Unique Browser (formerly Unique Visitor - one of the IFABC metrics) is an unique and valid identifier (= IP + User-Agent). [1]

## Unique Browser not Visitor

This metric does not measure a person but is rather a measure of the device through which a person interacts with a website or network. Formerly it was called the Unique Visitor, but it was misleading as it made people believing that they measure by this way real people. [2]

### Difficulties to count people

We may therefore reasonably assume that number of Unique Browsers somehow corresponds to people visiting a website (the trends corresponds well to the increasing or decreasing of the popularity of a website).

However, the very same person accessing a website from the office during the day, from the smart-phone when commuting, and from home the evening will be counted 3 times - because using 3 different devices. On the contrary, when sharing a PC (at home, internet cafe, etc), the visits of several persons are counted as only 1 Unique Browsers (as they access over the very same device).

## Understanding aggregation of the Unique Browser

A typical error that is done while treating with Unique Browsers/Visitors data is that of taking the figures for a specific period and then making a simple sum of single periods for counting a different timespan (for example, summing up unique browsers figures for 12 months to obatin a year period's unique visitors). Unfortunately, such sum does not have any real meaning as it does not correspond to the number of Unique Browsers for the needed period (in the previous example, for a year).

#### A practical example: unique and repeated clients in a shop

Imagine you want to know how much clients enter a shop during a week:

• Monday: Jane Dee came in the morning = 1 unique client
• Tuesday: Jane Dee came in the afternoon = 1 unique client
• Wednesday: Jane Dee came in the morning and she returned in the afternoon = 1 unique client + 1 repeated client (2 visits)
• Thursday: Jane Dee came at noon = 1 unique client
• Friday: Jane Dee came in the afternoon = 1 unique client
• Saturday: Jane Dee came in the morning and John Do came in the afternoon = 2 unique clients

(On Sunday shop is closed)

• When measuring number of clients ''''per week',you start measuring on Monday and stop on Saturday.
1. On Monday there was1 client and on Tuesday the very same client came again ('repeated client).
2. Then the same loyal client continued to come every day (twice on Wedndesday, that is, ther was a repeated client).
3. On Saturday there was1 new client.

Therefore during the week period you had 2 unique clients and 1 repeated clients.

In other words you have during the week 2 unique clients, Jane Dee coming every day (repeated client per week) and John Do coming once a week (unique client per week). (You might try to make a simple sum of number of clients per day (2+1+1+1+1+2=8) and believe that you have 7 clients, but it is just because you would count Jane Dee 6x and John Doo 1x.

If your clients keep the same behaviour, so Jane coming every day and John coming once a week, the number of your unique clients per month will be also 2 (and also number of the repeated client will be 2 - John came every Saturday in the month), as well as the number of the clients per year (Jane came every day in the year and John every Saturday of the year).

### Example with the Europa webnest

Following examples show the number of unique and repeat browsers aggregated per day.

Number of browsers per Day
Date Unique Repeat
01 Jan '11 234 697 24 473
02 Jan '11 362 481 32 052
03 Jan '11 668 729 58 837
04 Jan '11 733 644 79 701
05 Jan '11 718 853 84 057
06 Jan '11 651 774 70 195
07 Jan '11 641 498 61 367

To get the number of unique and repeat browsers aggregated per week, there has to been done new aggregation.

The sum of aggregations per day gives wrong number, because this way are counted again the unique browsers which have been counted already.
Number of Browsers per Week
Date Unique Repeat
01-07 Jan '11 733 644 84 057

All what has been mentioned above, can help to understand the number of unique and repeated browsers on EUROPA webnest by month and by year. (The conclusion that in the year 2010 there have been 157 593 711 unique browsers = the sum of the monthly aggregation is wrong, because some unique browsers could be count even 12times).