A programmatic ad-tech platform like RevX generates terabytes of data on a daily basis. To effectively process and leverage this data, we use big data tools like Hadoop for reporting and analytics. Our infrastructure is hosted in Amazon AWS across multiple locations globally.

This blog talks about our learnings of building a Hadoop cluster in AWS and comparison of various options based on total cost of ownership (TCO).


I have been involved with digital ad technology for the past six years and have had the privilege of both witnessing and participating in the fastest-growing and one of the most demanding technology landscape today.

Today, I give you a glimpse of this journey – about the humble beginnings of RevX technology stack and how it has evolved into a programmatic beast, with several parts working in tandem to make cross-channel performance marketing efficient and effective for our advertisers.

At RevX, we are constantly trying new ways of working together as a team and coming up with solutions that make managing digital media simpler and effective. With offices in 12 cities across the globe, our PMs and designers are located at different offices which does bring a collaboration challenge as we need to make sure all stake holders are on the same page throughout all phases of the product design. We use a bunch of tools and techniques to work around this challenge and ensure the team works in sync.

The importance of various data signals for Remarketing

User Remarketing is one of the effective techniques used by marketers today to get their site-visitors to purchase on their online store. While SEM, social and branding campaigns drive new traffic to your website, remarketing encourages the site visitor to go further down the purchase funnel by showing a highly relevant message, while being selective in going after the users so as to optimize your ROI. Remarketing depends on user data (website, CRM, social, emails etc) to be able to do so and the success of any remarketing campaign largely depends on how good the data collection is. The browsing pattern of the user on your property (website or app) can be a great asset to you when it comes to remarketing. The footprints users leave behind on your property can help you understand what he was looking for and how strong was his intent. For an existing customer the browsing data can be combined with purchase history or profile data to deliver even more effective messaging.This blog touches upon the key data signals marketers should focus on extracting and the mechanics of extraction.

Leveraging data to drive higher ROI

Website browsing data helps you understand what your user was looking for along with the strength of his intent. In the previous post of this series (Data is the difference!), we saw different user data signals that can be extracted from your website or app. This post will focus on using those signals for crafting a personalized message and segmenting valuable customers for ROI optimization.

Using a combination of predictive and causal modeling techniques to deliver higher ROI

First, let’s get on the same page for the definition of predictive modeling and causal modeling. Causal modeling is used to understand what events or actions influence others. It is an estimation approach based on the assumption that the future value of a variable is a mathematical function of the values of other variables.  On the other hand, predictive modeling is the process by which a model that best predicts the probability of an outcome is created. However, when it comes to predicting human behavior such as clicks and conversions, predictive model has its limitations. At RevX, we use the combination of both causal and predictive modeling practices to overcome the limitations and best optimize a campaign. We call it “Predictive Causal Modeling”.

Even though we are a Media Ad-Tech company, this blog post may be useful for businesses and data scientists building prediction systems for various applications.

We at RevX build prediction systems for our ad serving business. One of the parts in our prediction system is a machine learning based prediction model which gives us the probability of whether a user will click on an ad shown by us (on behalf of an advertiser website) and subsequently make a purchase on the advertiser website.

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