Datacratic Blog: Technology
At Datacratic, one of the product we offer our customers is our real-time bidding (RTB) optimisation that can plug directly into any RTBKit installation. We’re always hard at work to improve our optimisation capabilities so clients can identify valuable impressions for their advertisers. Every bid request is priced independently and real-time feedback is given to the machine learning models. They adjust immediately to changing conditions and learn about data they had not been exposed to during their initial training. This blog post covers a strange click pattern we started noticing as we were exploring optimized campaign data, and a simple way we can use to protect our clients from it.
Optimizing a cost-per-click campaign
Assume we are running a campaign optimized to lower the cost-per-click (CPC). The details of how we optimise such a campaign are beyond the scope of this post, but in a nutshell, we train a classifier that tries to separate bid requests based on the likelihood that they will generate a click, assuming we win the auction and show an impression. Our models are naturally multivariate, meaning they learn from many contextual features present in the bid request, as well as any 3rd party information that is available.
One simple and highly informative feature used by the model, the feature this post is about, is the site the impression would be shown on. From a modeling perspective, this roughly translates to asking...
Today is a big day for Datacratic and we couldn’t be more enthusiastic. We are releasing the first open source version of RTBkit, our real time bidding platform, which represents several man-years of effort and is an expression of our vision for what we think a data-driven RTB platform should look like.
Welcome to my little corner of the datacratic blog where I'll be writting about random bits of interesting code that I happen to be working on at the moment. I'll start things off by describing a fun little algorithm that I recently wrestled with, namely, a 3-way trie merge.
Un projet sur lequel notre équipe d'apprentissage machine travaille actuellement est un engin de recommandation qui sert à générer des courriels personnalisés pour les clients d'un magasin en ligne. Le modèle que nous avons développé utilise l'historique de navigation et d'achats des utilisateurs du site. Chaque utilisateur est représenté par la combinaison de l'ensemble des produits avec lesquels il a interagi ainsi que leur relation avec chacun des produits que nous pouvons lui recommander.
When you bid truthfully and pace economically, you are always trying to allocate your budget to the auctions which look like the best deals, whether that means that the user is very likely to click, or that the price is low because fewer bidders are in the running or there is no publisher price floor.
At Datacratic, we develop real-time bidding algorithms. In order to accomplish advertiser goals, our algorithms automatically take advantage of other bidders’ sub-optimal behaviour, as well as navigate around publisher price floors. These are bold claims, and we want our partners to understand how our technology works and be comfortable with it.
There doesn't appear to be a good Wikipedia entry for RTB for me to link at the moment, when I want to blog about it so I'll draft my own explanation here. (Edit: there is an entry now, but I like my characterization better!) Keep in mind while reading this that I'm looking at RTB as a software engineer with an interest in economics, rather than as an ad industry veteran!
I read an interesting post on the AppNexus tech blog about their campaign monitoring tools and the screenshots there almost exclusively contained various pacing measurements. Some of the graphs there looked a lot like the ones I had sketched up while trying to solve the pacing problem for our real-time bidding (RTB) client.
One of the things we do at Datacratic is to use machine learning algorithms to optimize real-time bidding (RTB) policies for online display advertising. This means we train software models to predict, for example, the cost and the value of showing a given ad impression, and we then incorporate these prediction models into systems which make informed bidding decisions on behalf of our clients to show their ads to their potential...