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Paytronix Enhances Customer Engagement with Real-Time Data Science via Fivetran and Coalesce
技术
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
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
- 实时定位系统 (RTLS)
- 时间敏感网络
服务
- 数据科学服务
挑战
Paytronix 是一家面向餐馆和小型企业的客户参与平台，在管理数据和从数据中获取见解方面面临着重大挑战。该公司正在处理来自多个来源、在不同数据库上运行且格式不同的数据。他们使用的数据摄取工具不可靠，并且错过了许多交易，导致对基础数据缺乏信任。此外，该公司混合使用 Scala 和 PySpark 作业进行数据转换，这是自定义代码和手写的。该工具集无法满足业务不断增长的需求，并且大量时间花费在维护和故障修复支持上。该公司希望更多地关注实验，但现有系统不利于快速概念验证测试和快速迭代。
关于客户
Paytronix 是一个客户互动平台，为餐厅和便利店行业的 1,800 多个品牌提供服务。该公司帮助客户利用客户数据来改善数字营销渠道，并为客户每次访问商店（无论是亲自还是在线）提供无缝体验。该公司拥有一个由七人组成的团队，由数据科学总监 Jesse Marshall 领导，该团队与更大的战略和分析团队密切合作，为客户提供有效与客户互动所需的见解。
解决方案
为了应对这些挑战，Paytronix 采用了 Fivetran 和 Coalesce。 Fivetran 本地数据处理因其易于设置和可靠性而被选中，取代了传统的提取工具。它用于将各种来源的所有数据带入 Snowflake。 Coalesce 因其易用性和灵活性而受到一位行业同事的推荐。该公司还开始使用 Snowflake Snowpark，它允许数据科学家用 SQL 以外的语言进行编码，而无需从 Snowflake 中取出数据。 Paytronix 使用 Fivetran 将数据近乎实时地传输到 Snowflake，然后使用 Apache Airflow 触发 Coalesce 中的转换并在 Snowpark 中运行模型。这一设置使 Paytronix 能够大规模执行实时预测建模，为客户提供有关其客户活动的实时信息。
运营影响
数量效益
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