Data Science for Broadcast Media & OTT
- Audience/Viewership Measurement & OTT delivery-side web-analytics
- Advertising Revenue Management through
- Ad-Deal Proposals Inventory & Pricing customization
- Spot / RO revenue optimization
- Channel content scheduling/programming & promo planning
- OTT recommendation engine
What we don't do:
What we do:
Transformative Impact of Media Analytics
- Selling right inventory to the right advertiser at the right price can easily result in 10% revenue gain
- Optimized allocation of daily FCT to ad-spots & correct placement in breaks, ensures that the right advertisements reach the right audience segments at the right time, significantly increases ad revenue and effectiveness
- Automating content scheduling/programming ensures rights utilization, avoids viewer fatigues and delivers variety to retain/increase viewership
- OTT content recommendation helps retain viewers on the platform, customize experience and increases subscription revenue
Other areas in Media Analytics
- Social media analysis to integrate viewer feedback into content development, improving reality content (quiz shows, matches, sports)
- Promoting right shows at right time, on resonating content helps increase viewership. Managing promo placement is an optimization problem!
Case Study
Ad Revenue Optimizer
Problems & Objectives
- Advertising proposals are traditionally aimed at clinching the deal with limited view on inventory fill, other client deals, prices etc.
- Result: servicing issues; imbalanced inventory fill; false promises; makegoods
- Develop a system that has near-realtime visibility into the inventory fill status
- Customize prices & inventory allocation to meet advertiser needs while ensuring servicing of the deal
- Ensure business rules, pricing rules etc are met, violations are highlighted and approval processes streamlined
- Provide good visualization, post-eval & makegoods
Solution
- A complete software integrated with BMS/traffic system
- Viewership ratings database
- Intelligent module for ad-proposal optimization
- Approval workflow
- Inventory visualization
- Price monitoring & setting
- Intelligent makegoods
Benefits & Results
- Advertiser-specific price variation and demand-driven pricing of inventory
- Planned inventory overfill to manage day-to-day demand (RO) variation & avoid wastage
- Reduced servicing issues and make goods effort
- Improved pipeline visibility
- Sales executives performance tracking
- Negotiations history tracking for future reference
- Improved handling of Make goods
- Streamlined, accurate and faster billing
Case Study
Ad Spot Optimizer
Problems & Objectives
- Duration of spots to be allocated in a break usually exceed available inventory – which spots should be aired and which should be dropped – a decision made every single day for every program/break aired
- How do we ensure maximum revenue in the above process?
- Where can the dropped spots be allocated?
- Reduce human effort in this activity and enable inventory fill till the very last
- Perform the above task in few minutes
- Allow ample human intervention, overrides etc.
Solution & Benefits
Designed and developed a software with following features
- Assured allocation at spot, brand, advertiser, deal level
- Even distribution of spots of different brands, products, clients when the rates are same
- Long term even distribution of spots across day-parts from each deal
- Placement of spots to ensure higher viewership to higher valued spots
- Maximizes revenue ( 2-4% incremental gain)
- Automates the spot allocation process
- Respects FCT Caps & all allocation rules & deal conditions