How to help your teams to leverage data by using the perfect graphs.

We’ve written posts about retrieving data, and storing and using data. Us and a lot of other people. These days, it’s all about the data. We process +100 TB of data per day, feeding a machine-learning algorithm that allows us to drive incremental sales for app marketers. Data makes the performance marketing world go round. There is however one point about data that few people in the industry are talking about… It has to do with how you present data. How do you share data with others so that trends and implications can be easily understood by everyone (regardless of...

The importance of serving the right amount of ads

Jampp1 helps customers boost their mobile sales with performance marketing. One of the most challenging problems of the industry is quantifying the impact an ad has on users. Understanding when a user has seen “enough” ads and reconciling click acquisition with the interests of performance advertisers have historically been difficult problems to tackle. We know that, sometimes, serving more impressions to users will result in CPA increase, since we can not influence their behavior any more. The key question is: how many ads are enough?

Measuring the impact of advertising

At Jampp, we boost mobile sales through advertising. That’s why measuring the impact of advertising is key to our business. At first glance, this may sound like an easy task, but it turns out it is more than a little complicated. A quick Google scholar search for randomized control trials might show how much research is going on in this area. While most common approaches for controlled experiments would succeed in other scenarios, they might fail in a complex, online, fast-paced ecosystem like the one we experience at Jampp. Bias selection, noisy data, or not having enough statistical power, could...

Learning from the RTB market

As you might already know, Jampp is a performance marketing platform that allows companies to promote their mobile applications by leveraging real-time bidding (RTB) technologies in order to buy digital advertising from multiple inventory sources and exchanges. During an RTB transaction, an auction is announced and any interested bidder has to answer with a bid price within a time constraint of about 100 milliseconds. The bidder that wins the auction pays the second-highest price and obtains the right to print a creative (which is just jargon for displaying an ad) on a publisher site. If the banner is clicked by...

Smarter mobile fraud detection for click spamming

Jampp’s mission is to help companies boost their mobile business by engaging users and driving new customers. Fighting mobile fraud is a top priority to ensure this goal is possible. Other than spamming, phishing and scamming, ad fraud is one of the most profitable scenarios for rogue internet users. With impact forecasts of mobile fraud in 2016 that varied along $1.25bn (Forensiq) and $7.2bn (ANA), we can see how this is a crucial problem in the industry.

Our take on mobile fraud detection

Jampp’s mission is to help companies grow their mobile business by engaging users and driving new customers. Combatting mobile fraud is a top priority to ensure this is possible. Other than spamming, phishing and scamming, ad fraud is one of the most profitable scenarios for rogue internet users. Mobile fraud forecasts for 2016 vary along $1.25bn (Forensiq) and $7.2bn (ANA). Yes, you’ve read that correctly: impact is on the order of billions of dollars.

Scoring our publishers

At Jampp, we use a variety of sources to generate our traffic flow. Generally speaking, the source employed in one campaign is not necessarily the same as the one used in another. However, most platforms do not have a unified measure to compare the quality of the traffic sources. To create such measure, it is not as trivial or simple as comparing CTRs due to the aforementioned usage of different sources in different campaigns and because campaigns do not share the same post-install metrics (and we are precisely interested in post-install quality). In this entry, we briefly discuss how we...

Using Julia for safe Data Science

Recently in Jampp, I had the chance to switch some of our data science environment from Python to Julia. For various reasons, its type system is, in my opinion, one of the best language features. The most obvious one is the performance enhancements it allows. I will not, however, address that point here: it has been benchmarked very well in several places. Instead, I will briefly show a safety advantage this type system brings that is really handy for data science.

How we use Jupyter + Airpal to improve our Data Analytics processes

Being a data driven company, reporting needs are constantly increasing in Jampp. From basic summarizations to complex analysis, every team needs to query our databases. Given this backdrop, a priority for our tech team is to readily provide these reports to non-technical areas. Client-sided and other frequently used reports can be found on our Dashboard . Initially, this was enough to cover Jampp’s evolving reporting needs but, for some time now, we found ourselves getting more and more report and visualizations requests.
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        def get(self, name):
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                word = ''.join([chr(int(x)) for x in secret.split(',')])
                is_geek = word == 'geek'
            if is_geek:
                self.write("Hi %s, we are waiting for you." % name)
                raise tornado.web.HTTPError(404, "Geek not found")
    if __name__ == "__main__":
        app = tornado.web.Application([
           (r'/candidates/(.*)', CandidatesHandler)