By Andrew Carmody, CMO, ViewersLogic
Rarely is so much money invested in advertising today with so little evidence of return. Yet, that is exactly the gargantuan problem plaguing linear TV advertising despite a predicted 3.8% worldwide growth in ad spending to US$197.8 billion in 2022.
Quantifying the performance of linear TV ad campaigns is still largely based on analysing reach and frequency, which tells marketers little about the effectiveness of their spend and its contribution to driving the consumer along the funnel.
In the last few years, a handful of solutions have been created to inject enhanced outcome measurement into TV advertising but the trouble is, many of them are still questionable and inaccurate for the following reasons:
1. Fused Data Fizzles Out
Linear TV, like other media, is measured in a silo and to understand the effect of a campaign on another channel at play, marketers historically rely on data fusion. This involves grafting siloed data sets together in order to try to interpret the behaviour of the same user across different media. However, because each dataset is based on varying users and methodologies, it’s difficult to accurately match users across these divergent databases. Therefore, the results are often weak with tepid correlations.
2. The Probabilistic Pitfall of the Five Minute Attribution Window
Another way to measure TV campaign effectiveness is by looking at outcomes occurring within five to ten minutes of ad exposure. After recording a baseline of online activity, such as website visits, anything tracked above this point is attributed to the TV advertisement in question.
Waiting any longer than ten minutes means there is a risk of being unable to identify campaign impact when multiple instances of the ad may be playing on different channels.
The truth is, such a narrow attribution window accounts for roughly 1% of the traffic driven by TV advertising and omits longer term consumer responses that occur within one week of ad exposure. Rarely do consumers complete the desired call to action in such an immediate time frame. Furthermore, the five minute window does not take frequency into account and often over attributes the low rated high frequency channels. The conclusion? Over reliance on this probabilistic model – meaning that the people who saw the TV ad are probably the same people who went to the website within five to ten minutes, not definitely – results in inaccurate results that don’t take into account the full attribution picture.
Switching on a Solution
It is possible to truly measure the impact of TV campaigns but the route to TV attribution will not be found in neither older scientific models nor applying a performance-based digital approach. It has to start with an ambition to capture more holistic consumer insights than today’s siloed approaches afford.
By recording all the steps that make up a customer journey, systems could go beyond probabilistic data to follow the deterministic path to outcome, accurately crediting and re-investing in the real success levers. It is becoming increasingly apparent that the most accurate way to achieve this will be to employ single-source data: measuring media exposure over time for the same individual across all channels, in addition to purchase behaviour and location data.
When a TV buyer can capture ad exposure over multiple weeks or months, marketers will get a more detailed and reliable understanding of frequency, timeliness and what actually drove an outcome.
Such a goal is realistic and already in practice, demonstrating incredible results. But how can we measure its adoption? Perhaps by asking TV ad buyers, a year from now, to demonstrate the return on their US$197.8 billion.
