Making sense of viewer data Genius Digital Products

Understanding subscribers across every screen 2 Act upon data with specialized applications Optional capture agents provided where existing systems cannot provide necessary data Analyse and interpret data with industry leading analytics Consumer Insights Platforms Set top box Agent Marketing team Sales team Retention team Ad sales team Operations team Content team Product team Smartphone app Agent Web site Agent Connected TV app Agent CRM data EPG Data Research data Channels / Packages CRM system DM system OSS/BSS Recommendation Engine

 Identify viewing of channels and programmes by different segment of the subscriber base: top 10s, most preferred, and most watched  Optimise channel packaging to minimise costs and increase propensity to upsell  Provide details consumption information on channels to channel providers to reduce carriage fees - includes household demographics, PVR usage  Leverage clusters of programmes and channels that frequently perform well together for packaging and upselling  Quickly identify under- performing programmes and channels and act accordingly Content Insights 3

 Identify the strongest viewing preferences of the subscriber base and leverage this for marketing campaigns, content commissioning and acquisition  Identify key viewing behaviours that drive loyalty and build marketing campaigns around these areas  Understand the viewing habits of subscribers who take-up new services, PVR, HD, VOD, and OTT and optimise marketing and content accordingly  Measure the impact of marketing campaigns on subscriber viewing and consumption Subscriber Insights 4

 Target individual subscribers based on their viewing patterns and preferences  Identify those at risk of churn based on similar viewing patterns from those who have previously churned  Integrate personal viewing summaries into call centres to improve the customer experience  Reduce retention cost by identifying those least likely to churn based on viewing of exclusive content Personalised targeting and retention 5

Ad optimisation 6  Individual reach and frequency measures for each ad slot and campaign according to demographic measures  Improve ad sales revenue based on actual audiences  Measure the impact of PVR and VOD on ad viewing  Identify key viewing characteristics of a third party database, e.g. purchasers of a particular brand

 Compare viewers of individual promotions with those of promoted content  Identify best performing slots, copy, and programmes to place promos in  Identify success of promoted VOD, OTT, and linear TV services Promo booster 7

 Identify most commonly use keys on the remote control and how they are used  Set-up and analyse conversion funnels for PPV and other services  Measure the impact of new user interfaces (and A/B splits) on viewing habits UX Insights 8

 End to end performance monitoring from set-top-boxes, mobile devices, and PCs  Identify households, devices, channels, VOD assets, and network segments in error in real time  Bring real-time data into the network operations centre to provide immediate corrective actions  Analyse trends in quality to identify problematic devices, networks, and third party suppliers  Full compliance with TR-160 and complimentary to TR-069 and TR- 135 Realtime QoE monitoring 9

Individual viewer model Predict viewing for individuals rather than households Compare reach and frequency on your platform with industry currency measures Identify individual habits within pay TV households to personalise marketing to an individual level Non-connected devices Predict the viewing patterns and behaviours of unconnected devices from the behaviour of connected devices Leverage data for marketing across the entire subscriber base, not just connected devices Make decisions based on representative data on the whole subscriber base Minute by minute ratings Key industry metrics that are used for ad trading: share, rating points etc... Minute-by-minute resolution of channel and programme viewing Other key analytics 10