Behavioural Algorithms for Online Advertising

Simply Data-driven behavioural algorithms for online advertising A DADA BRAND 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 associated 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. CAMPAGNA BRAND Clustering Methods The User Similarity Matrix is used to apply an automatic K-Mean Clustering Algorithm. It would be computationally difficult to cluster the huge amount of user profiles built over Simply Network. As a consequence we developed the following clustering 2. Affinity percentage 100% strategy: 1. We apply the dustering methods just on the most 75% 60% active users where for active we mean the fact that they clicked on advertising campaigns and/or launched search queries. 10% 2. Once the clusters are built, we estimate a set of centroids 3. We then built a classification algorithm that estimates the distance between an user and the centroids of the different cluster and will assign the user to the best matching cluster 4. We dlassified all profiled users Results Last 30 days Ecpm Last 30 days CTR - RANDOM RANDOM CLUSTER YIELD • CLUSTER YELD 0.16 015 8.085 0.4 013 0,1 010 0,08 0,07 0,06 0.05 0,04 0,03 0.02 242,5 245,0 247,5 250,0 252,5 255,0 257,5 260,0 262,5 265,0 267,5 270,0 272,5 242,5 245,0 247,5 250,0 252,5 255,0 257,5 260,0 262,5 265,0 267,5 70,0 272,5 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 simultaneously 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 optimized delivery. We measured an average increase of conversion rate of 60% by the cluster yield method compared with competitor platform.