Blueshift Predictive Scores empower you to focus more on creating engaging user experiences and less on rote spreadsheet analysis. This is done by applying and automating the best of the breed AI models to identify the most likely users to reach your custom marketing goals, all without any coding or scripting on your part. This is an advanced Blueshift capability, so contact your Blueshift Success Manager if you wish to turn this on for your account.

Marketing campaigns aimed at driving specific KPIs often involve targeting users who are likely to perform a specific action. For example, users who are likely to upgrade to a paid subscription, purchase a product, submit a form or download specific content. These desired actions are typically preceded by a flurry of activity indicating level of interest by each user in reaching that desired goal. Harnessing the latest in AI techniques, Blueshift can model sum total of user behavior, attributes and catalog interactions to predict likelihood of each individual user to perform specific actions and bucket them into precise segments. Using these precise segments, you can send very targeted and personalized messaging to nudge them towards specific parts of the site or apps that are expected to yield the highest follow through or conversion.

Blueshift offers the following predictive scores out of the box. Each of them can be customized to individual account data and desired business outcomes.

• Customizable Inputs and goals - You can select what events, user attributes and goals go into the models.
• Automated feature engineering - Blueshift's fully automated feature engineering takes away the grunt work of cleaning, de-duping and normalizing raw data to compute a rich set of features that feed into learning models. We also automatically remove noise features that do not contribute to the accuracy of the model.
• Automated Inference of training data - Blueshift's automated inference of training data from past user behaviors takes away the manual task of curating and labeling training data to feed the learning models. We do this by advanced sampling techniques over past user behaviors to select sufficient positive and negative examples for the desired outcome.
• Automated model building and validation - Blueshift's automated model building and validation uses state of the art learning models to build an ensemble of models that generalize well without overfitting.
• Easy to use outputs - The output score for each user is converted into a percentile rank between 0 and 100, allowing you to pick subsets of the range that best suits your campaign needs.
• Rich visualization - The model visualization, showing how the goal completion rates vary by each percentile, is built into the dashboard along with the relative importance of each feature.