TVadSync Taps Into TV Metadata To Detect Subtle Patterns In Viewing Behavior

You are what you watch.

One of the advantages of having access to the largest aggregate ACR TV viewing data set is being able to apply the latest data science to identify patterns within the data and to make predictions on consumer behavior.

TVadSync identifies “taste communities” hidden in billions of hours of television viewing data, similar to Netflix’s taste clusters. This is powerful stuff for brands who want to know “who’s my tribe?”. Marketers no longer have to rely on age / gender demographic consumer profiles, but can look to deeper behavioral and psychographic segmentation, based on how people spend more hours of their day than any other activity (sometimes including work!).

That’s television.

Brands can profile their existing customer base to identify high-likelihood prospects within taste communities — for planning contextual advertising such as linear TV; and planning addressable advertising such as digital/OTT/CTV.

Causal Attribution – How to Measure True Campaign Effectiveness

Determining the true relationship between control and exposed audiences is a problem that advertisers have faced for decades. We live in a time when unlimited amounts of media is being consumed. This is coming from a more diverse variety of sources than ever, so how can you know your advertising is effective?

The answer seemingly lies in the vast amounts of data now available to advertisers to analyze exactly where their ad was seen and the accompanying data on the exposed audience. Surely, with such large amounts of data we could easily determine what’s causing conversion and what’s not, but this vast amount of data poses another problem as it presents too large an amount to decipher the cause of true conversions. This is why causal attribution has risen to prominence as being the key in determining conversions.

Causal attribution refers to the reasons that people behave the way they do. It is a cornerstone of psychology and has also served as the bedrock for advertising, or more acutely why some ads work and some don’t.

We can think of how to perform truly effective causal attribution through the analogy of a medical trial. Imagine after years of hard work going through medical school and months of interning to long, sleepless nights in labs, and all your work manifests into a tiny single pill . You can finally hold in your hand a potential cure to diabetes. The clinical trial begins but the initial results are inconsistent and vary wildly, leaving you seriously doubting all those years of struggle. You think that some external factors are affecting the results, but how do you minimize the external effects on your group of control patients? How do you normalize all the factors governing a person’s life every day and at every moment? Ideally your test patients receiving the drug and control patients receiving the placebo would be identical clones of each other and do the exact same things everyday, but this simply cannot happen.

So how can we use causal attribution to solve this?

“In control vs. test studies, we aim to isolate a single variable in order to measure the incremental difference caused by this variable.  In our case this is being exposed to an ad, or not.  In order to isolate this variable, we attempt to normalize as many other variables as we possibly can.” said John McNicholas the Head of Product Management at TVadSync.

Effective causal attribution reveals itself to be more of a line of elimination as you work progressively towards the actual effect of the advertisement. In doing so we see the strength of true causal attribution lies not in the chosen variable itself necessarily but in the strength of the normalization of other variables.

So in the case of our diabetes trial we can change one variable for the patients (the drug), while keeping the rest of the behaviors as identical as possible, and in the case of measuring TV ad exposure, companies try and replicate a control audience to measure how effective their ad was in driving conversions. Some bias may affect how a control audience is created out of first party data, whereby a control audience is created that favorably skews the results.

 

Third party data vendors such as TVadSync use new forms of machine learning analysis to analyze a myriad of TV viewing behavioral indicators such as the propensity to watch a TV network, genre, actor or an extensive number of other content consumption variables. This allows the creation of exceptionally similar test and control audiences to accurately measure the effect of TV advertising.

 

TVadSync can directly detect what people are watching on their Smart TV with 1:1 accuracy, and in doing so solve the problem of determining causal attribution by allowing the ability to narrow down each variable while normalizing every other possible variable within TV viewing behavior. It is only through modern smart reaching TV data companies such as TVadSync that this true causal attribution can be understood, and in turn the effectiveness of TV advertising can be understood.

How TV ACR-based attribution measurement can help save TV

TV advertising – reverence turning to doubt
While it was, for decades, the medium for advertisers wishing to make an impression and win the hearts and minds of customers, TV has recently undergone a little bit of a crisis of confidence. Let’s not get confused here – TV is still an effective medium for shaping a brand’s message and customers’ perceptions in a meaningful way. According to a study performed by the Advertising Research Foundation relatively recently, TV is still “the most effective vehicle for driving ROI, and adding digital to a TV campaign yields a +60% kicker effect”. But observations made by eMarketer suggest that, where ad-spend is concerned, TV is suffering. Its share of allocated media budgets over the course of the next few years is set to steadily decline, resulting from a combination of increases in cord-cutting and an ongoing battle with digital ads, generally seen as more accountable. But for large brands, with correspondingly big media budgets, even small improvements in efficiencies pertaining to spend-placement can result in big cost-reductions and increased revenue. So the growing issue is TV performance becoming more measurable and accountable, matched with better-defined audience segments for perfect targeting of ads.

 

How brands measure TV ROI
So why has this been a problem? For the most part it’s down to the disconnect between TV and the rest of the customer path-to-purchase. Traditionally, if a brand wanted to gauge how well its TV campaigns are performing and to what degree they influenced consumer conversions, there were only a few (limited) options available. Sometimes an advertiser would simply observe KPIs, such as site visits or purchases, after a campaign had run and make a note of any correlation. But this method has always been a less-than-perfect approach, with the door being left open for other variables to impact customer behaviour. For example, viewers can now record shows and watch them later, meaning ads they might collectively see are spread across a variety of times, rather than concentrated in once are. Drawing correlations in this instance can be very, very difficult. Alternatively, if a company wanted to discover how TV changed perceptions of its brand and whether equity was added to, they might turn to using audience panels. But the issue with this has been that making sure the panels are representative of populations, and that audience responses actually matched their behaviour. To compound problems, the proliferation of viewing methods (e.g. DVR views, on-demand, streaming devices, OTT, set-top-boxes) has created a lot of siloed data sources meaning pulling together measurement data in a coherent way is made ever complicated.

 

TV ACR-based measurement can help provide the solution
All of this has resulted in TV appearing to be a difficult proposition as it relates to accountability. Marketers simply don’t know for sure how measurements stack up and how it’s impacting lower-funnel activity, and it can give them the heebie jeebies if it means sweating over large TV budgets being misplaced. Digital, on the other hand, gives instant feedback, spend can be modified in real-time and conversions can be tied more directly to campaigns. So how can TV compete with this? Truth is it doesn’t have to anymore. TVs can now account for what content (e.g. ad spots) households see and, through data-matching techniques, this can then be tied to subsequent customer behaviour much more deterministically. In essence, TV is now a part of the digital path-to-purchase and its place in this increasingly visible. Automated content recognition technology means TVs can read what’s hitting their screens, meaning every TV produces data, collectively leading to a more holistic data source. This makes building a picture of viewing measurements easier than trying to grab data from myriad different places. All of this means networks can provide ever more clarity on ad-performance for brands, as well as incredibly well defined audience segments, meaning TV is very much levelling the playing field with digital. Forget that – TV is becoming digital now. New-age measurability and old-school emotive power will help it remain the platform to use for advertisers and, in combination with online campaigns, should still be the winner of hearts & minds to turn to.

 

https://www.forbes.com/sites/danafeldman/2017/09/13/tv-ad-spend-drops-as-cord-cutting-in-the-u-s-accelerates/#232377842c5a

http://uk.businessinsider.com/olympic-streaming-shows-data-on-digital-tv-audiences-is-hard-to-track-2018-2?r=US&IR=T

Secured By miniOrange