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顧客エンゲージメントを高める―
ビッグデータと AI の力を活用して顧客に関する洞察を深め、あらゆるチャネルでお客様を魅了するカスタマイズされたショッピング体験を提供します。
Innovating Retail with Unified Analytics
Built by the original creators of Apache Spark™, the Databricks Unified Analytics Platform enables retailers and ecommerce companies to easily build, scale, and deploy machine learning models in minutes, leading to better customer experiences that convert.
Personalize Recommendations
Use machine learning to mine clickstream, purchase and customer data to provide targeted recommendations that drive lifetime value
Optimize Pricing and Promotions
Improve promo conversion by using big data to serve the right ad, at the right time, to the right person, on the right device
Fight Retail Fraud
Prevent credit card fraud, cyberattacks and other illicit activities by detecting anomalous behavior with real-time analytics and AI
Better Serve Your Customers with Data Science at Scale
Read this ebook to learn how to unify big data and AI in the retail industry
Overstock Creates AI-Powered One-to-One Marketing Machine
Learn how Overstock tackled the challenge of building a powerful One-to-One marketing engine that would bring a personalized shopping experience to their customers using Databricks on AWS.
Rue Gilt Groupe Increases Purchase Engagement by 10x
Learn how Rue Gilt Groupe leveraged Databricks on AWS to accelerate query speeds by 60% and speed-up iterative model development, leading to increased shopper engagement and purchase behavior
もっと読む
From Batch to Real Time: Overstock Unifies Analytics
Chris Robison, Lead Data Scientist at Overstock, shares innovations in building a successful marketing technology infrastructure for instantaneous individualized shopping experiences
動画を見る
Build a ML Pipeline to Predict Advertising Click Rates
Learn how to build an end-to-end pipeline capable of analyzing large volumes of clickstream and advertising data to predict whether or not a customer will click on an ad impression
もっと読む"Working in Databricks is like getting a seat in first class. It’s just the way flying (or data science-ing) should be."事例を見る
Mary Clair Thompson, Data Scientist, Overstock.com
