Online marketing and advertising encompasses a large swath of content on the internet. eMarketer reported that digital ad spending will account for 38.4% of total ad spend, aggregating $77.37 billion by 2017. When online paid ads were first introduced, ad placements were determined by the originating query. As media buying has revolutionized, marketers are now able to granularly target audiences using rich data, effectively delivering the “right message, right audience, right time” aphorism. Since the advent of programmatic advertising, however, the focus of online marketing has shifted to the “who” rather than the “where.” Contextually targeted ads are one of the newest programmatic movements, adding a third dimension of rich data to previously one dimensional targeting.
Rich data illustrates consumer behavior and provides insight into purchasing patterns, and overlaying several data sets contributes to powerful targeting. In the programmatic environment, marketers can target data sectors such as audience, behavior, retargeting, geo-targeting, cross-device and contextual.
Contextual targeting scans the content of a website for the editorial relevancy of keywords and then extracts the most relevant keywords or phrases to serve corresponding ads.
However, contextually targeted ads are subject to the caprices of a syntactical approach to audience targeting. Standard contextual targeting has a high mis-categorization rate because marketers can only contextually target through selection of predefined categories.
Consequently, ads are served in the wrong environment, resulting in truncated performance, wasted budgets and brand misalignments. Further, contextual targeting hyper focuses on audience data points and the subsequent segmentation so much so that it neglects differentiating the lucrative market niches.
A recent study found that 63 percent of consumers are more engaged and receptive of contextual targeted ads than non-contextually targeted ads. Contextual targeting must be adaptable to consider the ever-changing variations in context, but current contextual targeting technologies fail to deeply categorize a website’s content into a hierarchical taxonomy.
What does this all mean? Well, consider the automotive industry and the vast variety of accessories, car parts, models and brands that make up a car accessory e-tailor’s inventory. Traditional contextual targeting would extract the keyword “automotive” for example. In this instance, an ad could potentially be placed on any website that features automotive-related content even if that website doesn’t sell car part accessories. As a result, the contextually targeted ad is displayed on an irrelevant website, generating no leads and wasting a campaign’s valuable budget.
Despite the fallacies of contextual targeting, we believe that paid acquisition dollars are best allocated with powerful data overlay capable of identifying granular sub classifications within each categorical taxonomy – and that’s exactly why we’re successful at implementing effective contextual targeted campaigns.
Interested in learning how exactly our contextual targeted campaigns work? Contact us at firstname.lastname@example.org.