Data-driven behavioural algorithms for online advertising Antonio Tomarchio Simply.com, Dada Spa

Via della Braida 5 20100 Milano

+393351605432

Antonio.tomarchio@dada.net

Francesco Bellacci Simply.com, Dada spa

Largo Annigoni

Firenze Telephone number, incl. country code

Francesco.bellacci@dada.net

Filippo Privitera Simply.com, Dada spa

Via della Braida 5 20100 Milano

Telephone number, incl. country code

Filippo.privitera@dada.net

ABSTRACT

In this paper, we describe an innovative data-driven behavioural

approach that we developed for the optimization of performance

online advertising on Simply, the new international adnetwork

developed by Dada spa.

Categories and Subject Descriptors

I.5.3 [Clustering and similarity algorithms]: Clustering

algorithms , similarity measures, multivariate statistics

General Terms

Algorithms

Keywords

Online performance advertising, conversion optimization,

advertising performance optimization, clickstream analysis, user

clustering, yield optimization, real time bidding

1. INTRODUCTION Performance advertising is becoming the most successful

advertising model on internet and mobile internet. In this model

advertisers do not pay the visualization of their marketing

message but just a direct response from the users who visualize

the ad. In particular, the last years the CPA (cost per action)

model had strong increase. In this latter model the advertiser will

pay a commission to publisher if and only if the user who clicked

on his ad will perform a specific target action on the advertiser’s

web site such as a purchase or the subscription to a newsletter. It

is then clear that in this kind of market it is absolutely necessary a

capability to optimize advertising visualization. Publishers usually

subscribe to adnetworks which will take care of optimizing their

inventory, acting as intermediaries between them and advertisers

with the aim to maximize revenues for publishers and ROI for

advertisers. The term “conversion rate” indicates the rate of

actions on 1000 users who clicks on the campaign ads, it is the

most important measure of performance for the advertiser.

Performance advertising model is financing several internet

services such as search engines and social networks, with big

benefits for users who can use these products for free..

Simply.com is a new adnetwork developed by Dada spa based on

a proprietary set of user-centric optimization algorithms.

2. BEHAVIOURAL TARGETING

TECHNIQUES One of most widespread buzzwords of the last two years in the

Internet advertising market is “behavioural targeting. The basic

idea is that if an advertiser is able to reach a user who seems to be

interested in the same products or who is searching for a good

which is sold by the advertiser, the probability of a click and of a

conversion on the advertiser web site will be much higher. By

reaching the interested users, advertisers will be able to increase

the ROI of their campaigns and publishers will be able to increase

the monetization of their traffic. Behavioural targeting is quite

different than more classical social-demographic targeting where

advertisers try to reach users in specific ranges of ages, gender or

annual income. Behavioural targeting can integrate this kind of

information but their primary goal is to identify in real time “the

intents” of users: what they are interested in buying.

The landscape of behavioural targeting solutions is huge, but the

general strategy of most of these platforms is to provide to

publishers a mean to create different “audience pixels” to segment

the users on their web sites. For example a publisher can create a

“sport” pixel and implement it in his sport sections. Users are then

segmented by these pixels and when they visit another page in the

network will receive campaign matching with their profiles. The

core aspect of all these solutions is that the publisher affiliated to

the behavioural targeting adnetwork will create audience pixels by

“a priori” human defined tree categories, not based on

performance data

3. A PURE DATA-DRIVEN

BEHAVIOURAL APPROACH Our idea is to create data-driven clusters of users and then to

develop a learning algorithm where each new campaign is at the

beginning delivered randomly to all clusters until the system

“learns” which are the user clusters where the campaign is getting

the highest conversion rate.

3.1 Data Gathering Affiliated publishers on Simply network implements a single

audience pixel. They do not have to create predefined segments

such as sports or entertainment. They just have to implement a

1X1 pixel. By this pixel we insert anonymous cookie in user

browsers and we are able to collect information such as the

campaigns they clicked on, the web pages they visited in simply

network and search queries they executed on simply affiliated

publishers. The data gathering is completely privacy consistent as

we do not track any sensible private information, neither the ip

address. For each publisher or advertiser web page they visit, by

proprietary state of the art information retrieval techniques, we are

able to extract most significant keywords contained in the above

pages.

3.2 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 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..

3.3 Similarity measure The user profiles can be used to introduce a mathematical

similarity measure.Each user is represented as a vector in a n-

dimensional space. Each coordinate represents the weight of a

specific keyword in the user profile. This representation is then

very similar to vector space model in information retrieval, where

documents are represented as vectors in a space where each

dimension is a determined word.We can add to our representation

other dimensions given by the demographic information if

available. The vector modeling of users allow to introduce a

standard similarity measures as the Cartesian product between two

vectors.By the Cartesian product operator we can build a

similarity matrix of our users set.

3.4 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 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 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 classified all profiled users

A key issue of k-mean based clustering algorithms is to establish

the optimal number of clusters. We applied a feedback algorithm:

we estimated the optimal number of clusters for conversion rate

3.5 Yield Optimization Once profiled users are clustered, Simply optimization algorithms

implement what is called in the industry “yield optimization”

strategy that can be summarized in the following steps:

1. A new advertiser campaign is uploaded on Simply

Network

2. At the beginning, the campaign is delivered completely

random on all user clusters

3. Algorithm tracks in real time on a hour-basis the

average conversion rate of the campaign across the user

clusters

4. Algorithm identifies clusters where the performance of

the campaign in term of conversion rate are highest

5. Algorithm will then delivery the campaigns just to the

users belonging to the top clusters

This delivery algorithm is completely real time and very effective.

We highlight that this strategy is radical different than standard

behavioural techniques: we do not take care at all about the

content of the advertising campaign and the content of the web

pages the user visited. We just focus on creating a similarity

between users and on clustering them. Once clusters are created ,

the delivery is led uniquely by the performances data. As the

conversion rate is the only driver, this algorithm can provide a

boost in term of performances optimization

3.6 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

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

3.7 Conclusions We developed a new performance advertising behavioural method

which is radically different than standard behavioural

solutions.The main and core innovation is that the algorithm is

based on a data-driven user clustering and on a real time analysis

of campaigns across the clusters. This method is content

independent and do not require any segmentation of the web

pages by the publishers.We have strong results that support our

intuition that a pure data-driven strategy can have a much higher

optimization potential than any strategy based on human

classification of content

4. References [1] De Liung and Jianqing Chen, 2006. Designing auctions with

past performance information. Decision Support Systems 42

(2006)

[2] Liu, C. 2010. When Machine Learning Meets the Web.

Microsoft Research Keynote Speech

[3] Jaworska, J. and Sydow, M. 2008. Behavioural Targeting in

Onlina Advertising: an Empyrical Study. Lecture Notes in

Computer Sciences (Springer, Volume 5175/2008, 2008)

[4] Muthukrishnan, S.2008. Internet Ad auctions: insights and

directions. ICALP , 2008

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