I’ve built up a bit more history but now I know that MeeTimer is storing my data in a way that I can access it, I’m even more excited about it. Thanks, MeeTimer. You rock.

You can request your own copy here (I don’t think it will cost you any money. Feel free to say I sent you). If you’re interested in social networking platforms can do for your brand or business, you might get something out of it. Although the sample size for this report is not huge, it’s an attempt to undertake some formal research into the use of social networking sites by brands, and it does a pretty good job. The report also includes case studies from Innocent drinks and BBC Radio 1 and the Chris Moyles Show, among others.

I don’t think it’s just relevant for marketing either, though that’s clearly an important focus of the study.

At 47 pages, it’s a bit too long for my tastes. Hence, I’ve pulled out some of the highlights. These may be obvious to you (they certainly make me nod pretty frantically) but it’s useful evidence.

From section 2.1:

“67% of Facebook respondents and 65% of MySpace respondents who answered 5 (slightly stronger) and 6 (much stronger) on the Likert scale indicated that they would feel more affinity and loyalty toward a brand they are a friend or fan of, if that brand listened to their opinions and responded to communications.”

From section 4.1 Are Facebook and MySpace effective platforms for social network marketing?:

“…the consumer conversation between brands and social network users should be controlled by the users themselves. This idea is backed up by the response to the Facebook and MySpace surveys, whereby the respondents strongly indicated that their loyalty to a brand on a social network site would be lost rapidly if the brand itself controlled the conversation…”

From section 5.6 User comments and suggestions offer real value:

“Brands should place significant importance on the value of comments made by users after they have become a friend or fan, and their ability to influence others as brand advocates. This really is the focus of a brands attention, not just the number of friends or fans they have. “

This really important point is also emphasised in section 5.3: Brands and marketers must listen to their friends/fans.

Although the BBC were interviewed for the report, I can’t take any credit; I actually didn’t have any involvement with it at all (Sam Bailey, from Radio 1, was the person interviewed). I’m just a reader and sharer, and I’d be interested to hear what you think about it.