Retail

Alliant Retail audiences are built using product-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics, and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics to predict purchase from brands. Samples/Tables Included: -Apparel Brands & Products -Big Box Retail Brands -Buying Chanel Preference -Ecommerce Platforms -Drugstore Brands -Household Goods Brands -Jewelry Products -Kids Products -Sporting Goods Brands Sample Fields Included: - Abercrombie and Fitch Buyer Propensity - Adidas Buyer Propensity - Costco Buyer Propensity - Amazon Buyer Propensity - LEGO Buyer Propensity - Hoodie - Men's Products - DICK'S Buyer Propensity - Quill Buyer Propensity - Williams-Sonoma Buyer Propensity Expected Workflow - Navigate to the Alliant Marketplace tile and click “Request” - An Alliant representative will reach out - Provide business requirements and/or create table query - Upload data to Snowflake data share (if applicable) - Alliant will return requested data

搜集汇总

背景与挑战

背景概述

Retail数据集通过整合产品特定的电商数据和包含消费者购买交易、人口统计及生活方式信息的线下数据库，构建预测模型以识别具有共同特征的潜在购买家庭。它涵盖了多个零售类别，如服装、大卖场和电商平台，并包含具体品牌的购买倾向性指标。使用该数据集需通过Alliant Marketplace请求，并与代表沟通业务需求后获取数据。