What is contextual targeting?
Piano’s Contextual Targeting is created by leveraging machine learning, natural language processing, and data analysis to drive innovation in contextual targeting. It enables DMP clients to target and convert customers based on the content of a webpage in real time. Moreover, it enables publishers to categorize their content manually or automatic generation with DMP’s advanced natural language processing (NLP) capability. Piano DMP also conducted semantic analysis for publishers to ensure the accuracy of the keywords and that the most relevant ads are shown to the readers at the right time. All these are designed to help publishers get the highest revenue from their ad spaces.
Contextual targeting is not a new concept. It used to be publishers' default targeting method until behavioral targeting showed up, which tracks users’ online activities and personal data.
In the ever-evolving digital advertising landscape, marketers will face periodic challenges in effectively reaching and engaging their target audiences. Privacy is the latest challenge – and it’s a big one. The focus on privacy began in 2018 with the introduction of the General Data Protection Regulation (GDPR) in Europe. This was followed by similar initiatives worldwide, such as Apple’s App Tracking Transparency, Android’s Privacy Sandbox, and Google’s upcoming phasing out of third-party tracking cookies on Chrome by the second half of 2024; beginning for 1% of users as early as the first quarter of 2024. Having to navigate restrictions that govern the collection and use of consumer data has left marketers frantically searching for alternative approaches.
Contextual advertising offers compliant targeting at scale and, thus is emerging again as the dominant solution for marketers.
However, the conventional approach of contextual targeting is not sufficient today. Recent research undertaken by TPA Digital has found some contextual solutions fall short of meeting their basic campaign goals. Advertisers often find themselves wasting significant spending on ads that fail to reach their target audiences. Inaccurate keyword analysis, limited contextual understanding, and insufficient real-time optimization are contributing to suboptimal campaign performance (source).
The other issue is the growing problem of next-generation "content farms" created to hijack programmatic advertising revenue. An internet trust tool, NewsGuard, is tracking an increasing number of spammy, AI-generated news sites.
To solve these challenges, new technologies need to be leveraged to drive innovation in contextual targeting.
How does contextual targeting work in Piano?
The impending deprecation of 3rd party cookies by Google in the latter part of 2024 necessitates a strategic shift for publishers in targeting audiences. While some companies have initiated the collection of first-party data to directly engage their audience, a significant portion of publishers is yet to be fully prepared. Piano DMP offers a compelling alternative by enabling contextual targeting, allowing publishers to expose ads based on contextual segments without requiring user consent, leveraging the content of the webpage.
Piano's contextual targeting stands out for several reasons. Firstly, it is offered at no cost, distinguishing it from competitors like Permutive, where publishers are required to pay licensing fees for similar functionality, including the use of IBM Watson for accurate contextual targeting. Secondly, Piano's contextual targeting feature is entirely developed in-house, providing publishers with unparalleled access and control over the entire feature.
Thirdly, Piano's contextual targeting is not just another iteration of an existing concept; it embraces the latest technological advancements and industry trends. Incorporating machine-learning-based natural language processing, among other innovations, Piano ensures that publishers can deliver the most precise targeting to advertisers, thereby sustaining a steady flow of advertising revenue. This commitment to cutting-edge technology positions Piano as a robust solution in the evolving landscape of contextual targeting.
The two main use cases of contextual targeting are:
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For publishers: present targeted ads to end-users based on the content of a webpage (via Google Ad Manager)
By analyzing the keywords and themes of a page via Piano Natural Language Processing (NLP) capability, contextual targeting will help publishers provide more relevant ads for their onsite customers (even to the ones who do not consent to the data-tracking policies).
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For other verticals: present targeted and personalized templates/ recommendations/offers to onsite visitors based on the content of the webpages (via Piano Composer)
By analyzing the keywords and themes of a page via Piano Natural Language Processing (NLP) capability, this feature will help Piano clients identify the most relevant templates/ recommendations/ offers for their onsite visitors to personalize their experience and improve call-to-action conversion.
What are the new features introduced with contextual targeting?
Piano has introduced innovative features within its contextual targeting framework to empower DMP clients with enhanced capabilities. Utilizing Piano's Natural Language Processing, DMP clients can now safeguard their brand image by eliminating unsafe content and labeling critical material with the corresponding "Sensitive Topics" IAB category.
In anticipation of a post-3rd-party-cookies era, contextual targeting equips Piano DMP clients to decipher consumer needs more effectively based on the pages visited. Notably, this approach doesn't rely on user-level identifiers, allowing for targeted conversions of onsite visitors who haven't consented to the cookies policy through ads.
Moreover, contextual targeting facilitates the creation of personalized templates, recommendations, and offers for onsite visitors by analyzing the content of a webpage. For DMP and Composer clients, this translates into a dynamic and tailored approach to engaging visitors.
New Features Introduced in the DMP with Contextual Targeting:
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Contextual Segments Builder: A new tool empowering users to construct contextual segments tailored to their needs.
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Terms Attribute: A novel attribute amalgamating existing content profile attributes to identify matching pages based on elements such as location, concept, and person.
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Suggestions Tab: An intuitive feature providing clients with a list of keywords and respective scores (high, medium, or low) to enhance segment definitions.
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Matches: Clients can now review pages containing content relevant to selected terms, with the flexibility to remove sensitive or irrelevant content.
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Sensitive Topics IAB Category: An addition aimed at flagging pages displaying sensitive content, providing clients with the means to exclude material misaligned with their brand image.
Upcoming Feature (Planned for Q1):
Overlap: A feature designed to identify if an existing segment shares more than 50% similarity with a new segment, enhancing segment precision. The estimated release time for this feature is forthcoming.
These new features collectively empower Piano DMP clients with advanced contextual targeting capabilities, fostering a more nuanced and effective approach to content management and audience engagement.
Upon the feature's launch, reporting capabilities to assess the impact of contextual targeting actions will not be available.
What is the difference between contextual and behavioral targeting?
These actions can be achieved outside of Piano DMP. To make contextual targeting and other targeting methods work together, there are two ways:
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In Composer, clients could target users based on both contextual and behavioral segments by using the user segmentation card.
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Via the Ad Server (Google Ad Manager), advertisers could combine and target different segments.
One way to think about the differences between contextual targeting and audience targeting is that contextual targeting reaches all consumers in the context of particular content, with no ability to distinguish one consumer from the next, but audience targeting can more granularly reach individual consumers irrespective of context.