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Retail & E-Commerce

Demand forecasting, personalization, and churn prevention

Build ML models for demand forecasting, product recommendations, customer churn prediction, and dynamic pricing — with canary deployments for risk-free rollouts during peak traffic.

The Challenges

Seasonal Demand Shifts

Holiday spikes, flash sales, and trend cycles cause rapid changes in purchase patterns that static models cannot handle.

Customer Data Scale

Millions of transactions, browsing events, and product interactions require efficient training pipelines and fast prediction serving.

Cold Start Problem

New products and new customers have no historical data, making recommendations and churn predictions unreliable.

A/B Testing Complexity

Comparing model versions in production requires careful traffic splitting and statistical significance monitoring.

How CorePlexML Helps

AutoML

Automated Demand Models

AutoML trains time-series and regression models for demand forecasting with automatic feature engineering from historical sales data.

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MLOps

Canary Deployments

MLOps enables gradual traffic shifts between model versions with automatic rollback when prediction quality degrades.

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ML Studio

What-If Pricing

ML Studio lets analysts test pricing scenarios and see predicted impact on conversion rates before going live.

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SynthGen

Synthetic Cold-Start Data

Generate synthetic purchase histories for new product categories using SynthGen, bootstrapping recommendation models.

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SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Train demand forecasting model
experiment = client.experiments.create(
    project_id="proj_demand",
    dataset_version_id="dsv_sales_history",
    target_column="units_sold",
    max_models=25,
    max_runtime_secs=900
)

# Deploy with blue-green strategy
deployment = client.deployments.create(
    project_id="proj_demand",
    model_id=experiment["leader_model_id"],
    strategy="blue_green"
)

# What-If pricing analysis
session = client.studio.create_session(
    project_id="proj_demand",
    deployment_id=deployment["id"]
)

Expected Impact

94%
Forecast Accuracy
31%
Churn Reduction
Zero
Deployment Downtime
3x faster
Model Iteration Speed

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

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