AI-Based Behavioral Targeting Software
If you are a B2C or B2B advertiser, chances are that you have used and are currently using behavioral targeting or other forms of online ad targeting. These methods help you reach relevant audiences to drive awareness for your brand or a sales conversion for a product or service.
According to a recent report by eMarketer, compared to 2019, there are 600 million more people using the internet today to interact with brands. As more people turn to the internet to engage with brands, online behavioral targeting and other online marketing have grown to match this interest, with online advertising forecast to reach $626 billion by 2026.
With these trends in mind, what is behavioral advertising, and how can brands leverage it today?
What is Behavioral Targeting?
Behavioral targeting is an online advertising method that uses website or application data, clickstream data, form-fill data, transaction or purchase history data, or any other type of online browsing activity or event.
All this information is tracked to deliver targeted advertisements to users based on individual preferences and needs.
What Are the Benefits of Behavioral Targeting?
There are many pros and cons in online behavioral targeting. The top benefit of behavioral targeting for advertisers is that they can be more precise with their ad spend. Leveraging behavioral targeting allows advertisers to target specific customers or prospects, which will ultimately lower the cost of an advertising campaign.
Behavioral targeting also benefits the customer by delivering ads specifically to the most relevant person. This means the customer will get ads tailored to their preferences and more pertinent to their needs and interests.
From this perspective, behavioral targeting connects prospects who are most likely to purchase a product or service to the brands that are selling them.
The cons of behavioral targeting usually stem from where and how the data is collected. Bad data collection can result in poor performing ad campaigns, wasted ad spend, or bad user experience. Most importantly, inappropriate data collection could infringe on international laws and violate user privacy regulations, such as GDPR, the CCPA, or proposed U.S. legislation like the “DATA Privacy Act.”
With these pros and cons, it’s important to understand the best ways to create behavioral targeting segments that both avoid bad data collection and drive your campaign performance.
Creating Behavioral Audience Segments With Predictive Analytics
Leveraging predictive analytics is the single most impactful method for creating high-performance behavioral segments for your ad campaigns. Predictive analytics and advanced AI allow you to analyze millions of rows of customer data to find complex patterns that predict customer buying behaviors.
Based on likely customer behaviors, advertisers can segment audiences to categories based on predicted future activity, anticipated product purchases, etc. Four commonly used patterns used by brands today for database segmentation are:
- Upsell/Cross-sell Audience Segmentation: Predicting and modeling who will buy again and who will buy more.
- Customer Churn Audience Segmentation: Predicting high flight-risk customers.
- Customer Lifetime Value Audience Segmentation: Predicting who will be your best customers.
- Conversion Rate Behavioral Segmentation: Predicting which customers are likely to switch from a free trial to a paying subscriber or to move to a higher tier of service.
Why Is Predictive Modeling So Important For Advertisers Today?
Predictive analytics allows brands and advertisers the ability to create highly accurate lookalike audiences modeled on the behaviors of their most profitable customers, going beyond the basic capabilities built into ad platforms. Using your rich customer data, AI-powered predictive analytics can create predictive models and generate predictions about which customers will behave in the ways you’re most interested in identifying. You can then optimize campaigns using these highly accurate, reliable predictions.
Learn more about how you can use predictive analytics to optimize these critical KPIs for your business in our concise guide.