GDPR has been in effect for a couple of years now, and CCPA has been in full effect since July. Device manufacturers and browser providers are sunsetting their cookies and advertising IDs, making it increasingly difficult to track user behavior to determine which ads to serve them, or when.
But advertisers ahead of the curve know that contextual targeting is more than just a privacy-safe alternative to behavioral cookie-based targeting. According to DV’s recent findings, 69% of consumers are likely to view an ad if it’s served with contextually relevant content, and 44% of consumers say they have tried a new brand after viewing it alongside relevant content.
Traditional Contextual Targeting
Most contextual targeting is done by setting keywords and analyzing web environments to determine relevant content to serve ads on. Such analysis can match ads with relevant content, but it has some limitations:
- Brands that don’t have the expertise or time to finely optimize keyword lists won’t get consistent results from their contextual campaigns.
- Over-reliance on keyword analysis can misidentify content as contextually relevant, as keyword analysis can miss crucial contextual differences
- For example, when “salsa” is mentioned, most keyword targeting tools struggle to differentiate between salsa as food and salsa as a dance.
- Alternatively, keyword analysis often misidentifies idioms or metaphors as markers for contextual relevance – it can flag an article that uses salsa dancing as a metaphor or example, even if the article isn’t about dancing at all
DV’s Custom Contextual Solution
Our Custom Contextual solution instead uses ontology driven by our Semantic Science engine. The difference between DV’s approach and keyword analysis is how content is processed, analyzed and classified. Instead of sifting through content to find individual keywords, Custom Contextual processes content by analyzing relationships between concepts – seeing how “salsa” is related to the rest of the content to determine if it’s about dancing, food, or something else entirely. Once it’s classified, the content is then categorized to make it easy to target.
Advertisers can determine contextual relevance by selecting our managed content categories that align with their contextual targeting strategy. Instead of manually refining keyword lists to ensure contextual relevance, DV manages each content category and regularly updates them without any input required from the advertiser.
The Benefits of In-Market Categories
One of these categories advertisers can leverage is our in-market categories. In-market categories don’t just classify content that’s contextually relevant, they target content associated with purchase intent. We do this by using our ontological technology to focus on several purchase intent concepts like: “Clearance,” “Offers,” or “Customer Reviews” within the framework of a larger content category.
For instance, the IAB category “Home and Garden” may return an article like this: A Breakdown of Contemporary Victorian Architecture. Our in-market category “Home and Garden” may return an article like this: A Review of the Top 20 Most Popular Gardening Tools.
The former is a commentary on architecture, while the latter is research material for gardeners looking for new tools. While both pages establish contextual relevance to a home and gardening brand that’s releasing a new line of tools, it’d be more effective to target the content similar to the latter article in order to leverage both contextual relevance and stronger purchase intent.
In addition to high precision, DV’s in-market categories achieve the necessary scale to drive effective advertiser goals. DV accomplishes this through applying our ontological and semantic approach to the vast inventory of internet pages we process via pre-bid targeting and post-bid measurement.
To learn more about DV’s Custom Contextual solution, click here.