通过 IoT 转变客户支持：Freshworks 案例研究
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
- 教育
- 设备与机械
适用功能
- 维护
- 销售与市场营销
用例
- 施工管理
- 预测性维护
服务
- 数据科学服务
- 培训
关于客户
Freshworks 是一家技术和软件公司，提供广泛的 CRM 和客户体验解决方案。其使命是帮助客户让不同工作职能的员工满意，包括 IT、客户服务、销售、营销和人力资源。 Freshworks 拥有超过 60,000 家企业客户和多个产品线，从客户那里生成大量数据，用于优化流程并加快解决问题的时间。该公司之前使用的是旧版 Hadoop 基础设施和各种数据工具，这导致了性能瓶颈并减慢了客户服务速度。
挑战
Freshworks 是一家 CRM 和客户体验解决方案提供商，由于遗留的 Hadoop 基础设施和各种数据工具，在提高其客户支持组织的绩效方面面临着挑战。该公司拥有超过 60,000 家企业客户和多个产品线，由于需要大量和高水平的支持，因此很难保持卓越的客户满意度。管理服务台票证的手动方法不足以满足需求。该公司的内部企业数据平台由 Hadoop 提供支持，由多种数据和分析工具组成，这会产生大量 IT 开销来管理升级和监控性能。随着数据量的增加，这种环境产生了性能瓶颈，降低了客户支持团队有效服务客户的能力。
解决方案
为了应对这些挑战，Freshworks 迁移到 Databricks Lakehouse，该平台提供灵活性、多云支持以及统一的数据、分析和人工智能方法。此次迁移涉及跨多个云移动 500 多 TB 数据以及 40 多个数据源和工具，整个过程在七个月内完成。该公司与 Databricks 合作确定依赖关系和模式，这使他们能够制定跨数据源、集成和端点的迁移计划。 Databricks Lakehouse 取代了基于 Hadoop 的平台，使 Freshworks 能够实现数据访问民主化并改进运营分析。该公司还使用 Delta Lake 来确保所有数据层的数据可靠性和一致性，因为它构建了支持分析和机器学习工作负载的管道。借助 MLflow，数据科学团队能够简化从训练和实验到版本控制和部署的机器学习生命周期。
运营影响
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