Turning TV habits into richer user profiles

More people are watching OTT than ever, but that doesn’t mean advertisers can reach them in the right way. How can we build a richer picture of who’s watching what, to take the TV value exchange to the next level?

The TV industry really needs to reimagine how it reaches audiences and offers service.

With 72% of SVOD subscribers seeing TV ads as ‘unacceptable’ (via Differentology), parties in the advertising chain need to start fiercely defending and differentiating their revenue streams: leaving users lost in a jungle of mindless spam and multi-screening just won’t work anymore… and neither will follow the status quo to segment in the same ways, or serve up the same old stream of content.

So let’s start solving this issue right at the heart of the TV-advertiser value exchange; the user profile. Profiles are the fuel for personalised targeting, evolving all the time in their richness and the multi-dimensionality of their attributes – mostly courtesy of digital media.

It seems ironic that television, the original mass marketing channel, is the last to become a full-fledged citizen of the profile ecosystem. But now, getting a deep understanding of TV watching habits offers unprecedented ways to build both a sustainable source of revenue and superior viewing experiences, through richer profiling.

This approach to profiling focuses on segmenting your existing audience in new ways, to get more from your current investments. Better yet, it broadens your targeting options so you don’t only have to rely on demographic data. It simply means using ready-made machine learning methodologies to extract valuable metadata and understand users from a different perspective. Let’s walk through how it works.

Who’s Sarah?

Meet Sarah. Your advertiser can’t trace who she is, but they can determine that she lives with her partner and two kids, has a steady high-level income and even that her car lease is about to expire.

This means it’s all go for addressable advertising: data on income, postcode, programmes watched, age group and these 3rd party characteristics give the green light for advertisers to target her with a new Mercedes Benz, ready to rent.

But this heavy reliance on PII (Personally Identifiable Information) doesn’t sit well with tightening consent laws surrounding consumer data, nor does it allow advertisers to monetise the full potential of her profile. There are deep gaps in the data.

So start to do differently: who really is Sarah, beyond her PII? How about harvesting implicit as well as explicit profile attributes attached to Sarah progressively via understanding exactly what she watches, and how?

Interests: Cooking & Climate Change

After a month of extensive metadata analysis, you start to understand more about Sarah based not on her personal information, but her personal preferences. TV operators can start to couple viewing behaviour (which programs she switched or stayed on) with an understanding of actual content: she skipped all sports but remained engaged throughout the UN Summit live stream, paying particular attention to Greta Thunberg’s speech which she replayed several times.

Through this advanced understanding of Sarah’s interests via facial recognition, image and speech detection within the content she is viewing, you can identify topics she likes and segment content. Then, you can scale targeted advertising to those who are particularly politically engaged or obsessively watch cooking shows.

Product Categories: Home & Garden/ Healthy Living

Beyond which topics interest her; the actors, politicians and popstars she prefers, service providers can really up the ante when it comes to contextual advertising capabilities.

Remember the fact you targeted Sarah with a Mercedes ad because her car lease was expiring? What you couldn’t know through demographic data and 3rd party attributes was her interest in climate via her news watching habits.

Instead, you can relate content she’s watched directly to advertising industry categories (IAB) through logo, object, and topic detection: you know her mind is on cooking and she’s been consuming content around smart homes to reduce her energy usage, so you can pair her current interests with a matching commercial.

This approach means reducing risk by spreading out inventory: although TV advertising was typically only accessible for large (Fast Moving Consumer Goods) brands seeking mass marketing, more granular targeting capabilities and extended audiences can open up this space to smaller players and offer untapped sales opportunities for service providers.

Responding to what’s inside screens with stronger experiences

By enriching existing user profiles in these new ways, operators and broadcasters are better positioned not only start adding incremental, refined value for advertisers but also personalise experiences in a whole host of new ways.

Through activating this scalable metadata, it’s possible to repackage the kinds of content viewers love in ways which surprise, satisfy and delight their consumption needs:

  • Correlate and stitch together shorter reels of content which reflect Sarah’s interests from across multiple broadcast channels and sources
  • Augment search capabilities by enriching criteria with topics, interests and public figures which go beyond what she’s actively chosen before – and instead serve up new results based on what she’s seen or heard

Turning viewing patterns into richer profiles

Inside video content, you can find all the hidden answers you need to better serve advertisers and re-imagine experiences. Here, you can go beyond demographic data to determine not only what a viewer wants to watch and how, but their interests, hobbies and product inclinations based on what they’ve seen and heard.

Through AI-driven insights generated through millions of metadata, TV service providers who build the user profiles of tomorrow will lead the way in reshaping what valuable consumer behaviour actually means.

November 12, 2019

Published by:

Roland Sars

CEO & Co-Founder