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InMarket Enhances Data Platform with ML-Powered Solution for Improved Efficiency and ROI
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
- 零售
适用功能
- 产品研发
- 销售与市场营销
用例
- 实时定位系统 (RTLS)
- 零售店自动化
服务
- 数据科学服务
- 测试与认证
挑战
InMarket 的旧数据平台效率低下,作业成功率为 40%,并且存在数据部署和开发延迟的问题。该平台无法处理从多个来源收集的不断增长的实时位置数据,从而导致延迟、瓶颈和效率低下。将数据管道从数据科学家移交给数据工程师和运营人员需要长达十二个月的时间,从而减缓了收入增长。由于作业中止,数据科学家不得不重新运行多个 Spark 作业,效率低下。
关于客户
InMarket 是一个全渠道营销平台,帮助财富 500 强品牌识别新的潜在客户和客户、提高商店客流量并利用人工智能和数据驱动的消费者智能增加销售额。
解决方案
Provectus 使用 AWS 产品设计并构建了一个由 ML 驱动的数据和分析平台。该平台使用 Amazon EMR 管理的 Apache Spark 进行数据处理,使用 Amazon S3 进行数据存储,使用 AWS Lambda 进行事件处理,并使用 Snowflake DWH 进行商业智能和分析。微服务架构和Apache Kafka用于实时、交互式生产集群。该解决方案部署在四个主要集群上:数据集群、Kubernetes 集群、批量数据处理集群和 Snowflake 集群。
运营影响
数量效益
相关案例.
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Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
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