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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.


Adaptive Ads

A banner ad typically is a rectangular advertisement placed on websites either on top, bottom or sides of the website’s regular content. Here are the 3 most important parts of a banner ad-

1. Call to action

2. Color, images, and background

3. Value proposition


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”.


A digital identity is data that uniquely describes a person. This unique identity may not necessarily be traceable to an actual person, yet it helps in providing a very rich experience. Publishers can use this information to show personalized content on their websites and DSPs may use this to show highly personalized creative.

A digital identity is the lifeblood of the advertising ecosystem and it is this identity that helps differentiate an online advertising medium from its mass channel counterparts. Identity is used in all stages of advertising – namely collection, identification, personalization and attribution.


Since its launch in 2012, Facebook Exchange has seen phenomenal success in delivering ROI to clients and the birth of several ad technology companies that help leverage the power of real time bidding to drive the very best out of FBX. RevX has been working with clients to scale remarketing on FBX through innovations in dynamic creative technology and advanced optimization algorithms since early 2013.

While FBX is a powerful channel on its own, there is more to remarketing on Facebook. For one, FBX does not allow for cross-device targeting i.e. being able to target customers who have dropped off the website on one device and showing relevant ads when they are logged into Facebook from their other devices. This is largely a limitation of cookie based user identification used by FBX ad partners to identify and bid on users in real-time. 


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.


Another day begins with customers expecting a further growth in their revenue and high quality performance. Our Retargeting Platform RevX is designed to keep all requirements at one place with a smart design to meet customer’s expectations.

We at RevX, understand the importance of transactions/visits and the ROI expectations of clients. So the built-in model and machine learning algorithms around CPC/CPT (cost per click/cost per transaction) aim to bid relevantly based on campaign, inventory and user signals. This helps in optimizing the campaign to drive required scale and quality visits/transactions at minimal RTB inventory costs from SSP’s.


We all know that ads personalized to each user drive better engagement and higher conversion rates. With the objective of strengthening its remarketing solution, Facebook recently launched the support for “Dynamic Product Ads” through their select marketing partners. RevX platform supports and builds on Facebook’s native dynamic ads solution to drive maximum performance for advertisers looking to remarket to users who have dropped off the website or mobile app.