cClone

Positive event

(variable to explain) to select the targeting parameters to be considered by the algorithm for the creation of the clone

Negative event (Universe)

(explanatory variables) characterizes the group of internet users that will be scored against the parameters selected in the positive segment

cClone Mode

Clusters Only

No Socio-demo behavior

Versioned profile

profile that already exist

Mini bucket

Volume of cookies to create scoring tree,

Algorithm parameters

he depth of the scoring tree, i.e. the branches of the tree that will highlight the best explanatory variables for a score

Data TYPE

1st party data

My Data, My Media data

• My Data (1st party data)

data from advertiser’s website and CRM databases applying AND, OR, EXCLUSION rules on the target parameters

• My Media (1st party data)

data from advertiser’s previous campaigns Combine various data sources to create an audience segment,

• My Audiences (1st party AND/OR 3rd party data)

Saved audiences composed of C-clones, 1st party and/or 3rd party data

3rd party data

Composed of Demographic, Behavioral, Geography, Technology

Behavioral (3rd party data)

Weborama’s data with segments & clusters on Interest profile

This data set is divided into 2 criteria: Segments and Clusters

1- Segments: 23 segments that aggregate several clusters

2- Clusters: 160+ centers of interests. Each cluster is linked to one single segment

Example: The “Clothing, shoes and accessories” segment is composed of 7 clusters: accessories, clothing, eyewear, fashion trend, footwear, jewelry, lingerie

Demographic (3rd party data)

Weborama’s data with segments & clusters on Socio-demographic profile

Geography (3rd party data)

enables to select the geographic criteria to build an audience
This data set is divided into pre-defined administrative groupings according to the country, e.g. Regions and Departments for France

Regions: regroup the 27 administrative regions in France

Departments: regroup all the departments linked to a region

Technology (3rd party data)

enables the user to select the technologies criteria to build an audience
This data set is divide into pre-defined groupings according to User Agent information and Digital Elements (for Internet Service Providers)

1- Browsers = IE, Firefox, Chrome, Safari, Opera, etc.

2- Devices = Desktop, Mobile, Tablet, Unknown

3- Operating Systems = IOS, Android, Blackberry, MacOS, Windows, Symbian etc …

4- Internet Service Providers = depending of the country

Boolean engine

Combine various data sources to create an audience segment, applying AND, OR, EXCLUSION rules on the target parameters

Bootstrap

Retrieve data on defined previous period (Recency 1, 3, 5, 10, 15, 30)

Frequency (FEATURE)

- Unique frequency : The number of times a unique user was exposed to an ad.

- Daily unique frequency : The average number of times a daily unique user was exposed to an ad.

Recency

Calculated in real time, Volume on previous N Days.

recency 1, 2, 3, 5, 7, 14, 21, 30 days.

Quantile

Level of user’s interest in a segment from 1 to 20

Surf intensity

Level of user’s internet navigation from 1 to 20