Choosing Between Contextual Advertising and Audience Targeting – Which is Better?
Choosing Between Contextual Advertising
and Audience Targeting – Which is Better?
For marketers, choosing contextual advertising over
audience targeting or vice versa has always been dilemmatic. Contextual advertising
involves placement of ads on the basis of the content the user is engaging with,
whereas audience targeting involves use of cookies to track users and display
ads. Here, we will try to provide a solution to this dilemma. We will consider
the data available from different studies and draw a conclusion.
Balancing audience and context is difficult for the majority
of marketers, shows a survey conducted by Sizmek. The solution to this problem
is not just as simple as targeting both audience and context, as for 8 in 10 of
the survey participants, targeting both has a negative impact on the advertising
campaign.
90% of the participants said that they expect to
increase the scale of their contextual targeting and find new audiences. 80% said
that in the coming year, it will be either a critical or high priority for them
to achieve both audience and contextual targeting at scale.
For the direct response campaigns, 42% of the
participants said that their targeting strategy involves only audience or gives
more importance to audience over context, while 37% said audience and context
are equally important. 21% said that their targeting strategy involves only context
or gives more importance to context over audience.
For the brand campaigns, 37% of the participants said
that their targeting strategy involves only audience or gives more importance
to audience over context, while 35% said audience and context are equally
important. 28% of the respondents said their targeting strategy involves only
context or gives more importance to context over audience.
75% of the respondents agreed that deploying artificial
intelligence in advertising increases the performance of the ad campaigns. 81% said
that they plan to increase the use of artificial intelligence in advertising.
With the coming in effect of the data privacy
regulations such as the General Data Protection Regulation (GDPR) and the
California Consumer Privacy Act (CCPA), and the gradual phasing-out of
third-party cookies in Chrome by Google, a major shift from cookie-based
audience targeting to contextual advertising has begun.
77% of the survey’s participants anticipated that the
GDPR and other privacy laws will make it harder for marketers to target
audience through the means of third-party data. 80% of the respondents said
they will improve their contextual targeting.
Industry experts think that AI-powered contextual
advertising will become the preferred mode of online advertising as it does not
rely on users’ data.
Another study published on The Drum compared
contextual targeting to audience targeting in terms of attention, cost and
brand lift. It found that the average time spent by individuals on ads in the
case of contextual advertising was 12% greater than when audience targeting was
used. On a CPM basis, audience targeting cost was found to be 4.7 times more
expensive than contextual targeting. Apart from the data costs, the media costs
incurred in the case of contextual targeting were 6.3% lesser than audience
targeting.
Brand lift per second of attention was 2.1% in the
case of audience targeting, while it was 3% for contextual targeting. At developing
brand impact per second of attention, contextual targeting came out to be 43%
more efficient than audience targeting. The study found that for every dollar
spent, contextual targeting is 7.5 times more efficient than audience targeting
in achieving brand lift.
From the above two studies, two conclusions can be
drawn – firstly, contextual advertising is much more effective and efficient
than audience targeting, and secondly, marketers are increasingly considering contextual
targeting, especially AI-powered contextual advertising.
However, most of the AI powered solutions currently
available in the market depend on the use of machine learning (ML), natural
language processing (NLP) and semantic analysis. These solutions fail to truly
understand the sub-text, nuanced contexts, and complex relationship words have
in written or spoken language.
AI-powered contextual advertising solutions that use
computer vision are far more effective and accurate, offering a higher degree
of context relevance that overcomes the limitations of traditional keyword
targeting and NLP based technologies. Computer vision enables detection of
contexts in video such as faces, emotions, logos, objects, scenes and
activities with high accuracy, thus allowing advertisers to display in-video
ads that are in line with what the user is actively engaging with. Computer
vision powered contextual advertising allows brands to achieve unparalleled user
engagement, reach and ROI.
The benefits offered by AI and computer vision powered
contextual targeting are not limited to boosting performance of ad campaigns,
but also include successful resolution of the grave problem of brand safety.
Accurate detection of unsafe or inappropriate contexts in video enables
marketers to effectively avoid any sort of undesired ad placement, thus
ensuring brand safety.
For marketers seeking an advertising strategy that is
compliant with privacy regulations, cookieless, highly effective and brand
safe, AI and computer vision powered contextual advertising surpasses audience
targeting.
