Case Study: Commerce website doubled conversion rate with upsell model
After only a few hours contributed time, a retailer with multiple brands saw an effective upsell model built from raw sales and customer data.
After only a few hours contributed time, a retailer with multiple brands saw an effective upsell model built from raw sales and customer data.
To achieve success in a highly dynamic market, ecommerce businesses must be able to stay one step ahead of their customers—by predicting their behavior and interests in advance.
A grocery delivery app with over 12,000 suppliers was struggling to deploy data science. By using Pecan, they turned raw sales and inventory data into an effective demand forecast model in days instead of months.
The pressure is on to get value from big data, yet enterprise AI and data science projects can be slow, expensive, and produce nebulous results.
Pecan was built to break this trend. Our AutoML platform automates data preparation, feature engineering and selection, and AI-based predictive analytics algorithms, to transform your raw data into actionable predictions in the shortest time possible.
All without any code or data prep.
The Pecan platform keeps your business objectives front-and-center by design. Say goodbye to AI vanity projects.
Messy, siloed, CSV—drop in any data that might be relevant to Pecan’s model building and let automated data prep take it from there.
Insights shouldn’t have gatekeepers. Pecan’s dashboards give you high-level and granular visibility of the factors that affect your future outcomes.
Predictions only have value if they facilitate action. Connect your predictions to your action platforms by export or API.