What are the emerging trends and technologies for audience targeting in programmatic?

Programmatic media buying is the automated process of purchasing and delivering digital ads based on data and algorithms. It allows advertisers to reach the right audiences at the right time and place, while optimizing their budgets and performance. But how can you leverage the latest trends and technologies for audience targeting in programmatic? Here are some insights to help you stay ahead of the curve.

1 Contextual targeting

Contextual targeting is the practice of placing ads on web pages or apps that are relevant to the content or theme of the page or app. For example, if you are selling sports equipment, you might want to target pages or apps that feature sports news, tips, or videos. Contextual targeting can help you reach audiences who are interested in your product or service, without relying on personal data or cookies. It can also help you avoid placing ads on inappropriate or harmful content, which can damage your brand reputation and trust.

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2 Semantic targeting

Semantic targeting is a type of contextual targeting that goes beyond keywords and categories, and analyzes the meaning and sentiment of the content. For example, if you are selling travel packages, you might want to target content that expresses positive emotions, such as joy, excitement, or curiosity, rather than negative ones, such as fear, anger, or sadness. Semantic targeting can help you align your ads with the tone and mood of the content, and create a more engaging and relevant experience for your audience.

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3 Behavioral targeting

Behavioral targeting is the practice of using data and analytics to segment and target audiences based on their online behavior, such as browsing history, search queries, clicks, purchases, or social media activity. For example, if you are selling pet supplies, you might want to target audiences who have visited pet-related websites, searched for pet-related keywords, or followed pet-related accounts on social media. Behavioral targeting can help you deliver personalized and timely ads to your audience, and increase your conversion and retention rates.

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4 Predictive targeting

Predictive targeting is a type of behavioral targeting that uses machine learning and artificial intelligence to predict and anticipate the future behavior and preferences of your audience. For example, if you are selling fashion items, you might want to target audiences who are likely to buy a new outfit for an upcoming event, such as a wedding, a birthday, or a holiday. Predictive targeting can help you identify and reach potential customers who are in the market for your product or service, and influence their purchase decisions.

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5 Identity-based targeting

Identity-based targeting is the practice of using data and technology to create and target audiences based on their unique and persistent identifiers, such as email addresses, phone numbers, or login IDs. For example, if you are selling online courses, you might want to target audiences who have signed up for your newsletter, downloaded your app, or registered for your webinars. Identity-based targeting can help you build and maintain relationships with your audience, and deliver consistent and cross-device ads to them.

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Emmet Light

Business Development Director
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Building these identity-based targeting lists can prove to be a separate challenge. Focusing on lead magnets, click funnels and ads that lead to form-fills will allow easier access to your audiences personal information (name, address, phone number) at scale.

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6 Omnichannel targeting

Omnichannel targeting is the practice of using data and technology to create and target audiences across multiple channels and platforms, such as web, mobile, social, video, audio, or TV. For example, if you are selling car insurance, you might want to target audiences who have visited your website, watched your video ad, listened to your podcast, or seen your billboard. Omnichannel targeting can help you increase your reach and frequency, and create a seamless and integrated customer journey for your audience.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Nikhil B.

is Open to Work
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Artificial Intelligence(AI) based targeting: Increasingly gaining attention and widespread adoption is the increased use of AI in programmatic advertising as it allows for more precise audience targeting. For example, if the CPG brand is looking at introducing a potential launch for a new health based drink, AI can accurately pick up audiences on the basis of past consumption habits, data on health based apps & predictions by analysing shopping habits and social media presence. As AI can analyze large amounts of data to identify patterns and make predictions about user behavior, it helps advertisers to deliver more relevant ads and save costs due to its effective buy rates.

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Emmet Light

Business Development Director
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Audience modeling can now be done by AI. Over the last few years, what was the time-intensive task of data scientists, now can be done quickly and accurately by AI. With a robust enough data set (10k+ inputs) an audience profile can be established using simple name, address and phone data. For example, an apparel brand might get a better idea of their target audience by running analysis of their existing customers NAP, or hashed emails through data sources pairing their customer list with behavioral, demo, geo and psychographic information to create a model. What's more, this apparel brand can then match that new audience profile with new unique and de-duplicated leads that match the model, then purchase them for future ad campaigns.

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What are the emerging trends and technologies for audience targeting in programmatic?

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1 Contextual targeting

2 Semantic targeting

3 Behavioral targeting

4 Predictive targeting

5 Identity-based targeting

6 Omnichannel targeting

7 Here’s what else to consider

Programmatic Media Buying

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