October 25, 2015

Big Data and Digital Platform Analytics

Big Data Analytics

 Client Background

  • A UK based audience research, IPTV monitoring, and TV loyalty platform provider with installations across the globe. The client platform captures massive amounts of data (Big Data) about viewers’ content consumption
  • A tier-1 IPTV service provider based in APAC
  • Approx. 750,000,000 linear (regular broadcast) TV sessions
  • Approx. 300 TV channels
  • Approx. 35,000 TV programmes
  • Approx. 10,000 Video on Demand (VOD) titles
  • Approx. 65,000 VOD purchase-per-view

Client Objectives

  • How is transactional VoD (pay-per view, Video on Demand) performing?
    • Number and type of subscribers consuming VoD
    • Average revenue per subscriber
    • Consumption by various demographics, particularly by ethnicities
  • How can TV service provider drive uptake of transactional VoD?
    • Which linear channels and programmes should TV service provider use to promote VoD?
    • Which linear channels and programmes over-index for VoD users?
  • Is TV service provider pricing its content attractively and correctly?
  • How can TV service provider promote its content across ethnicities?
  • Where should TV service provider focus content acquisition efforts?
  • Categorize all content across tiers reflecting relative popularity and appeal of content
    • Determine what kind of content is more successful?
    • Determine genres and sub-genres where there is insufficient content to drive consumption
  • Carry out Data Discovery to uncover opportunities leveraging Big Data techniques

Technology Involved

  • Amazon Redshift (for Datawarehouse)
  • Hosted on Postgres DB (version 8.4)
  • SQL Query (for data extraction)
  • Scripting & Microsoft Excel (for data manipulation)
  • Microsoft PowerPoint (for final presentation)

Results

  • Identified customer segments that were under-penetrated and determined root-causes of low uptake.
  • Identified content that was working vis-à-vis the one that wasn’t.
  • Analysed sales by price points and recommended simplification of pricing strategy.
  • Helped client optimize planned marketing spend by recommending that age-group specific and gender-specific targeting is not required.
  • Saved manpower costs for client by providing with on-demand analytics.