Audiences are the main component to any advertising campaign. Modeling out the right audience for a campaign is vital. But it can also be complex and exhausting for advertisers to navigate. This data-driven approach for identifying and targeting audiences is an invaluable technique to understand and harness.
In this article, we will explore the concept of modeled audiences, as well as the use of AI and ML in modeling audiences, and the different types of models used in advertising.
What Is Audience Modeling?
Audience modeling is the data driven approach to identifying and categorizing groups of people into audience segments.. It involves analyzing data across multiple channels of information regarding audience persona as well as behavior and engagement data.. To be effective with audience modeling, you must develop a clear understanding of not only who your audience is, but also how they interact and engage with your messaging and brand. .
The proper balance of your industry-specific marketing strategy, data and analytics, demographics, interests, behaviors, and preferences is key to audience modeling. This leads to developing personalized content, communication, and advertising to ensure your business or brand meets your modeled audience’s needs. Your ultimate goal should be to drive customer engagement, boost conversions, increase revenue, and experience growth.
This process is extremely important in reaching audiences because it allows advertisers to create an effective advertising campaign that resonates with them. Advertisers can then use demographics, interests, behaviors, and preferences to develop a personalized campaign for each targeted group. This can create and build brand loyalty, stronger connections, and results.
Audience modeling is also important in advertising because it can identify the audience segments relevant to your product or service. Audience segmentation helps focus your advertising efforts, efficiently spending advertising dollars, increasing customer engagement, and realizing an ROI. Ultimately, advertising is more effective when your message is directed to your modeled audience and addresses their needs.
What Are the Different Kinds of Audience Modeling?
There are several kinds of audience modeling tools and techniques used in advertising. Although each type relies on data to create audience segments, some differences define each based on its purpose.
Predictive modeling is a technique based on statistics to predict and forecast the behavior of a particular group. It’s not only based on historical data but also current data to predict a future outcome. You can use predictive modeling to determine how your consumers will respond to an advertising campaign for a new product or service.
The process begins with collecting data. Once the applicable data is collected, predictions are made based on a statistical model. Most importantly, you must revise the model as additional data becomes available. According to Gartner, predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future. This type of audience modeling also includes models that look for subtle data patterns to resolve issues about consumer performance, such as fraud detection models.
Lookalike modeling is a technique based on creating a new audience segment with similar characteristics and behaviors of an existing audience segment. To use this technique, you must first identify people who look and act like your target audience. You then create advertising aimed directly toward this particular group of people.
To use lookalike modeling as a marketing technique, you must first collect data through audience segmentation, including but not limited to demographics and past purchases. Next, identify the characteristics related to your target audience and create a statistical model based on such. Finally, apply this model to the collected data and find people with similar characteristics to your target audience.
Contextual modeling is used to improve the effectiveness of advertising. It involves tailoring your advertising to your modeled audience to make the message as relevant as possible. Contextual modeling is widely used in advertising to improve the relevance and effectiveness of targeted advertising. The ultimate goal is to present information that’s interesting to your audience.
This type of audience modeling can also prevent your business or brand from advertising what could be deemed inappropriate or controversial, especially when analyzing web page content or social media posts. This is common with platforms such as Instagram, YouTube, Tik Tok, and Facebook. You can use this information to address issues or concerns and be socially responsible.
Using AI and ML in Audience Modeling
Artificial intelligence (AI) and machine learning (ML) in audience modeling is increasing with businesses and brands who see the benefit in audience modeling. AI and ML can improve the process of audience modeling by analyzing and interpreting data, whether it be large amounts or extremely detailed. Both help identify marketing trends, audience behavior patterns, and buying patterns previously difficult for humans to determine.
AI and ML are frequently used with predictive modeling, lookalike modeling, as well as contextual modeling. The gathered data is then used to create advertising campaigns that are more relatable to your modeled audience and applicable to either modeling technique. AI and ML can also help you with decision-making. You can use this data to create advertising campaigns customized to each customer’s needs and preferences. The ultimate goal is to increase customer engagement and revenue with an effective marketing strategy.
The beauty of using AI and ML for audience modeling is that the technology provides accuracy and effectiveness that are typically difficult for humans to create. For example, in digital audio advertising, AI and ML can help you develop a media intelligence strategy to leverage layers of data from various channels to create modeled audiences and audience segments. Digital audio is increasingly used in advertising to reach new levels of customer engagement and audience connection.
Businesses and brands are using AI and ML to better understand a target audience to optimize audio advertising efforts. Analyzing customer data gathered from digital audio platforms (such as Spotify, Pandora, or Apple Music) also gives a better understanding of the target market and listener behaviors. Using AI and ML to analyze this data helps create a more targeted and personalized advertising campaign that resonates with the modeled audience and drives business growth.
Audience Segmentation in Modeling
Audience segmentation is the process of dividing a larger target audience into smaller groups or segments based on demographics. This is essential in audience modeling because it helps make more relevant and targeted predictions for future advertising. It also helps improve the focus on your target audience. This results in efficient and effective advertising that attracts and retains the right customers.
Audience segmentation provides a more accurate prediction because the model created is tailored to each segment’s characteristics. This can increase sales and customer engagement. Audience segmentation helps make an audience model that’s more relevant to each particular segment, leading to customer satisfaction and loyalty.
You can use audience segmentation in your modeling strategy by defining your target audience, and then collecting data to identify pertinent characteristics of each audience segment. Develop a model for each audience segment and then test each model to ensure accuracy and effectiveness. Make adjustments where necessary. Finally, implement each specific model and monitor for effectiveness. Most importantly, be willing to make adjustments where necessary.
Explore Backtracks Today!
You can use Backtracks’ media intelligence capabilities to develop modeled audiences in many ways. This includes collecting data, shaping audio and media intelligence, or reimagining audio advertising and analytics. Backtracks can help take your media strategies to a deeper level of data insights and effectiveness. We invite you to explore Backtracks and partner with us.