中文

Application Case 5.4

Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Rethink Its Redesign

    Lenovo was near final design on an update to the keyboard layout of one of its most popularPCs when it spotted a small, but significant, online community of gamers who are passionately supportive of the current keyboard design. Changing the design may have led to a mass revolt of a large segment of Lenovo’s customer base—freelance developers and gamers.
    The Corporate Analytics unit was using SAS as part of a perceptual quality project. Crawling the Web, sifting through text data for Lenovo mentions, the analysis unearthed a previously unknown forum, where an existing customer had written a glowing six-page review of the current design, especially the keyboard. The review attracted 2,000 comments! “It wasn’t something we would have found in traditional preproduction design reviews,” says Mohammed Chaara, Director of Customer Insight & VOC Analytics.
    It was the kind of discovery that solidified Lenovo’s commitment to the Lenovo Early Detection (LED) system, and the work of Chaara and his corporate analytics team.
    Lenovo, the largest global manufacturer of PCs and tablets, didn’t set out to gauge sentiment around obscure bloggers or discover new forums. The company wanted to inform quality, product development, and product innovation by studying data—its own and that from outside the four walls. “We’re mainly focused on supply chain optimization, crosssell/up-sell opportunities and pricing and packaging of services. Any improvements we make in these areas are based on listening to the customer,” Chaara says. SAS provides the framework to “manage the crazy amount of data” that is generated.
    The project’s success has traveled like wildfire within the organization. Lenovo initially planned on about 15 users, but word of mouth has led to 300 users signing up to log in to the LED dashboard for a visual presentation on customer sentiment, warranty, and call center analysis.
The Results Have Been Impressive

• Over 50% reduction in issue detection time.
• 10 to 15% reduction in warranty costs from out-of-norm defects.
• 30 to 50% reduction in general information calls to the contact center.

Looking at the Big Picture
    Traditional methods of gauging sentiment and understanding quality have built-in weaknesses and time lags:

• Customer surveys only surface information from customers who are willing to fill them out.
• Warranty information often comes in months after delivery of the new product.
• It can be difficult to decipher myriad causes of customer discontent and product issues.

    In addition, Lenovo sells its product packaged with software it doesn’t produce, and customers use a variety of accessories (docking stations and mouse devices) that might or might not be Lenovo products. To compound the issue, the company operates in 165 countries and supports more than 30 languages, so the manual methods to evaluate the commentary were inconsistent, took too much time, and couldn’t scale to the volumes of feedback it was seeing in social media. The sentiment analysis needed to be able to sense nuances within the native languages. (For example, Australians describe things differently than Americans.)
    The analysis-driven discovery of an issue with docking stations provided the second big win for Lenovo’s LED initiative. Customers were calling tech support to say they were having issues with the screen, or the machine shutting down abruptly, or the battery wasn’t charging. Similar accounts were turning up on social media posts. Sometimes, though not always, the customer mentioned docking. It wasn’t until Lenovo used SAS to analyze the combination of call center notes and social media posts that the word docking was connected to the problem, helping quality engineers figure out the root cause and issue a software update.
    “We were able to pick up that feedback within weeks. It used to take 60 to 90 days because we had to wait for the reports to come back from the field,” Chaara says. Now it takes just 15 to 30 days. That reduction in detection time has driven a 10 to 15% reduction in warranty costs for those issues. As warranty claims cost the company about $1.2 billion yearly, this is a significant savings.
    Although the call center information was crucial, the social media component was what sealed the deal. “With Twitter and Facebook, people described what they were doing at that minute, “I docked the machine and X happened.’ It’s raw, unbiased and so powerful,” Chaara says.
    An unforeseen insight was found when analyzing what customers were saying as they got their PCs up and running. Lenovo realized its documentation to explain its products, warranties, and the like was unclear. “There is a cost to every call center call. With the improved documentation, we’ve seen a 30 to 50% reduction in calls coming in for general information,” Chaara said.
Winning Praise beyond the Frontlines
    The project has been so successful that Chaara demoed it for the CEO. The goal is to configure a dashboard view for the C-suite. “That’s the level of thinking from our senior executives. They believe in this,” Chaara says. In addition, Chaara’s group will be formally measuring the success of the effort and expanding it to measure issues like customer experience when buying a Lenovo product.
    “The application of analytics has ultimately led us to a more holistic understanding of the concept of quality. Quality isn’t just a PC working correctly. It’s people knowing how to use it, getting quick and accurate help from the company, getting the non-Lenovo components to work well with the hardware, and understanding what the customers like about the existing product—rather than just redesigning it because product designers think it’s the right thing to do. “SAS has allowed us to get a definition of quality from the view of the customer,” Chaara says.

English

应用案例5.4

将客户带入质量方程:联想使用分析重新构思设计

    联想最近更新了一款最受欢迎的个人电脑键盘布局，当时他们发现了一个小型但很大。义的在线社区，社区内的人们热衷于支持当前的键盘设计。改变设计可能会导致联想客B中大部分自由职业开发者和游戏玩家的大规模反抗。
    企业分析单位将SAS作为感知质量项目的一部分。通过在Web上爬取数据，再对其中的文本数据筛选，分析发现了一个以前未知的论坛。其中一个客户写了一篇关于当前设计尤其是键盘的6页评论。这次审查收取了2000 条评论!“这不是我们在传统的预设计评富。发现的。”客户洞察和VOC分析主管Mohammed Chaara说。
    正是这种发现坚定了联想公司致力于Lenovo早期检测(LED)系统、Chaara 及其公司分析团队。
    联想是全球最大的个人电脑和平板电脑生产商，并没有打算对晦涩的博客进行情感评估或发现新的论坛。该公司希望通过研究自己的数据和外来的数据来了解质量、产品开发和产品创新。“我们主要集中在提供供应链的优化、交叉销售、向上销售的机会，以及定价和打包服务。我们在这些领域所做的任何改进都是基于客户的意见。“Chaara说。SAS提供了“管理产生的疯狂数据量”的框架。
    该项目的成功在该组织内像野火一样传开。联想最初计划大约15名用户，但目前已有300名用户注册并登录到LED仪表盘中，直观地展示客户的情感、保修和呼叫中心分析。
结果令人印象深刻:

• 问题检测的时间减少超过50%。
• 对不规范缺陷进行保修的费用减少10%至15%。
• 联络中心的常规信息咨询减少了30%至50%。

看一下重点
    评估情绪和理解质量的传统方法有内在的弱点和时间滞后的缺陷:

• 客户调查只从愿意填写它们的客户那里获取信息。
• 保修信息通常出现在新产品交付后的几个月内。
• 联很难解读客户不满和产品问题的多种原因

    此外，联想销售的产品是与非联想开发的软件捆绑在一起的，客户使用的各种配件(打展坞和鼠标设备)可能是也可能不是联想产品。更复杂的是，该公司在165个国家开展业务，并支持30多种语言，因此评估评论的人工方法不一致，而且占用了太多的时间， 无法扩展到社交媒体所看到的大量反馈。情感分析需要能够感知本土语言中的细微差别(例如，澳大利亚人对事物的描述不同于美国人。)
    对联想LED计划来说，使用分析驱动发现扩展坞( docking station)的问题为其带来!第二大胜利。客户打电话给技术支持，说他们的屏幕有问题，或者机器突然关机，或者电池没有充电。类似的报道也出现在社交媒体帖子上。有时，虽然并非总是如此，客户提到、“扩展”。直到联想使用SAS来综合分析呼叫中心记录和社交媒体帖子时，才将“扩展 这个词与问题联系起来，帮助质量工程师找出根本原因并发布软件更新。
    “我们可以在几周内得到反馈。这在过去常常需要60到90天，因为我们不得不等待现场报告，”Chaara说。 现在只需要15到30天。由于检测时间的减少，这此问题的保修成个降低了10%至15%。由于保修索赔成本每年花费约12亿美元，这节省了一笔巨大的资金。
    虽然呼叫中心的信息至关重要，但社交媒体部分起到了决定性作用。“有了Twitter和Facebook，人们可以分享他们的动态”，“我把机器停下来，X现象就发生了”。“这是实时的，无偏见的，非常强大的。”Chaara说。
    当客户使用电脑时，他们总会遇到了一些不可预见的问题。联想意识到用来解释它的产品，保修等信息的文档并不清晰。“每个呼叫中心都有成本。采用改进的文档，我们可以减少30%到50%咨询常规问题的呼叫”Chaara说。
在前线赢得赞誉
    该项目非常成功，Chara 也已经向CEO展示样例。目标是为C套件配置高级仅表盘视图。“这是我们高层管理层的想法，他们相信这个。”Chan说。此外，Chan的团队将正式度量他们的努力是否成功，并进一步将其扩展去度量其他问题，例如当客户购买联想产品时，他们的客户体验如何。
    “应用分析最终使我们对质量概念有了更全面的理解。质量不仅仅限于PC工作正常。人们知道如何使用它，并且可以从公司快速、准确地得到帮助，让非联想组件与硬件协同工作，并了解客户对现有产品的喜好而不是凭借产品设计师的想法来进行重新设计。“SAS允许我们从客户的角度去定义质量。”Chaara说。