LivePerson > 实例探究 > Predictive Returns for Commonwealth Bank with Live Chat and Predictive Targeting

Predictive Returns for Commonwealth Bank with Live Chat and Predictive Targeting

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公司规模
Large Corporate
地区
  • Pacific
国家
  • Australia
产品
  • LiveEngage platform
  • Predictive Targeting
技术栈
  • Live chat
  • Transcription analysis
实施规模
  • Enterprise-wide Deployment
影响指标
  • Customer Satisfaction
  • Revenue Growth
  • Productivity Improvements
技术
  • 应用基础设施与中间件 - API 集成与管理
适用行业
  • 金融与保险
适用功能
  • 销售与市场营销
服务
  • 培训
关于客户
Commonwealth Bank is a leading provider of integrated financial services in Australia. Founded in 1911, the bank has more than 52,000 employees and has remained focused on being the bank of the people. The bank's new brand campaign, “When you believe you can, you can”, asserts that the bank’s technologies and know-how will help its customers achieve their goals. The bank has been an early adopter of digital engagement, initiating a live chat pilot in 2009 to differentiate its brand and make it easier for customers to complete mortgage and other banking applications online.
挑战
Commonwealth Bank, a leading provider of integrated financial services in Australia, was looking to improve customer experience and support, increase conversion rates, and improve operational efficiency. The bank had initiated a live chat pilot in 2009 to differentiate its brand and make it easier for customers to complete mortgage and other banking applications online. However, the bank had reached a point of diminishing returns with its existing rules for customer engagement and needed to rethink its approach to targeting customers.
解决方案
Commonwealth Bank chose the LivePerson platform for its live chat solution, citing LivePerson's leadership in the space and the scale and security of its solution. The bank expanded its live chat program to all retail banking products and increased the number of concurrent live chat agents. The bank also engaged the LivePerson Customer Success organization for weekly performance reviews, engagement strategy analyses, staffing optimization, agent performance analysis, benchmarking against global performance, and ROI analysis. In 2013, the bank extended live chat to business banking and increased the number of concurrent agent seats. The bank also moved to Predictive Targeting, a feature of the LivePerson platform, to improve its targeting of customers.
运营影响
  • Customer satisfaction scores for the live chat program improved from 60-70% to consistently over 80%.
  • Chat response times were reduced from 31 seconds to an average of 26 seconds.
  • Operational efficiency improved from 20% away time to three percent or less.
  • Agent efficiency improved, with the number of chats per labor hour expanding from 8.9 to 10, while the length of live chats per hour increased from 11.5 to 12.5 minutes.
数量效益
  • Conversion rates increased from 10% to 17% with the use of insights from transcription analysis and institution of rules (2011-12).
  • Conversion rates increased from 17% to 21% with Predictive Targeting (2013).
  • Acceptance rates improved from 11% to 12%.
  • Saved as much as 80 staff hours per month with Predictive Targeting.

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