Home Analysis Delivery Infrastructure Boosting television prospects with enriched data analytics

Boosting television prospects with enriched data analytics

Share on

Think-hours-viewed-for-web.jpg
ThinkAnalytics illustrates the impact of content recommendation on engagement levels

Last September ThinkAnalytics published research showing that after integrating its Recommendations Engine product, its clients’ subscribers increased their viewing time by 20-50%, and the number of channels they watched rose by 25-35%. VOD uptake, meanwhile, increased by between 30-100%. These benefits were achieved just weeks or months after clients went live with its technology, ThinkAnalytics found.

Two months later, UK Pay TV operator Sky released figures showing that channel switching during Sky AdSmart’s targeted commercials was a third lower than during non-targeted ones. As Jamie West, Sky Media’s Deputy Managing Director, observed at the time, since viewers cannot distinguish AdSmart commercials from any others, higher viewing levels can only be attributed to customers finding them more interesting or engaging.”

Both findings eloquently demonstrate how enriched data analytics can boost television’s prospects and why data analytics is one of the most talked about subjects in the industry today as media companies make it a priority to get a better grasp on what their viewers and networks are doing. 

More data is becoming available to service providers and content owners that has commercial value. Andy Aftelak, Vice President and Director in Advanced Technology at ARRIS, whose Pay TV and broadband solutions cover the network, back office, headend and home, points out: “On a modern TV system, almost every button push is able to be recorded. You’re beginning now to look at being able to measure the way that people interact with a TV system in the same way that they interact with the Internet.”

Advanced Pay TV systems can access remote control commands to the set-top box and OTT video platforms such as Netflix (and broadcasters with direct-to-consumer services) can access server logs recording media player interactions. The maximum benefit is derived when such data is married with other information such as socio-demographic profiles, which can be derived from audience research panels at a macro level and household level subscriber data from Pay TV operators’ CRM systems. 

Steve Sydee, Head of Media Business Solutions at data analysis specialists Information Management Group Ltd (IMGROUP), says that while good data about what people are watching gives you good insight into what they’re likely to want to watch, you can ally that with lots of geo-demographic data to essentially make some more educated guesswork. This can then be fed back into search and recommendation engines’ algorithms to refine personalized suggestions.

Combined data-sets can be leveraged to help reduce churn, says David Mowrey, Vice President, Product Management at the multiscreen platform provider Clearleap, which was recently acquired by IBM. “If you can look at what a consumer is watching on any device, from whichever location, and whether that user then takes an action to either change their subscription, drop their subscription or consume more content over the next 30 or 60 or 90 days, then you can start doing some very interesting things. You can say, ‘Okay, this user’s habits are like that user, who is 20% more likely to churn than customers that watched two more videos last month. If I can just get this consumer to watch another couple of videos, I can reduce their churn rate by 20%.”

Such information can also be used to optimize content acquisition, points out Peter Docherty, Founder and CTO of ThinkAnalytics, whose content and recommendations engine services more than 130 million subscribers worldwide via its various Pay TV customers. “If you can really understand the preferences of the consumers, then you can actually use that to try to negotiate or look for content that you think is going to [better] match the preferences and the likes and dislikes of your consumer base compared to your competition.”

Better data analytics can improve TV advertising, too, partly because set-top box data and server logs – unlike audience measurement panels – register all instances of channel or programme viewing, no matter how small the number of viewers. Niche channels not previously seen as attractive to advertisers can be better monetized.

ARRIS’s Aftelak points out that the availability of data that includes detailed behaviour such as fast-forwarding means that “if you know where the ads are, you can figure out whether that fast-forward was somebody fast-forwarding the programme or fast-forwarding through the ads.”

IMGROUP’s Media Insights Evangelist, Suranjan Som, says this technique, dubbed ‘junction analysis,’ can help broadcasters look at “the impact of where an ad is placed in a break, what is the impact of that ad, how many people are fast-forwarding through it, how many people are zapping it completely, and so on. Where you have your campaign data, your advertising revenue, your cost information, and the advertising break information together in one place, you can do all these types of analyses, including campaign effectiveness.”

It is tempting to deduce that Pay TV operators – who manage their own infrastructure and are inherently able to exploit viewing data from their set-top boxes as well as CRM data from their subscriber management systems – are currently best placed to exploit data analytics. They may be able to achieve a virtuous cycle where QoE and network statistics can be used to improve content consumption, consumption data can be used to improve customer relationship management and CRM data can be fed back into improving network operations.

IMGROUP’s Sydee thinks so but suggests “many of them aren’t actually joining all those ingredients up in the right way, so they’re not at an advantage. A lot of the ex-analogue operators have got to work out where the information resides, then join it together in the right way.”

Eric Abbruzzese, Research Analyst at ABI Research, believes that while the advantage exists, it is not massive and may only be temporary: “Over the next year or so I think that [advantage] is going to shift more and more towards OTT and IP video as operators figure out ‘How can we glean more data from users, what can we get, how can we use it?’.”

Whoever benefits most, there is general agreement that if video service providers are not already implementing data-driven strategies, the time to do it is now. “We are definitely at an inflection point,” declares Mowrey at Clearleap. “If you don’t address this, you are not going to be competitive in the marketplace, either from a customer acquisition perspective or from a churn perspective.”

 

More reading

This is an edited excerpt from Videonet’s latest report, ‘Boosting television prospects with enriched data analytics’, which investigates how media companies can use a combination of viewing data, better programme metadata and network performance statistics to super-charge their content marketing and monetization, and strengthen advertising. It investigates what a holistic, enterprise-wide data analytics strategy looks like, with insights from Channel 4, Sky Media, ABI Research, ARRIS, ThinkAnalytics, IBM Global Business Services Division, Clearleap and IMGROUP, among others.

You can download the report (free) here.


Share on