Blueshift provides numerous standard out-of-the-box predictive models, including conversion, engagement, churn, and retention models. Blueshift also provides the flexibility for marketers to create their own custom predictive models that are tailored to meet their specific business requirements.
Blueshift also offers channel engagement predictive models to better understand a user's propensity to engage in a specific channel. Blueshift provides channel engagement predictive models for the following channels: email, SMS, push, and in-app.
Blueshift’s approach to predictive intelligence is to be fully transparent and customizable. You have full visibility into which user attributes and behaviors impacted the model the most and can track how the performance (i.e. conversion rate) of the predictive score changes over time. You can also customize models to your unique business requirements as required.
Blueshift’s AI-powered predictive models are trained on historical data to learn behavioral rules, which separate converting users vs non-converting users. Our platform then automatically derives user behaviors from user clickstream events and campaign engagements. Specifically, we derive hundreds of behaviors such as recency, frequency, time spent, catalog affinities (category affinity, brand affinity, product attribute affinity), etc for each event. These behaviors, along with user attributes, are referred to as features/variables and are fed into the AI model. The model then learns an optimal combination of features, which leads to conversion and a scoring function is learned.
Blueshift provides a number of out-of-the-box predictive models. It also provides the ability for you to create new predictive models and customize existing models based on business-specific goals via an easy-to-use interface.
Data analysis is automated and cleaning, de-duping, or normalizing of raw data is not required to get the training data ready or to remove noise from the models.
Predictive models, as well as the scoring against the models, are deployed and updated automatically as new data becomes available. Predictive scores are refreshed on the user profile on a daily basis, whereas the predictive models are re-run and optimized on a weekly basis.
By looking at all of the behaviors across every user, Blueshift’s predictive scores can understand and predict users' intents and propensities to take certain actions. Once computed, predictive scores are immediately available throughout Blueshift’s platform, including within the user profile, segmentation, recommendations, campaigns, and communication templates.
Blueshift provides you with the ability to bring your own in-house models and scores as custom attributes on the user profile. You can then use your in-house model scores in the same way as you would use Blueshift’s predictive scores.
For example, you can create highly targeted segments that focus on high or low propensity customers by defining which predictive score ranges to target in their campaigns. Similarly, predictive scores can be used in campaign journey building to move customers through different paths and receive different messages and offers based on their propensity to perform a certain action (i.e., purchase, subscribe, and so on).
You can export the predictive scores to any 3rd party application, paid media channel, or back to your internal systems for deeper analysis.