Fivetran > 实例探究 > Paytronix 通过 Fivetran 和 Coalesce 通过实时数据科学增强客户参与度

Paytronix 通过 Fivetran 和 Coalesce 通过实时数据科学增强客户参与度

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

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。

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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。

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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。

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运营影响

The adoption of Fivetran and Coalesce has brought significant operational benefits to Paytronix. The company can now offer its clients real-time information about their customers' activity, enabling them to run more effective marketing campaigns and offer tailored messaging that strengthens the personal connection with the brand and boosts engagement. The new system has also reduced the development time, allowing analysts to focus on their core tasks instead of creating ETLs. This has led to a single source of truth for the company's data, increasing trust in the data among the analysts. The ability to perform real-time predictive modeling at scale has also opened up new possibilities for the company's data science initiatives.

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数量效益

Fivetran provides reliable, worry-free data ingest every 15 minutes
With Coalesce, two new team members were able to complete a high-profile transformation in one month, a task that previously took the entire team 6 months without much progress
Real-time predictive modeling is now possible at scale

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