My readers are perhaps just a bit geeky, um, like me, because they are coming from universities, financial institutions and the software computing industry.
Google analytics tells me the Network locations of computers that have reqested page-loads from the wendyhome servers. Often these network locations are clearly consumer internet service providers, sometimes they are not. Here are some of the Network locations that do not look like consumer internet services grouped by primary business type.
Software/Computing
- Microsoft Corp
- APPLE COMPUTER
- Intel Corporation
- IBM
- Macafee Security
- Research Machines plc
- Hewlett-Packard Company
- Cisco Systems inc
- Opera Software asa
- Honeywell
- Eastman Kodak Company
Financial
- Credit Suisse group canada
- Fidelity Investments
- Bloomberg Financial Market
- Bank of America
- Barclays Capital (UK)
- Nat West Bank group (UK)
- First Rand Bank
Universities
- Cornell University
- Purdue university
- Leeds University (UK)
- North Carolina State university
- University of Brighton (UK)
- University of Cambridge (UK)
- University of Washington
- Charles University
Local government
- Wolverhampton city council (UK)
- East Sussex local education authority (UK)
- State of Arkansas
- State of Minesota
- State of Tennessee
- Government of South Africa
Aerospace
- the boeing company
- lockheed martin corporation
- Patrick Air Force Base
According to Google analyticals I have one regular reader in Tring. Excellent, Good show. The internet reaches the person in Tring who is curious about middle-aged English girlies in the Seattle region. hooray! Who would have guessed?! Welcome reader from Tring, You know who you are….. …isn’t Tring ’cute’? Oh my, I am definitely developing Anglophile tendancies…
Blog statistics below courtesy of Google Analytics. Google Analytics’ glossary defines visitors as:
A Visitor is a construct designed to come as close as possible to defining the number of actual, distinct people who visited a website. There is of course no way to know if two people are sharing a computer from the website’s perspective, but a good visitor-tracking system can come close to the actual number. The most accurate visitor-tracking systems generally employ cookies to maintain tallies of distinct visitors.
The method, heuristic, that Google Analytic employs to identify visitors is not detailed. The Google Analytics graph of :: Wendy House :: visitors below covers a ‘normal’ calendar month and suggests that between April and May 23rd:
- 600-ish visitors were sent by search engines.
- 60-ish visitors return* several times per month. Many friends and family are in this group.
- 40-ish visitors return on a daily basis. If this is ‘people’ what troopers you are!
- 10-ish visitors, and me, return twice a day. Given that I regularly post only once per day 2 visits is a tad perplexing.
- no-one visits between 101-200 times per calendar month, how odd is that? As odd as a snake wearing a beanie in a wheelbarrow race, that’s how odd.
- 100-ish visitors return over 200 times in one calendar month. Super Snoopers! Now thats just SILLY. I don’t believe it. I wonder what this number really suggests…
*return = becomes active after more than 30 minutes inactivity in the Wendy House.
Visitor loyalty, fabulous idea!
The bar-chart below from Google Analytics shows that between 24th and 30th of January:
- 8 visitors (probably unique IP addresses?) visited between 7 and 14 times
- 11 visited 15-25 times, and a further
- 11 visited 26-50 times
I’m probably one person who visited 50 times
This means that I may have 29 readers (unique IP addresses) that return frequently enough for me to guess-timate that its probably on a daily basis. I hope this isn’t really because their browser keeps crashing when they try to open the blog….
Of those 29 I can make a good guess about who 9 of them are… …based on both verbal and blog comments, people I’ve met… Based on this sample of 9 people I’ll develop a “Robin the Regular Reader” persona. I’m not assuming that these loyal visitors are the same visitors who stay for more than 10 mins when they get here. That’s a very rash assumption. The kind of rash that could go all red after a good scratching…
In this second post of a sporadic series on blog quality I hypothesize a classification of current blog visitors using some of the Google Analytics statistics for :: The Wendy House:: This is my first step in developing an intended user ‘Persona’, Alan Cooper style, that will be used to improve your experience. Hoorah!
One of the many fancy displays provided by Google Analytics shows the average length of a ‘visit’ (a ’session’):
By default in Analytics, a session is defined as the period of time during which visitors are interacting with your site and there has been inactivity for less than 30 minutes. After 30 minutes of inactivity, any further page views will be treated as a new session. Users that leave your site and return within 30 minutes will be counted as part of the original session
I’m hypothesising that the data in the graph below from 24hrs in :: The Wendy House :: shows 4 types of visitor*. I’ll call them:
- Pat promptly leaves: 149 visits are less than 10 seconds.
- Sam the speedy scanner: 18 visitors stayed between 10 seconds and one minute.
- Charlie checks content: 24 people stayed between 1 and 10 minutes. I suspect these people acutally read enough of the Blog to make a reasonably well informed estimation about the relevance of its content to them.
- Robin the reader: 13 visitors stay at the Wendy house for half an hour (10) or more (3)
*one person can appear in these statistics more than once, by re-visiting. Approximately 10% of the total site visits are return visits.
Google analytcis provides a web site statistics service that is free to websites that receive less than an obscenely large amount of clicks. I set it up for the Wendy house and dropped by to look at the reports 24 hours later.
It told me all sorts of things with graphs, pie-charts, percentages. maps and tables.More pretty data representations than you can shake a stick at. The data can even be exported to Excel. What does a peak at this data for Yesterday tell you?:
