Ecommerce and retail

AI technology has evolved to be a powerful tool for eCommerce and retail companies to boost sales and optimize operations. Common use cases include:

• Personalized product recommendations
• Pricing optimization
• Customer segmentation
• Enhanced customer service
• Smart logistics
• Sales and demand forecasting

The challenge

To achieve success with AI, eCommerce and retail companies capture and aggregate data from a wide range of online and offline sources. The volume of clickstream, traffic logs, social and other data can easily grow to tens of petabytes, costing millions of dollars annually even in "low-cost" cloud object stores.

For eCommerce and retail companies, all data is hot data which must be quickly ingested, analyzed, and acted upon. These data costs are consuming ever more of the AI innovation budget and crowding out investment in compute, tooling, and people.

How Granica helps

Granica increases the efficiency and utility of your AI-related data and your downstream AI pipeline stages, enabling you to free up significant money, resources, and time which you can reinvest to improve AI performance and outcomes.

Granica Crunch for eCommerce and retail data

Granica Crunch reduces the storage cost associated with AI data without archival and/or deletion. Its advanced, patented ML-powered data reduction algorithms are specifically optimized for the clickstream and log data prevalent in the eCommerce and retail industry.

How Crunch helps AI/ML Engineers

• Optimizes S3/GCS object storage costs, so you can allocate more resources to data quality and model performance
• Elastically scales up and down to support dynamic workloads

How Crunch helps AI Product Owners and FP&A

• No upfront capital outlay — Crunch doesn't cost budget, it frees up budget
• Ongoing, predictable savings enable accurate forecasting and planning