A Game-Changer for the Pay-TV Market
Data and artificial intelligence (AI) have the potential to improve performance all along the TV value chain.

Players like Netflix, Amazon, Apple and YouTube understood this long ago, creating a new business model by integrating the entire chain from content to consumption, connecting data from each part of the chain and using AI to maximize value. They have global, data-driven integration, fast, AI-supported processes and huge content investment power.
However, other pay-TV operators suffer from fragmentation: they focus on different parts of the value chain, different regions and different platforms and so lack integration, while deals between them are few in number and slow to be negotiated.
They are also under pressure from changing viewer behavior, the rise of professional pirate services and pure low-cost subscription services, the increasing cost of content rights, the difficulty of providing a compelling user experience with their existing infrastructure, and the fact that 75% of marketing directors feel they don’t understand their customers.
To address these problems, it’s not enough to have a data lake if the data aren’t properly documented and harmonized and can’t be used to answer business questions. It’s not enough to have lots of data scientists if they don’t report to the business, if they aren’t getting what they need from IT and if they aren’t building reusable models.
Pay-TV players need the facts that will help them determine and execute new initiatives and provide fast feedback on them: a “Try, Measure, Iterate” approach. To do that, they need to connect diverse data sources (cable, IPTV, CRM, OTT) and bring different teams together in a cross-functional way to solve business issues, using data to gain intelligence on critical aspects of the business.
This is where smart use of data and AI comes in. When a delivery failure happens, it helps identify the high-value subscribers affected and determine the best marketing action on a case-by-case basis. It helps deliver a real-time daily view of KPIs, the most important of which is usage. For content purchasers, it helps predict the value of a given package of content as a guide for negotiations. It helps detect the level of subscriber engagement and generate personalized actions to increase it. It helps measure the efficiency of marketing campaigns, in order to improve all future ones.
To sum up, our key recommendations for pay-TV players are as follows:
Cherish good data, especially on personal content consumption, while respecting GDPR and privacy constraints to maintain trust.
Invite your IT department to shift further into “service mode”.
Combine skills into an integrated center of excellence, in which data science and business intelligence are treated as cutting-edge expertise.
Foster a “Try, Measure, Iterate” mindset, instead of a fear of failure.
Put the matter in the hands of top management, where it belongs as a cross-functional issue.
Get support from a partner that understands the TV business, AI and core technologies, from custom solutions to vertical offerings integrating industry-validated intelligence.
Demand AI-triggered actions, not just information: this is where business KPIs start to improve.

