CASE STUDIES

CASE STUDY

Indian Financial Services Conglomerate Saved 17% Month-on-Month while running a Cost-Per-Acquisition performance campaign.

Problem : Identification of overlapped audience, programmatic manipulation of attribution data. 

We approach the client with the narrative of hijacked attribution, and today our client save ~17% of their monthly spends by deterministically deducting for conversions that were either hijacked or misattributed.  

CASE STUDY

We audited the clients user acquisition data, and found more the 12+ sub publishers demonstrating the install farm behaviour. Upon blocking, the client saw no difference in the MRR , even after reducing the entire ad-budget by 20%. 

Indian Travel Commerce Company reduced the overall spends by 20% and saw no change in conversion volume and ratio.

Problem :Identification of sources plaguing the inventory with advertising fraud.

CASE STUDY

New York based consultancy assisted to build high dimensional attribution models for Ad Fraud Detection. 

Problem : Building non-rule based behaviour algorithms to detect impression and click level ad fraud. 

We approach the client with the narrative of building exponential technologies to answer the rising menace of advertising fraud. We deterministically identified 21+ mobile applications doing aggressive click level ad fraud. 

CASE STUDY

We audited the clients user acquisition data, and found more than 60% of installs exhibit an anomaly of CTIT of 60 seconds of every 4th Install driven through a particular ad-network and set of  sub-publishers. 

Indian OTT Company identified over 60% of Cost-Per-Install campaign is driven by device farms.

Problem :To identify invalid or non-human traffic in a CPI based mobile-app campaign

Com Olho is a leading cloud-based machine learning as a service platform. We make it easy for CXO's to understand their consumers using behaviour analytics, machine learning and data science. 

e : contact@comolho.com

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