• 观察
    【HR术语】什么是人力分析?(What is people analytics?) 什么是人力分析? 人力分析是一种以数据为导向的方法,旨在改进有关团队成员和客户的业务决策。人力分析并不完全依赖直觉或传闻经验,而是为人力资源领导者提供全面的数据,以做出有据可依的战略决策。 人力分析整合了人力资源信息系统软件,以收集和检查大量信息,预测趋势,并提供与员工生命周期不同阶段相关的宝贵见解。人力分析也被称为人才分析,公司利用人力分析将合适的人才匹配到合适的岗位上。 人力分析为何重要? 人力分析可对数据进行评估,以加强以下方面的工作: 招聘和录用 入职培训 劳动力规划 薪酬实践 留住人才 绩效 生产力 参与 不仅仅是人力资源部门,所有部门都开始采用人才分析功能。这种转变预示着人力资源自动化程度的提高: 人力资源领导者必须做好迎接这些变化的准备,以提供最新、准确的结果。 利用人力资源自动化工具,人力资源领导者可以做出明智的决策,提升员工体验,促进公司目标的实现。在竞争激烈、不进则退的市场中,人力资源领导者必须表现出灵活性,不断适应工作场所的创新。 人力分析实例 人力资源中的人力分析包括员工生命周期的各个方面。人力分析的例子包括: 流失预测。这包括分析历史数据,找出导致团队成员流失的模式和因素,使您有能力预测哪些员工有离职风险。 招聘优化。人力分析有助于改进招聘方法,以吸引和留住顶尖人才。 绩效分析。这包括评估关键绩效指标(KPI)、目标实现情况和能力评估等绩效数据,以获得有助于识别高绩效人才及其成功因素的见解。 员工参与度。调查数据和情感分析有助于深入了解员工满意度、敬业度以及影响因素。这些信息有助于组织设计提高员工敬业度的措施,并创造更加积极的工作环境。 学习与发展。分析可追踪培训成果、技能掌握情况以及教育计划带来的绩效提升,帮助您优化与业务目标相一致的培训投资。 关键人员分析指标 早期离职率。该指标指的是在公司工作第一年内离职的人员比例。它有助于评估留住人才的努力。 缺勤率。这衡量的是一个人意外缺勤的频率,无论是由于疾病、压力还是其他个人原因。团队成员缺勤率高,说明对工作场所不满意。 参与度得分。这些分数反映了员工对工作和整个组织的投入程度和满意度。 绩效评级。根据预定指标和目标对个人或团队的绩效进行评估。 每次招聘成本。评估招聘流程的总成本,包括广告、面试和入职成本。 如何利用人力分析做出决策 人力资源人力分析的有效性取决于将从数据中获得的见解付诸行动。以下步骤将帮助您为组织做出清晰、明智的决策: 确定目标。明确概述您打算利用人力分析来应对的具体组织挑战。 收集数据。从人力资源信息系统(HRIS)或人力资源管理系统(HCM)、绩效考核、调查及其他来源收集相关数据。 分析。采用统计方法、可视化工具或人力分析平台,从收集的数据中得出有意义的见解。 识别模式。从数据中寻找相关性和趋势,从而深入了解员工行为和组织面临挑战的原因。 做出明智决策。利用从数据中收集到的洞察力,做出有据可依的决策,帮助组织实现其目标。 人力分析仪表盘 人力分析仪表盘是关键人力资源指标和数据的可视化呈现。它提供的关键信息一目了然,如离职率、参与度评分、绩效数据和招聘统计数据。 仪表盘使人力资源领导和管理人员能够跟踪和了解劳动力指标,从而轻松做出明智的决策。用户友好、信息丰富的仪表盘可让利益相关者迅速访问和解释重要数据,而无需完全依赖 IT 部门或分析师。 如何成功实施人才分析系统 将人才分析纳入多个部门需要人力资源领导者挺身而出,指导他人完成这项新举措。人力资源领导者可以通过实施这些做法来支持人员分析的整合: 以身作则。展示对人才分析的熟练程度,或至少是对人才分析的理解,可以让人力资源领导者有效地使用人才分析,并在其他人学习的过程中为他们树立榜样。 观察。人力资源领导者可以确定其组织目前进行的数据收集水平,并注意公司目前使用的数据分析方法,如数据收集技术和类别,以及哪些人力资源领导者对数据负责。 向所有人力资源专业人员介绍人力分析。为所有人力资源人员提供人员分析 "基础培训",可以提高他们对系统的认识。让人力资源专业人员熟悉人员分析,可以增强他们的能力,同时将这种方法融入公司文化和思维模式。 培训分析团队。教育人力分析专家如何阅读和仔细检查数据、警惕不准确的数据并做出基于数据的决策,这一点非常重要。这些人力资源专业人员决定着人才分析系统的有效性。 注意潜在隐患。公司可以利用人力资源信息系统平台简化、过滤数据,并以易于理解的方式将数据呈现给管理人员。另一个需要注意的挑战是通过数据加密和遵守诚实、公平和透明的政策来保护员工的隐私。 人力分析与人力资源分析 专业人士会交替使用 "人员分析 "和 "人力资源分析 "这两个术语。然而,两者之间是有区别的: 人力资源分析侧重于利用人力资源部门的数据来了解和管理员工。它深入研究团队成员的个人行为、绩效和参与度,旨在优化员工生命周期中的各种人力资源流程。 虽然人员分析也使用人力资源数据,但其关注点超出了这一范围,而是扩展到整个组织中更广泛的数据源,如整体业务绩效、财务、市场营销和销售。它收集更广泛的数据,以获得更深入的见解,为战略决策提供依据。 人力分析趋势 企业接受人员分析的程度以及使用人员分析的方式正在发生变化: 关注员工体验。现在的趋势是改善员工的整体旅程,强调健康和富有成效的远程工作体验等方面。 合乎道德的数据使用。随着人们对数据隐私的日益关注,数据使用的道德考量以及保持数据收集和分析的透明度将受到更多重视。 平台整合。人员分析工具将整合来自企业不同软件和应用程序的数据,从而更容易从单一来源获得所有必要的见解。 多样性、公平性和包容性。人们越来越重视利用人员分析来提高组织内部的多样性、公平性和包容性,而且这种情况只会继续增加。 人工智能集成。人力分析平台开始整合人工智能驱动的工具,以简化数据分析,并从复杂的数据集中获得更深入的见解。 人力分析如何改善企业文化? 人力分析为人力资源领导者、经理和高管提供数据支持,使员工绩效与公司目标保持一致。对这些数据进行有效评估并采取行动,有助于制定有效的招聘和培训策略、提高员工参与度,进而促进公司文化的发展。 以下为文章原文: What is people analytics? People analytics is a data-driven method that aims to improve business decisions regarding team members and customers. Rather than solely relying on instinct or anecdotal experience, people analytics provides HR leaders with comprehensive data to make evidence-based, strategic decisions. People analytics integrates HRIS software to assemble and examine extensive information, predict trends, and provide valuable insights relating to the different stages of the employee lifecycle. Also known as talent analytics, companies use people analytics to match the right talent to appropriate roles. Why is people analytics important? People analytics assesses data to enhance the following areas: Recruiting and hiring Onboarding Workforce planning Compensation practices Retention Performance Productivity Engagement Talent analytics is a function that all departments, not just HR, are beginning to adopt. This is a transformation that heralds an increase in HR automation: HR leaders must be ready to embrace these changes to deliver up-to-date, accurate results. Leveraging HR automation tools enables HR leaders to make informed decisions that elevate the employee experience and promote company objectives. In a competitive, sink-or-swim market, HR leaders must demonstrate agility as they continuously adapt to innovations within the workplace. Examples of people analytics People analytics in HR encompasses various aspects of the employee lifecycle. Examples of people analytics include: Attrition prediction. This involves analyzing historical data to identify patterns and factors leading to team member turnover, giving you the ability to predict which of your people are at risk of leaving. Recruitment optimization. People analytics can help with refining recruitment approaches to attract and retain top talent. Performance analysis. This involves evaluating performance data such as key performance indicators (KPIs), goal achievement, and competency assessments to gain insights that aid in identifying high-performing individuals along with the factors that contribute to their success. Employee engagement. Survey data and sentiment analysis provide insights into employee satisfaction, engagement levels, and the factors influencing them. This information helps organizations design initiatives to improve engagement and create a more positive work environment. Learning and development. Analytics can track training outcomes, skill acquisition, and performance improvements resulting from educational programs, helping you optimize training investments that align with business goals. Key people analytics metrics Early turnover rate. This metric refers to the percentage of people leaving within the first year of working at a company. It helps with assessing retention efforts. Absence rate. This measures how often a person is unexpectedly absent from work, whether that’s due to sickness, stress, or other personal circumstances. A high absence rate among team members can indicate dissatisfaction in the workplace. Engagement scores. These capture how committed and satisfied people are about their work and the organization as a whole. Performance ratings. These evaluate individual or team performance against predefined metrics and goals. Cost per hire. This assesses the total expenses of the hiring process, including advertising, interviewing, and onboarding costs. How to use people analytics to make decisions The effectiveness of HR people analytics depends on putting the insights gleaned from data into action. The steps below will help you make clear and knowledgeable decisions for your organization: Define objectives. Clearly outline the specific organizational challenges you aim to address using people analytics. Data collection. Gather relevant data from your HRIS or HCM, performance reviews, surveys, and other sources. Analysis. Employ statistical methods, visualization tools, or a people analytics platform to draw meaningful insights from the collected data. Identify patterns. Look for correlations and trends in the data that offer insights into workforce behaviors and the causes of your organization’s challenges. Make informed decisions. Use the insights you’ve gathered from the data to make evidence-based decisions that help your organization reach its objectives. People analytics dashboard A people analytics dashboard is a visual representation of key HR metrics and data. It provides critical information at a glance, such as turnover rates, engagement scores, performance data, and recruitment statistics. A dashboard empowers HR leaders and managers to track and understand workforce metrics so they can easily make informed decisions. A user-friendly and informative dashboard allows stakeholders to access and interpret essential data swiftly without having to rely exclusively on an IT department or analyst. How to successfully implement a people analytics system Incorporating talent analytics into multiple departments demands that HR leaders step up to guide others through this new initiative. HR leaders can support the integration of people analytics by implementing these practices: Lead by example. Demonstrating proficiency in, or at least an understanding of, people analytics allows HR leaders to use it effectively and set an example for others as they learn the ropes. Observe. HR leaders can identify the level of data collection they currently conduct at their organization and take note of the prevailing data analysis methods the company uses, e.g., data collection techniques and categories and which HR leaders are accountable for the data. Introduce all HR professionals to people analytics. Providing people analytics “basic training” for all HR personnel will improve their knowledge of the system. Acquainting HR professionals with people analytics empowers them while infusing this method into the company culture and mindset. Train the analytics team. It’s important to educate people analytics specialists on how to read and scrutinize data, watch out for inaccurate data, and make data-informed decisions. These HR professionals determine the effectiveness of the talent analytics system. Be aware of potential pitfalls. Companies can use an HRIS platform to simplify, filter, and present the data in a digestible manner to managers. Another challenge to be mindful of is the essential protection of people’s privacy through data encryption and adherence to an honest, fair, and transparent policy. People analytics vs HR analytics Professionals use the terms “people analytics” and “HR analytics” interchangeably. However, there’s a difference between the two: HR analytics focuses on leveraging the HR department’s data to understand and manage the workforce. It delves into individual team members’ behaviors, performance, and engagement, aiming to optimize various HR processes across the employee lifecycle. While people analytics also uses HR data, its focus extends beyond this to wider data sources across the organization, such as overall business performance, finance, marketing, and sales. It gathers a broader spectrum of data to gain deeper insights that inform strategic decisions. People analytics trends The extent to which organizations embrace people analytics and the ways they use it are already changing: Focus on employee experience. There’s a shift toward improving the overall employee journey, emphasizing aspects like wellness and productive remote work experiences. Ethical data use. With the increased concern around data privacy, there’ll be greater emphasis on ethical considerations around data usage and maintaining transparency in data collection and analysis. Platform integration. People analytics tools will integrate data from an organization’s different software and apps to make it easier to get all the necessary insights from a single source. Diversity, equity, and inclusion. There’s a greater focus on using people analytics to improve diversity, equity, and inclusion within organizations and this will only continue to grow. AI integration. People analytics platforms are starting to integrate AI-driven tools to streamline data analysis and derive deeper insights from complex data sets. How can people analytics improve company culture? People analytics provides HR leaders, managers, and executives with data to support the alignment of employee performance with company objectives. Effectively assessing and acting on this data contributes to effective hiring and training tactics, employee engagement, and in turn, a robust company culture.
    观察
    2024年02月26日
  • 观察
    有关AI+CRM的一些观察和思考 作者:杨嘉琦 到了今年,客服和CRM相关的SaaS领域我们看到了这样的发展趋势,从最早Tool发展到Data服务爆发再到最近大火的AI。其实作为一线的从业人员,在Tool阶段我还也是有很多感慨的,但今天主要聊AI。很多CRM厂商都是Salesforce的跟进者,举几个最近关于AI+CRM的新闻: 2016年10月Dreamforce大会CRM全方位AI平台“爱因斯坦”与大家见面 2017年3月百会(Zoho)发布第四代CRM产品。它融合了数据挖掘和机器学习技术,能够智能识别重要客户、寻找附近的客户、推荐工作流配置、建议联系潜在客户的最佳时间等。 2017年3月硅谷人工智能专家加盟销售易,外勤365发布AI平台发布。 1、技术创新推动下的产品升级 技术和创新永远是IT行业的核心竞争力,在CRM行业我们也看到这样的发展历程。以Salesforce为例。一方面从CRM切入向其他业务扩张建立企业办公生态,另一方面不断利用新的技术推进产品升级。人工智能风头正劲,而Salesforce下一站的主要方向也正是打造CRM全方位AI平台从技术和服务上继续建立壁垒。 2、人工智能在CRM中可以发挥的能力 人工智能核心价值一定要有应用场景和商业模式,针对真实业务场景的解决方案才是关键。就像智能家居现在遇到了瓶颈,一方面是没有解决真实用户痛点,另一方面没有达到符合用户预期的效果,解决方案不完整。在有限的人工智能能力下,找到可行、可用、有价值的解决方案是CRM厂商现在最需要考虑的。 那么在营销场景下需要做什么,我想这个问题的答案不会偏离CRM本身的作用和其进一步的扩展。AI+CRM解决的仍然是以信息技术为手段,有效提高企业收益、客户满意度和雇员生产力。拥有强大而快速的数据处理能力和机器学习的人工智能结合营销真实场景后,我想可以发挥以下三种渐进能力: 聪明干体力活。机器代替之前有规则的大量需要人做的重复工作并逐渐自我优化 辅助决策。通过智能洞察和风险提醒来辅助人决策 发现新大陆。新线索、信息甚至知识的发现 3、AI+CRM的实施思路 在可行、可用、有价值的目标下,我们讨论了人工智能在营销场景下可以发挥的三种能力,在市场的具体实施中我们也看到了AI+CRM的两种现有思路: 1)更加智能的SFA 有人认为CRM=SFA,但遗憾的是很多厂商还是做不到更别提超越了。在可预期的情况下,“个性化”且不断优化的服务和更加智能的自动化可以有效提升一线销售人员的生产力,及时的数据分析和风险检测可以辅助管理者更快的发现问题并及时作出决策。这种思路的作用体现在全面管理客户、精细量化行动和快速响应,更加适合现有功能和服务已经比较完善的厂商。这类厂商也可以将这种思路下的AI能力赋予到其PaaS平台中,与行业紧密的结合后效率的提升可能是数倍。 2)预测营销+CRM 之前都在讨论预测营销,也说过预测营销处在CRM以上的高层级,当然CRM厂商升级入场也是可以预料的。预测营销+CRM可以达到什么样的效果,虽然看上去封闭了,但可能是预测营销2.0时代的开始。 国内系统开放性不足一直也在制约着预测营销在国内发展,全自营的预测营销+CRM似乎给预测营销带来了新的机会。潜在客户预测、线索评分、客户画像等都是可以应用的场景,预测营销+CRM我们也看到了以下优势: 更多的数据来源。CRM本身多样性和开放性将带来更多的数据来源,如跟进过程数据、交易数据、呼叫中心、邮件、客户触点等数据。相较于之前的客户数据,与CRM的结合将带来更多的行为数据。 实时的数据获取和更快速的调整。数据的采集更为实时,持续的反馈和检测让技术和业务模型的调整可以得到更快速的响应。 更加贴近业务场景的应用。预测营销作为CRM的一个组件,通知和建议可以展示在最适当的应用场景中,与系统或者说业务本身融为一体。 以上的思路看来仍然无法避免实施成本和教育成本高的问题,找到合适的目标客户尤为关键,当然垂直领域和行业切入不妨是个好思路。大幕刚揭开,我相信并期待其他思路的发现和实践。2018年AI恐将是CRM的一个标配,轻量、快速的将AI应用到中小企业中需要大家继续去探索和努力。AI的发酵也会为厂商带来一些额外的品牌价值的加成。 企业服务本身是一个需要深耕的领域,AI可能不是一个弯道超车的捷径,但很可能会是让人掉队的壁垒。
    观察
    2017年08月01日