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 coming twice (repeated client)
• 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 Doo came in the afternoon = 2 unique clients

(On Sunday shop is closed)

You might be tempted to make a simple sum of number of clients per day (1+1+1+1+1+2=7) and believe that you have 7 clients, but it is just because you would wrongly count Jane Dee 6 times. In reality, there were just 2 clients during the week.

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 there were 2 clients and 1 repeated client: (1) Jane Dee coming every day (repeated client per week) and (2) John Doo coming once in a week (unique client per week).

If the clients had the same behaviour over a month, so that Jane came every day and John came once a week, the number of unique clients per month will be still 2 (and also number of the repeated client will be 2 as John came every Saturday in the month). The same as well per year (Jane came every day in the year and John every Saturday of the year) and so on.

### Examples with the Europa webnest

The following tables show the number of unique and repeated browsers aggregated

1.  per day:
Number of browsers per day
Date Unique browsers  Repeated browsers
01 Jan 2012 234 697 24 473
02 Jan 2012 362 481 32 052
03 Jan 2012 668 729 58 837
04 Jan 2012 733 644 79 701
05 Jan 2012 718 853 84 057
06 Jan 2012 651 774 70 195
07 Jan 2012 641 498 61 367

1. per week:

Number of Browsers per week
Date Unique browsers  Repeated browsers
01-07 Jan 2012 733 644 84 057

As previously explained (see example for shop), a specific aggregation is needed to get the number of unique and repeated browsers aggregated per week, as the sum of aggregations per day would give a wrong number, as in that way unique browsers which have been counted already would be -wrongly- counted again.

The examples above can be applied 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 aggregations) would be wrong, because some unique browsers would have been counted 12 times.