Alteryx > 实例探究 > 量化营销和 CRM 支持

量化营销和 CRM 支持

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公司规模
Large Corporate
地区
  • America
国家
  • United States
产品
  • Alteryx
  • Tableau
技术栈
  • Alteryx
  • Tableau
  • Data Mart
实施规模
  • Enterprise-wide Deployment
影响指标
  • Productivity Improvements
  • Customer Satisfaction
技术
  • 分析与建模 - 数据即服务
  • 分析与建模 - 实时分析
适用行业
  • 零售
适用功能
  • 销售与市场营销
  • 商业运营
用例
  • 预测性维护
服务
  • 数据科学服务
  • 系统集成
关于客户
Sally Beauty 是一家全球零售商,在全球拥有 3,673 家门店。该公司的主要产品类别包括染发、护发和器具。其客户包括专业造型师和零售消费者。该公司拥有 1200 万活跃的忠诚会员。Sally Beauty 的营销数据库和分析经理已在公司工作 4 年,在营销行业拥有超过 6 年的经验。她的大部分经验是在渠道(电子邮件、直邮和数字)营销和客户洞察方面。
挑战
Sally Beauty 是一家全球零售商,在全球拥有 3,673 家门店,其客户关系管理 (CRM) 和营销分析面临挑战。该公司的报告是在 Access 和 Excel 中完成的,交易和客户数据不在集中位置。销售/交易数据是从不支持临时报告的应用程序中提取的,并且 IT 不再提供支持。该公司依靠 IT 提取平面文件进行客户级分析,这需要几天到几周的时间。活动数据未存储以供查看绩效趋势或历史视图,月末报告需要 2-3 周才能提取所有数据并进行分析。
解决方案
Sally Beauty 实施了领先的自助式数据分析平台 Alteryx 和用于数据可视化的 Tableau。Alteryx 用于可重复使用的月度报告工作流、人口统计数据、预测工具和复杂的临时数据分析。60% 的月度报告都是在 Alteryx 中构建的,用于可重复使用的工作流,包括客户迁移报告、渠道报告和忠诚度会员指标。报告时间缩短了一半,复杂的临时数据问题可以在几小时内得到解答,而不是几天。Tableau 用于仪表板、数据可视化和简单的临时问题。仪表板使领导团队能够轻松跟踪结果和客户群。可视化显示了平面电子表格上看不到的趋势,并允许非数据人员回答他们的数据问题。
运营影响
  • The implementation of Alteryx and Tableau has significantly improved Sally Beauty's reporting and analytics capabilities. The company is now self-sufficient and no longer needs to rely on IT to pull flat files. The reporting time frame has been cut in half and the company can answer complicated ad hoc data questions in hours, not days. The company can also output finished reports and does not have to build a report in one tool and polish it in another.
  • The use of Tableau has provided the leadership team with the ability to track results and customer groups easily. Visualization has revealed trends that were not seen on a flat spreadsheet and has allowed non-data people to answer their data questions.
  • The company has also been able to enhance its marketing data sciences capabilities. It has used demographic analysis tools to append ethnicity, occupation, hobbies and interests, and household information to its customer data. This data, along with 24 months of transactional data, was used to identify different and distinct clusters of customers. Personas were developed around the clusters to identify the key characteristics/differences of each cluster. This has enabled various cross/up sell, retention, and lifecycle modeling to be done at the cluster level.
数量效益
  • The reporting time frame has been cut in half.
  • Month end reporting now takes 5-7 business days, down from 2-3 weeks.
  • An estimated 1.5% growth on monthly revenue from converting one-time customers into multiple time buyers.

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