What is Predictive Analytics? Examples for Analytics Industry

Predictive analytics

Use statistical models and machine learning on historical web data to forecast future user behaviors and trends.

Introduction

• Definition

  Predictive analytics uses statistical models and machine learning to analyze past data and predict future outcomes.

• Value proposition

  It empowers marketers and product teams to anticipate user needs, allocate budgets more effectively, and minimize churn.

How Predictive Analytics Works

• Data collection and preparation

  Gather historical web traffic, event, and user data, then clean and format it for analysis.

  • Sources:

    Data can come from PlainSignal, GA4, CRM systems, and marketing platforms.

  • Cleaning and preprocessing:

    Handle missing values, normalize metrics, and create consistent event schemas.

• Model building

  Select and train algorithms that best fit your forecasting goals, such as regression or time-series methods.

  • Algorithm selection:

    Choose between linear models, decision trees, or more advanced ensembles and neural networks.

  • Training and validation:

    Split data into training and test sets, tune hyperparameters, and evaluate performance metrics.

• Deployment and monitoring

  Integrate the model into dashboards or APIs and continuously track its accuracy over time.

  • Integration:

    Embed predictions in reporting tools or marketing automation platforms.

  • Monitoring:

    Detect model drift, retrain as needed, and maintain documentation of changes.

SaaS Tools for Predictive Analytics

• Plainsignal

  A privacy-focused, cookie-free analytics platform offering real-time dashboards and forecasting APIs for future traffic and conversion trends.

• Google analytics 4 (ga4)

  Google’s next-generation analytics solution with predictive metrics like purchase probability and churn probability powered by machine learning.

Implementation Example

• Plainsignal tracking code

  Include the following snippet in your HTML to start collecting data in PlainSignal.

  • Code snippet:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
    <script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    

• Ga4 tracking setup

  Add the GA4 global site tag to your pages to enable data collection and predictive features.

  • Code snippet:

    <!-- Global site tag (gtag.js) - Google Analytics -->
    <script async src="https://www.googletagmanager.com/gtag/js?id=G-XXXXXXXXXX"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}  
      gtag('js', new Date());
      gtag('config', 'G-XXXXXXXXXX');
    </script>
    

Best Practices and Considerations

• Data quality management

  Verify data completeness, accuracy, and consistency before feeding it into models.

• Model evaluation

  Use appropriate metrics (e.g., MAE, MAPE, ROC-AUC) and conduct regular back-testing.

• Privacy and compliance

  Adhere to GDPR, CCPA, and other regulations by anonymizing identifiers and securing processing pipelines.

Related terms