Generate Marketing Conversion ML Training Data

Generate production-ready marketing conversion datasets with demographic and behavioral features, multi-channel signals, and configurable conversion rates, built for growth teams.

Binary classification5,000 rows6 features15/85 imbalanceMulti-channelNoise 0.15

Generate Marketing Data

Marketing Conversion template configuration

Here's the pre-built template configuration. Customize everything after loading.

marketing-conversion.json

{
  "templateName": "Marketing Conversion",
  "taskType": "classification",
  "numSamples": 5000,
  "features": [
    { "name": "age", "type": "numeric", "distribution": "normal", "mean": 35, "std": 12 },
    { "name": "income", "type": "numeric", "distribution": "log-normal", "mean": 55000, "std": 25000 },
    { "name": "page_views", "type": "numeric", "distribution": "poisson", "mean": 8 },
    { "name": "email_opens", "type": "numeric", "distribution": "poisson", "mean": 3 },
    { "name": "channel", "type": "categorical", "categories": ["organic", "paid_search", "social", "email", "referral"] },
    { "name": "previous_purchase", "type": "boolean", "trueRatio": 0.25 }
  ],
  "target": { "labels": ["no_conversion", "converted"], "weights": [85, 15] },
  "noise": 0.15
}

Built for Marketing

Every feature is configured with domain-appropriate distributions and realistic parameters.

Demographic & Income Features

Age follows normal distribution; income follows log-normal with realistic right skew. These demographic signals help model audience segment conversion propensity.

Digital Engagement Signals

Page views and email opens use Poisson distributions modeling discrete engagement events. Higher engagement correlates with conversion probability.

Multi-Channel Attribution

Five acquisition channels (organic, paid search, social, email, referral) with weighted distributions. Model channel-specific conversion rates and attribution.

Purchase History Signal

Boolean previous_purchase flag with 25% true rate. Test how prior purchase behavior affects conversion prediction, a strong signal in real marketing data.

Who uses Marketing Conversion training data?

Growth & Marketing Teams

Build conversion prediction models to optimize campaign targeting. Test audience segmentation strategies with controlled synthetic data before spending ad budget.

Marketing Analytics Engineers

Develop and test attribution models, lead scoring pipelines, and conversion prediction APIs with realistic multi-channel engagement data.

CRM Platform Teams

Prototype lead scoring and next-best-action features with realistic marketing data. No need to wait for production CRM data access.

Marketing Data Patterns

SynthForge IO generates marketing datasets that mirror the statistical patterns of real customer acquisition and conversion data.

Log-Normal Income

Income follows log-normal distribution. Most values cluster around the median with a realistic long tail of high earners, matching census-derived income distributions.

Poisson Engagement Counts

Page views and email opens use Poisson distributions modeling discrete, independent engagement events. Configurable mean rates let you simulate different engagement levels.

Configurable Conversion Rate

Default 15% conversion rate matches typical marketing campaign performance. Adjust from 1% (cold outreach) to 50% (high-intent remarketing) to match your scenario.

Channel Mix Control

Five channels with adjustable weights let you model different marketing mixes. Test how channel attribution affects conversion prediction accuracy.

More ML use cases

Housing

Regression

View Housing ->

View all ML training dataset features ->

Frequently asked questions

What conversion rate does this template use?

The default is 15% converted / 85% not converted, which represents a typical marketing campaign conversion rate. You can adjust this to match your specific scenario, from 1% for cold outreach to 50% for high-intent remarketing audiences.

Can I customize the marketing channels?

Yes. The template includes 5 default channels (organic, paid search, social, email, referral), but you can modify the categories and weights to match your specific channel mix.

How is income distributed?

Income follows a log-normal distribution with mean $55,000 and standard deviation $25,000. This produces a realistic right-skewed income distribution where most values cluster around the median with a long tail of high earners.

What export formats are available?

Datasets export as a ZIP containing CSV and Parquet files with separate train/test/validation splits, a data quality report, and an auto-generated Jupyter notebook for immediate exploration and modeling.

Can I model multi-touch attribution with this data?

The template generates single-channel data per record. For multi-touch attribution, you can add additional boolean features (e.g., saw_social_ad, received_email, clicked_search_ad) to model multi-channel customer journeys.

Start Generating Marketing Conversion Training Data

Load the Marketing Conversion template, customize features and parameters, and export publication-ready datasets in seconds.