遇见数据集

Acxiom Predictive Audiences

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Databricks2024-05-09 收录
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Acxiom Audience Propensities® are market-leading behavioral and attitudinal indicators based on known, actionable information and tied to shopping intent, attitudinal data, media behavior, etc. These unique indicators predict brand affinity and preferences, in-market interests and timing, and media viewing habits, allowing you to fuel powerful experiences and engagement. Audience Propensities are designed to address the distinct nuances of a wide range of industries. Rooted in multidimensional insights from syndicated data partner relationships, Acxiom’s audiences are superior to other approaches that rely on a single attribute or attitudinal indicators. Our varied partners and expertise in ethical data collection methods yield superior predictive insights. With statistical confidence, Audience Propensities help predict answers to the most important marketing questions. **Over 3,500 prebuild audience predictors, including:** - 300+ automotive industry propensities including brand affinities, accessories, and packages - 500+ CPG propensities including consumable food and beverage preferences, pet products and beauty supplies - 200+ insurance propensities including brand preference for auto, property, life and health as well as channel propensities - 200+ investment services attributes including assets, retirement savings and affinity for investing - 900+ retail propensities including purchase behavior, shopping propensities and brand affinities - 150+ technology category propensities including mobile wallet interests, consumer electronics brand affinities, wearables attitudes and behavior, and media usage **Why Acxiom Predictive Audiences?** - Increases the accuracy and predictive power of your marketing efforts - Extends audiences when data coverage and scale are lacking - Enables effective and timely messaging

提供机构:
Acxiom LLC
搜集汇总
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Acxiom Predictive Audiences 数据集图片
背景与挑战
背景概述
Acxiom Predictive Audiences是一个基于行为与态度指标的数据集,提供超过3,500个预建的受众预测指标,涵盖汽车、消费品、保险、投资服务、零售和科技等多个行业。该数据集利用多维数据源和伦理收集方法,能有效预测品牌偏好、购买兴趣和媒体习惯,从而提升营销活动的准确性和覆盖范围。
以上内容由遇见数据集搜集并总结生成
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