A recent study commissioned by SpotX from the Kagan Group gives us a foretaste of the future of online and television advertising in the United States (almost identical development worldwide). We should notice that in the United States, in 2017, digital advertising brought in more than 12,000 million dollars (an increase of 49% expected for 2021). This study was conducted on 41 MVSP (Multichannel Video Service Providers), creators or advertisers based in the United States.
First of all, let’s recall which types of ads are concerned by this study: We are talking here about video ads, which appear for example during a Youtube video, before a live Twitch and actually on many internet platforms. It is generally the same advertising format that can be found on television. We must therefore exclude from this study any other type of advertising that is not videos (banners… etc…). Note that despite their differences, these advertising formats often obey the same price model, i.e. a “cost per” system (thousand, click, fan, acquisition, etc.).
The development of internet, the media and content consumption habits have changed the world of advertising. Indeed, of the 41 companies surveyed, 100% said they had to completely change their marketing strategy. Several adaptations have therefore resulted from these strategic changes, and for the most part (80%), it has become imperative to be present on several platforms in order to reach all audiences.
Indeed, of the 41 groups surveyed, the vast majority (61%) consider that the two audiences are entirely different, and therefore require appropriate advertising. This is mainly seen among creators and suppliers (OTT and Pay TV) who consider, respectively at 69% and 63%, that the two types of audience should be treated differently. Strangely, the majority of advertisers consider the opposite.
And that can be partly explained by Big Data. Indeed the Big Data, this development of data created on the Internet and their use, convinced 85% of respondents, to take the step as for the adaptation of their advertising content, according to audience segments. The power of Big Data is now recognized worldwide, and the study shows that almost all respondents (94%) now consider the use of data to be the main engine for audience-based ad management.
On the other hand, even if most consider that Big Data is essential today, its use is less widespread. Kagan then distinguishes relatively different usage forecasts depending on the medium (Television or OTT). We can then see that the two media currently use about as much Big Data (TV : 30% / OTT : 39%), but that the use of the latter seems to be much more in vogue in the OTT milieu than on television. In fact, 51% say they will use Big Data within a year when only 39% say the same thing on television. Moreover, the number of sceptics is much lower in the OTT community (10% more than one year) than in the television community (39% more than one year or never). OTT services are more inclined to adapt to their audience than the traditional television environment. Note that advertisers are both the biggest users (or future users) of Big Data for television and the smallest users for OTT, marking the contrast between the two.
To understand the low use of Big Data (less than 40%) in both environments, the study proposes an observation of expectations and expenditures in both environments. Note that the OTT community uses Big Data more. Indeed, OTT platforms generally seek to create an experience allowing to create links with their customers, essentially created thanks to Big Data.
However, the study shows that the majority of groups currently have lower revenue expectations from the OTT. Indeed, among the OTT and Pay TV providers, only 11% think that OTT will bring in between 21% and 40% of their total revenues, against 50% who think that TV will bring in this income bracket. These statistics are related to the spending of advertisers who spend more on television than on OTT. Indeed 44% of advertisers invest between 21% and 40% of their budget in TV against 11% who do the same in OTT. It is therefore understandable that the higher the expenses in the OTT, the higher the revenue expectations from this environment, the more essential Big Data will be.
This is what most of the interviewees plan to do. The study shows that OTT will take over TV within 24 months in terms of spending and revenue expectations. Indeed, advertising investments (21–40%) in TV would then fall by 11% while those in OTT would increase by 56%. Similarly, expectations (21–40%) would also be revised up for OTT (+50%) and down for TV (-47%). Forecasts that are consistent with Big Data adoption forecasts.
Thus, thanks to Big Data, more and more groups are able to adapt their ads according to their audience, thus facing new media and new consumer habits. Many companies have already integrated Big Data, and those interviewed, have it or plan to use it, expect a lot if we look at the changing expectations in terms of ROAS (Revenue On Ad Spending) / YIEL. Indeed currently the majority would expect an increase from 6 to 10% when within 24 months the majority would expect an increase from 11 to 20%.
High expectations are associated with personalized advertising, for which Big Data seems to be the most appropriate.
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