Data-driven behavioural algorithms for online advertising Users Profiling Cluster For each user we are able to have information about advertising campaigns, web pages and search queries of interest. We analyze the top significant keywords as- sociated to the content he visited and we are able to extract those ones which have the highest occurency frequency across the corpus of documents. The User Profiling algorithms is then able to build a profile with selected keywords.

1. CAMPAGNA BRAND
0%0%
0%0%0%
0%
0%
0%
0%
0%
100%
90%
75%
60%
45%
30%
10%
Last 30 days CTR RANDOM CLUSTER YIELD
0,085
0,080
0,075
0,070
0,065
0,060
0,055
0,050
0,045
0,040
0,035
0,030
0,025
0,020
0,015
0,010
0,005
0,000
242,5 245,0 247,5 250,0 252,5 255,0 257,5 260,0 262,5 265,0 267,5 270,0 272,5
Last 30 days Ecpm RANDOM CLUSTER YIELD
0,16
0,15
0,14
0,13
0,12
0,11
0,10
0,09
0,08
0,07
0,06
0,05
0,04
0,03
0,02
242,5 245,0 247,5 250,0 252,5 255,0 257,5 260,0 262,5 265,0 267,5 270,0 272,5
Results
We ran several tests to compare this methodology with competitors platforms and with non optimized impressions.We delivered the same campaigns on the same publishers and si-
multaneously by three different delivery algorithms:
1. Our cluster yield method described in this paper / 2. A random non optimized method / 3. A competitor
platform based on standard behavioural techniques
We executed this test on different days and with different campaigns.We measured an average increase of conversion rate of 150% by the cluster yield method compared with non op-
timized delivery.We measured an average increase of conversion rate of 60% by the cluster yield method compared with competitor platform
Data-driven behavioural algorithms
for online advertising
Users Profiling
For each user we are able to have information about
advertising campaigns, web pages and search queries
of interest. We analyze the top significant keywords as-
sociated to the content he visited and we are able to
extract those ones which have the highest occurency
frequency across the corpus of documents. The User
Profiling algorithms is then able to build a profile with
selected keywords.
Clustering Methods
The User Similarity Matrix is used to apply an automatic
K-Mean Clustering Algorithm. It would be computatio-
nally difficult to cluster the huge amount of user profi-
les built over Simply Network. As a consequence we
developed the following clustering strategy:
1. We apply the clustering methods just on the most
active users where for active we mean the fact that
they clicked on advertising campaigns and/or launched
search queries.
2. Once the clusters are built , we estimate a set
of centroids
3. We then built a classification al-
gorithm that estimates the di-
stance between an user and the
centroids of the different cluster
and will assign the user to the best
matching cluster
4. We classified all profiled users
Cluster
30%
Affinity
percentage