User Action Prediction

Predictive analytics play an important role in digital marketing. User Action Prediction (UAP) is the process of predicting the probability that a user/visitor/customer will perform a certain action on the website or in-store, within a given time. For instance:
  1. For eCommerce companies, this could mean predicting the propensity that a user/visitor/customer visiting the website will complete a purchase online or offline
  2. For companies with mostly in-store operations (like automobile companies), this can be used to predict whether a visitor coming to their website will fill up an in-person visit form that will be used as a lead (e.g. book a test drive or consultation)

These predictions enable marketers to optimize their targeting and retargeting strategies.

Stacktics’ User Action Prediction (UAP) System

The Stacktics’ User Action Prediction System uses Machine Learning to help analysts score the user propensities for specific actions on their website or in store. This can be deployed for ANY action of choice.

The diagram below shows the typical workflow for User Action Prediction:

Advanced ML Models for Prediction

Stacktics’ ML models can be used to get very accurate predictions for the propensity scores for user actions.

State-of-the-art Continuous Training Pipelines

Stacktics’ UAP comes with continuous training pipelines that can automatically retrain Machine Learning models, monitor model performance and observe data and model drifts in production.

Automated Scheduled Uploads to End Marketing Platforms

Stacktics’ Audience Activation and Audience upload pipelines can be plugged into the user action prediction system to automatically activate on the user lists and audiences created using the outputs of the user action prediction system.


Stacktics provides an end to end system that leverages ML models in a cloud-based environment to successfully predict propensities for users/visitors/customers to complete an action. This concrete intelligence empowers a team to make advanced effective-targeted decisions.

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