Unique Device

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

Unique Device not Unique 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. 

Difficulties to count people
We may therefore reasonably assume that number of Unique Devices 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 café, etc), the visits of several persons are counted as only 1 Unique Device (as they access over the very same device).

Understanding aggregation of the Unique Devices
A typical error that is done while treating with Unique Device/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 devices figures for 12 months to obtain a year period's unique devices). Unfortunately, such sum does not have any meaning.

A practical example: unique and repeated clients in a shop
Imagine you want to know how many clients entered a shop during a week:


 * Monday: Jane Dee came the morning = 1 unique client
 * Tuesday: Jane Dee came in the afternoon = 1 unique client
 * Wednesday: Jane Dee came the morning and she returned the afternoon = 1 unique client coming twice (repeated client)
 * Thursday: Jane Dee came at noon = 1 unique client
 * Friday: Jane Dee came the afternoon = 1 unique client
 * Saturday: Jane Dee came in the morning and John Doo came in the afternoon = 2 unique clients

(The shop is closed Sunday)

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 unique 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 Wednesday, that is, there was a repeated client).
 * 3) On Saturday there was 1 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 web-nest
The following tables show the number of unique and repeated devices aggregated


 * 1) per day:


 * 1) per week:

As previously explained (see example for shop), a specific aggregation is needed to get the number of unique and repeated device aggregated per week, as the sum of aggregations per day would give a wrong number, as in that way unique device which have been counted already would be -wrongly- counted again. In fact, over the weekly period we can consider that the top daily value counted for unique devices has to be taken into account.

The examples above can be applied to understand the number of unique and repeated devices on EUROPA web-nest by month and by year:



The conclusion that during 2010 there have been 157 593 711 unique devices (= the sum of the monthly aggregations) would be wrong, because some unique devices would have been counted 12 times. However, an approximation can be done whenever a specific aggregation is lacking (in this case, the yearly aggregation), and we can consider the top value as being the best possible indicator for the period (in the example, March 2010's value).