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3 Step Program to Customer Targeting

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Businesses, today, are turning to customer analytics to define and predict customer behavior. Most businesses represented by publishers and advertisers rely on demographic data to target consumers online. This is required to associate a product to a particular audience as defined by its demographics (age, gender, income) and interest data.

Below is a three-step method  for advertisers and publishers to reach their target audience:

  1. Collect User Behavior 

User behavioral data is usually collected through web browsers and video/audio players. Scripting languages such as JavaScript or Flash action script can be used to collect information related to browser, IP and content consumed by the user.

This information can be categorized as strong or weak. For example, video player, tags or GPS-based location information can be categorized as strong and  IP address, browser type, or login patterns as weak.

  1. Audience Segmentation  

The next step is the classification and identification of users by their interest or demographic characteristics.

Users can be broadly classified by the following attributes.

>Interest (media content)

>Behavior (source, location, region)

>Demographic (age, gender, income, company)

This data can be used for machine learning model. Machine learning algorithm correlates user behavior to a specific interest. Users can then be targeted by using a combination of observed behavioral data.

  1. Interest and Behavior-Based Targeting
  2. Online behavior and the kind of media content consumed is required to predict user interest. The audience segmentation model identifies relationships between interest or content categories. The affinity rule increases the penetration of the ads campaign beyond the observed data.
  3. Interest-based advertisements, also sometimes known as personalized ads, are displayed based on information from online buying and browsing interacting patterns.
  4. Demographic-based Targeting
  5. The registration process can help to obtain demographic data such as age, gender, income or place of residence. The combination of behavior and demographics data is used as input to the Machine learning algorithm. By affinities rules regarding interest and demographics the machine learning algorithm increase number of users for ads targeting.

 

  1. Deliver Ads in Real Time

When all this information is collated, then the next step is to deliver advertisements in real time.  The trend in real time advertising is already visible and businesses can push dynamic content advertisements, across platforms and in a social environment.

Thus, once the target audience has been defined and the ad content is formalized the power of the Net takes over.  By doing this, advertisers and publishers will move away from creating perfect messages to creating perfect brands.

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