E-commerce Use Cases/Product & CatalogData Analyst

What percentage of our product recommendations come from only the top 5% of products, and are we creating a 'rich get richer' effect that hides long-tail inventory?

Evaluate recommendation algorithm diversity to ensure catalog breadth is surfaced and long-tail products get visibility.

Metrics & KPIs

Recommendation concentrationlong-tail coverageunique products recommended

Required Data

Recommendation impression logsproduct IDsproduct catalog size

Data Sources

Search & PersonalizationE-commerce PlatformData Warehouse

Works with tools like

AlgoliaNostoDynamic YieldBloomreachKlevuShopifyWooCommerceMagentoBigCommerceSalesforce Commerce CloudSnowflakeBigQueryRedshiftDatabricksClickHouse

How Bruin answers this

Bruin

Bruin AI Data Analyst

What percentage of our product recommendations come from only the top 5% of products, and are we creating a 'rich get richer' effect that hides long-tail inventory?

Bruin connects to your Search & Personalization, E-commerce Platform, Data Warehouse and runs the analysis automatically.

It tracks Recommendation concentration, long-tail coverage, unique products recommended and delivers the answer in seconds, in Slack, Discord, Teams, Google Chat, WhatsApp, Telegram, email, or your browser.

Bruin for e-commerce

Use cases across every team in your e-commerce business, from conversion funnels to inventory, marketing to customer lifetime value. One AI that speaks your data.

C-Level/ExecutiveCategory ManagerCustomer Experience ManagerData AnalystDigital Marketing SpecialistE-commerce ManagerFinance ManagerGrowth ManagerMarketing ManagerMerchandiserOperations ManagerSupply Chain Manager

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