Predictive Modeling and Look Alike Targeting

Predictive targeting involves analyzing data to identify patterns and make predictions about which additional users are similar to an advertiser’s existing customer base or high-value users. The machine learning algorithm then identifies users who share these characteristics but have not yet interacted with the advertiser’s brand. This enables us to target other users who are most likely to convert, improving the efficiency of the ad spend and driving better results.

Look alike targeting, on the other hand, involves identifying new data audiences that have high concentrations of people with similar attributes to the people who already have taken a conversion action. This is done by analyzing data from first-party sources, such as website visitor data. Look alike targeting allows us to expand a campaign’s reach and target new, high-value audiences who are likely to be interested in their products or services.

Both predictive and lookalike targeting offer powerful tools for us to identify and reach specific audiences more effectively, driving better results and ROI for your programmatic media campaigns.