Programmatic advertising campaigns can be successful when ads are displayed to interested and ready-to-buy audiences. This is achieved through accurate audience data targeting. In fact, a recent report found that aligning ads with the right content can increase search intent by 7% and brand relevancy by 6%, demonstrating the importance of high-quality audience data in personalizing ad experiences and maximizing effectiveness.
In this article by SmartyAds’ Enterprise Team, we will delve into data targeting and explore the various types of data used for audience targeting, including their sources and examples. Data targeting is a strategy where marketers use data to target specific audience segments with personalized ads. This targeting can be based on demographics, location, company industry, technology stack, and purchase intent.
It is worth noting that data targeting and audience targeting are not interchangeable terms. Audience targeting groups audiences based on specific criteria such as online behavior and interests, while data targeting uses audience data to target them with relevant content and ads. However, both have similar benefits in data-driven marketing, particularly in matching ads with audiences interested in them for high-ROI advertising.
Main types of data for audience targeting in programmatic advertising
There are three main types of data for audience targeting in programmatic advertising:
- First-party data targeting: This targets an audience based on data collected from web visitors interacting with your website and brand. This data is collected through their interactions with your various sales and marketing touchpoints, also known as customer data. First-party data helps create ideal customer profiles based on accurate information validated at the source.
- Second-party data targeting: This involves using data that is shared between two different companies for the purpose of targeting common audiences with personalized ads. However, to access and target another business’s audience, a direct agreement with them is necessary, which may be challenging if GDPR applies to the audience.
- Third-party data targeting: In this type of data targeting, data collected by third-party companies and sold to advertisers is used. Examples include targeting new audiences with Facebook or LinkedIn ads. Third-party data sources include data management platforms, marketing automation tools, and consumer research firms.
While these types of data can be beneficial for audience targeting, it is important to note the potential privacy concerns that can arise when using this data. Advertisers must comply with all relevant laws and regulations when collecting and using data for audience targeting purposes.
The most effective audience data targeting strategies for programmatic ad campaigns
To create effective programmatic ad campaigns, it’s not enough to just segment your audience. There are four extra steps you can take to ensure that you’re targeting the most accurate audiences possible.
- Build Lookalike Audiences:
Lookalike audiences are created from a “seed audience” of existing or likely customers. This allows businesses to significantly increase the reach of their advertisements using data without compromising precise targeting. Lookalike audiences connect advertisers with prospective customers who may exhibit similar traits and interests, improving ad performance.
- Create Custom Audience Segments:
Custom audience segments group audiences according to a unique criterion, creating a personalized ad experience for the audience. Machine learning is used to search databases with millions of user-profiles and choose the desired qualities. For example, a clothing retailer might create a custom audience segment of female shoppers between 18 and 34 who have previously made a purchase online.
- Combine Contextual Targeting and Audience Targeting:
Contextual targeting shows ads on websites with content related to the advertisement’s subject matter, while audience targeting uses various data signals to target specific audiences. Combining both strategies can increase return on ad spend by up to 10 times. An approach called “multi-touch attribution” is suggested as one efficient way to combine contextual, behavioral, and audience targeting capabilities.
- Implement a Mobile-First Ad Strategy:
Mobile device usage trends provide an increasingly rich source of data for more accurate audience targeting. Mobile data can be used by advertisers in numerous ways and is ideal for performance-based marketing and branding objectives. For example, a company might use a programmatic advertising platform to target users who have previously shown an interest in their products with personalized ads when they visit mobile websites or apps.
Summary
By using data targeting, brands can ensure that their ads are seen by the right people at the right time. This approach not only increases the effectiveness of advertising campaigns but also reduces wastage by avoiding targeting people who are not likely to be interested in the product or service being advertised.
The white label DSP from SmartyAds also includes a call-to-action (CTA) feature, which encourages users to take action after viewing an ad. This can include clicking a link, making a purchase, or completing a form. By including a CTA in their ads, brands can further increase the effectiveness of their campaigns and drive more conversions.
In addition to data targeting and CTA features, the SmartyAds white-label DSP also provides businesses with detailed reporting and analytics. This allows brands to track the performance of their campaigns and make data-driven decisions to optimize their advertising efforts.
Overall, using SmartyAds’ white-label DSP can help businesses achieve audience precision in their programmatic advertising campaigns. With data targeting, CTAs, and detailed reporting, brands can optimize their campaigns for maximum effectiveness and ROI.
