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Behavioral Targeting

What Is Behavioral Targeting?

Behavioral targeting is the process of marketing to people who are most likely to use your products or services based on their demonstrated behavior. This behavior may be online (for example, keywords searched, websites visited) or offline (location tracking data, for example).


Why Is It Important to Have a Good Behavioral Targeting Model?

Behavioral targeting is the means of identifying the people who will use your product or service. In other words, a strong behavioral targeting model allows you to increase your return on investment in targeted campaigns.

What Internal Data Should I Have for a Good Behavioral Targeting Model?

Internal data that you should have in your model includes customer profiles and transaction history and your own websites’ cookies. Surveys and feedback requests from one-time or loyal customers should also be considered.

What External Data Is Essential for a Good Model?

Essential external data for a behavioral targeting machine learning model includes device and web data: for example, phone location data, passive app behavior like the amount of time spent on an app when, and IP data.

What External Data May Prove Useful for a Model?

Additional external data that may help your model includes demographic data connected to device IPs and data about ad view times on other websites.

Additionally, you can consider purchasing registration and subscription information from third-party data providers.

What Are the Main Challenges of this Use Case?

One of the challenges of building a behavioral targeting AI model is data privacy: more and more, countries and other polities are limiting the amount of information that cookies can collect. In fact, many browsers either have already or are currently developing cookie-less versions.

Once the information has been collected, however, there is still the challenge of creating useful and accurate customer segments. Gathering a wide array of relevant data is the first and most important step in the model-building process; deep learning and deep pattern recognition models can generate surprising results and guidelines for action but only with a good data source.

Interesting Case Studies and Blogs to Look Into

ScienceDirect: ICT Express: Identifying machine learning techniques for classification of target advertising
The Beginner’s Guide to Behavioral Targeting to Increase Conversions

Tangible Examples of Impact

In addition to display remarketing, we anticipate some disruption to display, native, or paid social advertising campaigns that utilize a robust audience targeting approach. This will make it harder to find new users, generate brand awareness, and personalize ads using the ways of the past.

Google Is Eliminating Third-Party Cookies — What Does That Mean for Your Hotel’s Digital Marketing?

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