The World is rapidly moving towards quality over quantity and it is no different in the advertising world. The translation from advertising into sales is a key factor when the Advertisers are looking to spend their budget. Providing the viewership metrics of consumers actually interested in buying their products sparks more interest for the Advertiser.
With the emergence of numerous OTT platforms, the consumption of live and recorded content has started to shift from linear to digital platforms. To stay relevant in the linear TV market, it has become increasingly important to provide advertisers with more targeted viewership metrics to attract them to continue to advertise on linear TV.
The current industry strategy of using estimate impression of a broader demographic group to drive sales is losing its sheen. These do not always translate into Product sales. This is where the targeted advertising plays a big role. Targeting a certain part of the demo actually inclined towards buying a product drive better sale.
Our client’s current process was build using highly aggregated reports and lacked flexibility and drill down ability. The Client was looking to upgrade to a cost-effective and highly scalable solution that would process data faster, provide insights at speed of thought, and enhance business agility.
Fugetron devised a cloud based, elastically scalable architecture that offers faster analytics and business agility in a cost-efficient manner.
Custom Pyspark, python scripts were used to retrieve and process the data from Nielsen’s proprietary API’s.
AWS Elastic MapReduce (EMR) was used to scale out data processing across nodes, and store processed data in AWS S3 storage.
Validation steps added to the data processing itself to log and report data anomalies
AWS Athena enabled the Reporting & Analytics users to access processed data for reporting & analytics need on large volume of Data.
Around existing 5 and 10 newTableau reports were integrated using AWS Athena
AWS Spectrum enabled theAnalysts to query the raw data in AWS Redshift as needed for deeper Analytics, leveraging Data Lake built by Fugetron in AWS environment.
Technologies used – Pyspark, Python, AWS S3, AWS EMR, AWS Athena, AWS Spectrum, AWS Redshift
Team size – 2 SMEs, 1 Architects, 3 Senior developers, 2 Developers
Redshift Cluster – 4 Node Redshift Cluster
Data size – Total 46 TB / 2 Bill clean Records
Project duration – 6 months
Project Governance – Agile delivery governed by Joint Steering Committee, Daily Scrum, Weekly Status Reports (WSRs and Monthly Product Feature review meetings
Delivery model – Hybrid
Results and Benefits: