This is part 1 of a 2 part series in leveraging data for Programmatic Direct.
Data powers programmatic advertising and can be a powerful tool for improving performance. We’ve built a guide to help with data basics and strategies for maximizing data for Programmatic Direct campaign success.
Types of Targeting Data
Most types of targeting data can be classified into two categories:
- Behavioral data: what the person did in the past and their inferred needs, wants, and future intent
- Profile characteristics: who the person is in terms of hard tangible characteristics, which don’t change frequently, if at all
What is it?
Behavioral data usually comes from the user’s actions and content consumption. This can include data collected from page visits, visits to the advertiser’s site, visits to other sites, ad clicks, videos viewed, apps used, games played, social posts, and more.
How is it collected?
The data is often collected via a DMP or DSP tag, which is served by publishers or advertisers using tag manager solutions. On mobile apps, instead of cookies, the data is associated with mobile device IDs: AAID for Android and IDFA for iOS.
- Maximize the use of available 1st party data with aggressive buys across a broad array of inventory, such as a Multi-publisher PMP or Targeted Exchange. For example, you can overlay 1st party behavioral data on top of OpenX Bidder, ComScore 100 or High Viewability deals.
- Apply filters to exclude site visitors who don’t intend to purchase the product. For example, exclude low income households from an “in-market for Tesla” line item.
- Carefully evaluate the quality of 3rd party “in-market” data and combine it with profile data to exclude accidental visitors to content pages.
What is it?
Profile data includes age, gender, ethnicity, family & parenting status, income, net worth, product ownership, political affiliation, and many other characteristics.
How is it collected?
It’s usually either collected online via site or app registration forms, or onboarded by connecting the person’s or household offline profiles to cookies and mobile device IDs.
Profile data, whether derived from online registrations or off-line sources, can be very effectively combined with contextual targeting or 3rd party behavioral data.
An example would be showing Tesla Model S ads on luxury car pages to users who are known to have high disposable income and confirmed interest in protecting the environment.
Ways to do this:
- Combine profile targeting with category-targeted Multi-publisher Private Marketplace and Targeted Exchange buys. Or, Single-publisher Private Marketplace and Real-time Guaranteed buys.
- Combine profile data with 3rd party behavioral “in-market” data for broad PMP and Targeted Exchange buys to exclude users who were accidentally added to the “in-market” profiles and who do not match the socio-economic characteristics of your buyers.
*Disclaimer: The above are suggested best practices to be used with discretion. Online behavioral advertising should follow all applicable Digital Advertising Alliance self-regulatory principles, including all opt-out requirements.
As a best practice, ask your data providers whether their data collection practices are compliant with all applicable data privacy laws and regulations.