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How Viacom18 improved CTR 2.3x for Voot

Viacom18 Media Pvt. Ltd. is one of India’s entertainment networks and a house of iconic brands that offers multi-platform, multi-generational and multicultural brand experiences. A joint venture of TV18, which owns 51 per cent, and Viacom Inc., with a 49 per cent stake, Viacom18 defines entertainment in India by touching the lives of people through its properties on air, online, on ground, in shop and through cinema. VOOT is Viacom18’s flagship ad-supported digital video streaming platform.

VOOT, with 35+ Mn monthly active users (as on July 2018), has established itself among the top two premium video entertainment platforms. VOOT offers 50,000 hours of content with 40 hours of content added daily which includes TV Content, VOOT Exclusive content around popular TV shows, VOOT Original Web Series as well as Kids content.
Client Challenge
• Grow business with existing branding clients by addressing key requirements:
 High in-targets on demographic targeted campaigns
 Improved brand metrics through customised targeting and enabling measurement through Brand Lift Studies (BLS)

• Target new performance marketing clients by delivering campaigns optimized on performance metrics

• Offer customised advertising solutions and campaign insights:
 Leverage Insights tool to synthesise first party analytics and third party DMP data to enable value added solutions to drive loyalty of advertisers on VOOT and repeat business

MAVARiC (Management of Audiences at VOOT through Activation and data enRiChment) is VOOT’s Audience Management solution for key advertisers. To help achieve above objectives, VOOT designed MAVARiC, powered by Lotame DMP, in order to complete the following:

• Comprehensive Audience Taxonomy & Hierarchy for Digital Video
VOOT collated the needs of key advertisers, leveraging its experience working with 300+ advertisers in the Indian digital video ecosystem across sectors such as FMCG, Handset/OEMs, Ecomm, BFSI & Auto. This helped VOOT create a comprehensive audience taxonomy including segments based on Socio-Demographics, Behavioral, Interest/Affinity, Purchase Intent/Ownership, etc.

• Exhaustive data collection process
A flexible nomenclature was defined for 3+ Lakh behaviors to be collected from across VOOT platforms (such as Android & IOS Apps, Desktop web and Mobile web). Further, MAVARiC was integrated with internal analytics and consumer enrichment initiatives across teams at VOOT. This helped synthesize data of logged-in users (70 per cent of VOOT DAUs), data on engagement with content & ads on the platform and data inferred from VOOT’s internal machine learning algorithm for inferred demographics, interests etc. This was enabled through automated batch transfer process and product integrations.

• End-to-End automated management of viewership data
To enable continuously up-to-date segments leveraging 50,000 hours of content and 40 hours of new daily content on VOOT, a script-based automation was developed to manage and organize large volume of data in a scalable hierarchy. This seamlessly enabled 700+ audience segments.

• Enable insights for advertisers
To enable quick turn-around on key learnings for advertisers, VOOT developed a script for automated generation of templatized insights from Audience Profile Reports and DMP Insights reports.

Branding Clients:
Customised targeting solutions enabled for top advertisers from across sectors including FMCG & Internet services helped achieve business growth through:
 Advanced Retargeting: Enabled advertisers to target/exclude audiences previously targeted through earlier campaigns, based on various ad actions such as viewing or clicking on the ad and completing the skippable video ads

 Sector-wise affinity targeting: Enabled advertisers to target audience segments which have demonstrated a high engagement with ads of sectors such as Fashion & Beauty, Toys & Baby Products among others.

 Brand Lift Studies: Enabled advanced targeting and exclusion based on exposure of various ad units for targeting controlled & exposed surveys of brand lift studies.

 Improving In-Targets: 1.2x improvement in accuracy (in-targets) for demo-targeted campaigns through inferred Demo targeting.

Performance Marketing Clients:
Mid-campaign optimisation by leveraging learnings from campaign insights helped identify audience segments with higher affinity for client brands and optimise performance:
 Ecommerce Brand – Improved CTR 2.3x
 Media & Entertainment Brand – Improved CTR 2x
 Personal Care Brand – Improved CTR 1.5x
 Auto Brand – Improved CTR 1.3x

Shubhi Tandon

Shubhi Tandon is the Assistant Editor at Digital Market Asia. Fascinated by the evolving digital media industry, she has focussed on tracking developments in the Asia Pacific market since 2014.