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Optimizing Customer Engagement with Databricks Lakehouse: A Case Study on Iterable
技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 应用开发平台
适用行业
- 水泥
- 设备与机械
适用功能
- 维护
- 销售与市场营销
用例
- 预测性维护
- 快速原型制作
服务
- 数据科学服务
- 硬件设计与工程服务
挑战
Iterable 是一家帮助品牌优化和人性化营销的公司，其数据基础设施面临着挑战。该公司需要为其客户构建个性化和自动化的客户体验，这需要利用多样化、复杂的数据集并促进机器学习模型的快速原型设计。然而，他们最初使用 AWS 原生工具（包括 EMR）构建的基础设施是资源密集型的，维护成本高昂，并且产生了大量的运营开销。这使得 Iterable 难以扩展支持客户需求和快速响应市场变化所需的数据摄取水平和机器学习模型的快速原型设计。此外，该公司的人工智能解决方案必须考虑到不同的数据变量、模型的漂移、新的监管变化以及对更多隐私保护不断增长的需求。
关于客户
Iterable 是一家营销公司，帮助 1,000 多个品牌在当今的竞争格局中优化营销并使其人性化。该公司使营销人员能够在客户品牌旅程的每个阶段为客户提供个性化的跨渠道沟通。他们通过客户细分和个性化以及活动优化（例如确定向客户发送消息的最佳时间以及哪些参与渠道效果最好）来实现这一目标。 Iterable 致力于不断创新，创建和部署尖端解决方案，帮助品牌在竞争中保持领先地位，并满足客户不断增长和变化的期望。
解决方案
Iterable 求助于 Databricks Lakehouse 来克服这些挑战。 Databricks Lakehouse 使 Iterable 能够整理其多样化的数据来创建预测目标，这是一种数据丰富的细分工具，允许营销人员利用第一方数据创建定制的、面向目标的预测细分。该平台促进了整个模型生命周期的快速原型设计和协作，从简化数据访问和自动化集群管理到简化模型实验和迭代。 Delta Lake 是 Lakehouse 平台的一个组件，它为 Iterable 提供了以安全且合规的方式创建所有数据的统一视图的方法。这使得 Iterable 能够准确、完整地了解客户的需求，并开发有效的解决方案来满足这些需求。凭借 5,000 多个管道提供的可靠且一致的数据，数据科学团队使用 MLflow 为公司客户培训、实验和跟踪 2,000 多个项目中的数百个模型。
运营影响
数量效益
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