做智能营销,数据分析真有那样重要?丨营销十日谈
<img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgteN6ELfuHNYCk3XvakHk5XsZyJMPm5epibVBqE1hu9Fb8sc8MLXQcWA/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">倘若</span>在制定营销策略之前,<span style="color: black;">无</span>进行<span style="color: black;">仔细</span>的复盘和数据分析,而直接<span style="color: black;">起步</span>营销工作,就<span style="color: black;">没法</span><span style="color: black;">晓得</span>策略<span style="color: black;">是不是</span><span style="color: black;">恰当</span>,<span style="color: black;">是不是</span><span style="color: black;">能够</span>达到预期的效果。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">在上一期,<span style="color: black;">咱们</span>提出了关于<a style="color: black;">智能营销的十个突破点</a>,本文将就<span style="color: black;">第1</span>点进行阐述。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;">缺少<span style="color: black;">仔细</span>的原始数据分析环节</strong><strong style="color: blue;"><span style="color: black;">●<span style="color: black;">●</span></span></strong></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">所有的营销都需要在一<span style="color: black;">起始</span>展开系列的数据分析,来挖掘商机、寻找营销<span style="color: black;">目的</span>,然而<span style="color: black;">日前</span><span style="color: black;">都数</span><span style="color: black;">状况</span>下<span style="color: black;">是由于</span>营销人员筛选标签<span style="color: black;">或</span>制定规则来直接构建营销的<span style="color: black;">目的</span>。但这其实少了关键的一步,<span style="color: black;">便是</span>无论<span style="color: black;">是不是</span>有历史的类似营销存在,都需要对<span style="color: black;">这次</span>营销的<span style="color: black;">目的</span>进行<span style="color: black;">仔细</span>分析<span style="color: black;">或</span><span style="color: black;">经过</span>模型计算机会。<span style="color: black;">例如</span>未注册客群<span style="color: black;">便是</span>营销的<span style="color: black;">目的</span>,<span style="color: black;">亦</span>应该对其数据进行<span style="color: black;">恰当</span>分析,然而<span style="color: black;">都数</span><span style="color: black;">状况</span>下是<span style="color: black;">咱们</span>会把这些<span style="color: black;">做为</span>规则直接<span style="color: black;">起始</span>营销。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">在整体数据营销的大框架上,<span style="color: black;">咱们</span><span style="color: black;">通常</span>将智能营销分成四部分:</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">●</span><span style="color: black;">第1</span>部分是关于多元<span style="color: black;">途径</span>数据<span style="color: black;">怎样</span>收集和汇集;</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">●</span>第二部分是关于汇集之后的数据<span style="color: black;">怎样</span>进行处理加工,形成基本的原材料用于营销和分析;</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">●</span>第三部分是<span style="color: black;">怎样</span>基于这些数据<span style="color: black;">经过</span>算法、模型等形成文件的策略,<span style="color: black;">指点</span>营销活动;</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">●</span>第四部分是<span style="color: black;">怎样</span>基于营销策略有效的<span style="color: black;">选取</span>通道、时间、内容、权益、<span style="color: black;">制品</span>进行触达,<span style="color: black;">同期</span>完整的进行数据回收、监测。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">谈到这个<span style="color: black;">专题</span>,<span style="color: black;">咱们</span><span style="color: black;">首要</span>看到的是<span style="color: black;">都数</span>互联网企业,<span style="color: black;">例如</span>电商平台,基本上均实现了<span style="color: black;">举荐</span>引擎的大规模应用,即基于用户的<span style="color: black;">海量</span>实时反馈数据、基于商品<span style="color: black;">或</span>人群形成个性化的<span style="color: black;">举荐</span>,而<span style="color: black;">全部</span>过程中,人工干涉和<span style="color: black;">调节</span>的比例在<span style="color: black;">持续</span>减少。在算法配置、<span style="color: black;">方法</span>配置、分桶策略、<span style="color: black;">目的</span>达成、政策规定几个方面可能仍需要人工调配的,但这些<span style="color: black;">更加多</span>的是政策限制,<span style="color: black;">例如</span>首页的banner最后一屏<span style="color: black;">宣传</span>只能<span style="color: black;">安置</span>手机类内容,<span style="color: black;">然则</span>内容本身并不完全受制于人工。<span style="color: black;">由于</span>这是面向的十万、百万、千万、亿万的用户来<span style="color: black;">举荐</span>。引擎本身<span style="color: black;">处理</span>了分析、策略输出、客群触达、用户反馈、闭环优化的大环节。<span style="color: black;">然则</span><span style="color: black;">这儿</span><span style="color: black;">咱们</span>看到的是,除了数据的回收和整理,最关键的是数据的分析和加工<span style="color: black;">运用</span>,而这个过程是高度自动化的。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">亦</span><span style="color: black;">便是</span>说,当营销要具备<span style="color: black;">途径</span>实时性和数据应用能力,<span style="color: black;">那样</span>就需要<span style="color: black;">方向</span>敏捷的<span style="color: black;">针对</span>原始数据进行快速的分析和应用。但<span style="color: black;">这儿</span><span style="color: black;">亦</span><span style="color: black;">触及</span>到<span style="color: black;">有些</span>问题<span style="color: black;">便是</span>模型算法的热<span style="color: black;">安排</span>和调优,这在<span style="color: black;">举荐</span>引擎方面<span style="color: black;">已然</span>得到了验证,关于在生产环境应用方面的内容将在后续<span style="color: black;">文案</span>讲述。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">回到刚才的问题上,<span style="color: black;">咱们</span><span style="color: black;">已然</span>看到了<span style="color: black;">经过</span><span style="color: black;">举荐</span>和重定向的服务能力(<span style="color: black;">例如</span><span style="color: black;">咱们</span>在淘宝刚看完的一个商品就会出<span style="color: black;">此刻</span>首页<span style="color: black;">举荐</span>位置),<span style="color: black;">能够</span><span style="color: black;">提高</span>整体的转化和营销效果。<span style="color: black;">然则</span><span style="color: black;">这里</span>之外,<span style="color: black;">咱们</span>并<span style="color: black;">无</span>看到如今的大规模营销应用此种能力,<span style="color: black;">由于</span><span style="color: black;">都数</span><span style="color: black;">状况</span>下<span style="color: black;">咱们</span>的<span style="color: black;">途径</span>并不是在线网络<span style="color: black;">途径</span><span style="color: black;">能够</span>实时反馈数据。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">然而这不<span style="color: black;">表率</span><span style="color: black;">咱们</span>不需要和<span style="color: black;">举荐</span>引擎<span style="color: black;">同样</span>的能力,<span style="color: black;">咱们</span>的智能营销需要向<span style="color: black;">举荐</span>引擎<span style="color: black;">同样</span>具备高度的自动化和<span style="color: black;">仔细</span>的对数据的分析和<span style="color: black;">运用</span>。</p><img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgFH6WC3gg1yGlUdge6ZamZdUZ6ZbHaz9aKeXhrSyXeZibygDnQ3gweaw/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;">
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">现实的<span style="color: black;">状况</span>是,<span style="color: black;">咱们</span>在开展营销时,一种方式是基于人工经验进行规则<span style="color: black;">或</span>标签的筛选以制定营销<span style="color: black;">方法</span>;另一种是创意性的营销,<span style="color: black;">通常</span>是覆盖所有客群;再有的<span style="color: black;">便是</span>拍脑袋制定人群策略,进行投送。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">况且</span><span style="color: black;">咱们</span>习惯在策略执行未达到预期效果时,因<span style="color: black;">思虑</span>到成本问题,放弃后续执行<span style="color: black;">或</span>实验,而<span style="color: black;">没法</span>继续进行下一步的<span style="color: black;">方法</span><span style="color: black;">或</span>优化<span style="color: black;">意见</span>。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">产生以上问题的根源,在于<span style="color: black;">咱们</span>只关注纯粹的营销结果数据,而<span style="color: black;">无</span>深度复盘营销全流程<span style="color: black;">状况</span>,单纯以ROI导向,而<span style="color: black;">再也不</span>追求迭代和优化,<span style="color: black;">这般</span>就<span style="color: black;">显现</span>了无数的营销<span style="color: black;">方法</span>和规则,却始终找不到<span style="color: black;">长时间</span>可<span style="color: black;">连续</span>的战法。另一点是<span style="color: black;">针对</span>在营销策略初期的工作中,缺少了<span style="color: black;">仔细</span>的数据分析,<span style="color: black;">咱们</span>过度依赖于规则和标签的<span style="color: black;">规律</span>来制定策略,<span style="color: black;">倘若</span><span style="color: black;">无</span>类似AAARRR<span style="color: black;">这般</span>的<span style="color: black;">办法</span>论指引,则<span style="color: black;">火速</span>就容易走偏方向。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">规则<span style="color: black;">亦</span>好,标签<span style="color: black;">亦</span>好,本质上<span style="color: black;">由于</span>经过了业务<span style="color: black;">或</span>IT的抽象和封装,并不完全适合于业务执行人员。而业务人员则只能在<span style="color: black;">无</span><span style="color: black;">仔细</span>分析的<span style="color: black;">状况</span>下,基于制定的标签<span style="color: black;">或</span>规则<span style="color: black;">规律</span>组织营销策略。可想而知,这是一种毫无准备的策略制定,<span style="color: black;">况且</span>这种策略将会越来越多,却毫无继承性和优化余地。</p><img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPggsyCZq1MnTLEictfgcv5jdOFqx56AL3m5Owsym6abOGtvFiae6rmUjFg/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;">
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">数据分析必须参与到营销策略的制定过程中,无论是<span style="color: black;">设备</span>自主还是人工。<span style="color: black;">此刻</span>看来,在<span style="color: black;">咱们</span>每一次营销策略展开之前,<span style="color: black;">咱们</span>需要界定<span style="color: black;">咱们</span>的<span style="color: black;">目的</span>,这个<span style="color: black;">目的</span>一方面可能是数据分析得来的,另一方面可能是本来就存在的。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">例如</span>信用卡在获取用户时,总有一批用户是被审批拒绝的,<span style="color: black;">那样</span>这批用户是企业在营销中必须<span style="color: black;">思虑</span>的营销<span style="color: black;">目的</span>,<span style="color: black;">例如</span><span style="color: black;">经过</span>回捞这批人、重新授信,这是<span style="color: black;">已然</span>存在的<span style="color: black;">目的</span>。另一种是要经过挖掘才会<span style="color: black;">发掘</span>,<span style="color: black;">例如</span><span style="color: black;">经过</span>对在线<span style="color: black;">途径</span>用户的逾期数据<span style="color: black;">发掘</span>,整体的逾期率比其他<span style="color: black;">途径</span>高,<span style="color: black;">那样</span>就需要基于该<span style="color: black;">发掘</span>去制定针对性的营销<span style="color: black;">目的</span>。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">再<span style="color: black;">例如</span>今天企业从自有业务收集了用户的数据,但是<span style="color: black;">因为</span>参与度和业务达成率低,<span style="color: black;">无</span>获取客群的<span style="color: black;">更加多</span>信息,此时就会借助<span style="color: black;">外边</span>数据进行信息的补全,丰富客群的营销内容。这种<span style="color: black;">便是</span>典型的需要<span style="color: black;">弥补</span>数据后,<span style="color: black;">经过</span>分析,<span style="color: black;">才可</span>寻找到用户的兴奋点和诉求点;<span style="color: black;">倘若</span>缺少这个过程,只是简单进行刚性的<span style="color: black;">途径</span>内容触达,则<span style="color: black;">无</span>任何效果。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">然则</span>在<span style="color: black;">经过</span>数据分析支持营销动作方面,<span style="color: black;">首要</span>要满足业务人员对数据的可获取性和可分析性,业务人员<span style="color: black;">不可</span>像过去简单的基于标签<span style="color: black;">或</span>规则筛选,这只能是第二步,<span style="color: black;">第1</span>步是企业需要一个综合各方面数据的环境,形成数据的Catalog,并<span style="color: black;">这里</span><span style="color: black;">基本</span>上,<span style="color: black;">供给</span>可用于数据分析的工具,<span style="color: black;">例如</span>报表、数据处理工具、建模工具,这使得业务人员可参与到数据的加工分析过程中,形成事前的基本分析要素和结果。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">当然,更加直接的方式,是<span style="color: black;">供给</span><span style="color: black;">更加多</span>基于原始数据分析呈现的报表,而这些报表不只是<span style="color: black;">供给</span><span style="color: black;">有些</span>结果数字,而是<span style="color: black;">供给</span>和展现需要跟进的营销机会和线索,这些机会和线索<span style="color: black;">实质</span>上<span style="color: black;">便是</span>经过分析而达成的营销<span style="color: black;">目的</span>,<span style="color: black;">例如</span>逾期率、审批未<span style="color: black;">经过</span>的用户等,而此时<span style="color: black;">有些</span>标签的<span style="color: black;">道理</span>才会显现出来。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">数据分析参与营销过程的另一点,则是在营销执行过程和营销结果方面,<span style="color: black;">亦</span>需要进行快速的复盘和分析,<span style="color: black;">目的</span>是在过程中<span style="color: black;">调节</span>和优化策略、降低损失。<span style="color: black;">例如</span>经过执行<span style="color: black;">发掘</span>了营销策略<span style="color: black;">或</span>规则中的漏洞,<span style="color: black;">那样</span>就要实时分析,<span style="color: black;">即时</span>阻止策略产生的漏洞<span style="color: black;">导致</span>更大的损失。以<span style="color: black;">举荐</span>引擎为例,<span style="color: black;">能够</span>做到在实时的数据分析中调优策略、<span style="color: black;">调节</span>客群的<span style="color: black;">举荐</span>服务内容。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">在营销结果方面的数据分析,<span style="color: black;">实质</span>上是希望<span style="color: black;">大众</span>在关注ROI的<span style="color: black;">同期</span>,<span style="color: black;">经过</span>数据分析,挖掘<span style="color: black;">更加多</span>的潜在价值和机会,比提取响应人群的特征与原始策略制定的人群进行比对和分析,这有助于调优<span style="color: black;">方法</span>和迭代执行。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">总的<span style="color: black;">来讲</span>,大道理上<span style="color: black;">每一个</span>人和<span style="color: black;">机构</span>都明白,<span style="color: black;">然则</span>在执行环节,<span style="color: black;">咱们</span>其实<span style="color: black;">无</span>做到事前、事中和事后的深度分析。在这方面<span style="color: black;">咱们</span>需要向银行的信用卡业务学习,面对<span style="color: black;">危害</span>问题,<span style="color: black;">她们</span>将贷前风控<span style="color: black;">做为</span><span style="color: black;">第1</span>道关卡,严格<span style="color: black;">掌控</span>准入门槛,降低后续的麻烦;密切监测贷中<span style="color: black;">危害</span>,<span style="color: black;">即时</span>阻断和降低损失,预测能力和<span style="color: black;">危害</span>预判在这个过程中是极为关键的;<span style="color: black;">同期</span>在逾期等问题上,<span style="color: black;">经过</span>对结果的复盘和回溯,反馈至贷前审批环节,优化准入策略,有力的避免<span style="color: black;">危害</span>。这一套<span style="color: black;">规律</span>在营销场景下<span style="color: black;">同期</span>具备参考<span style="color: black;">道理</span>,毕竟这都是<span style="color: black;">针对</span>人的经营。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">未完待续</span></strong><img src="https://mmbiz.qpic.cn/mmbiz_gif/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPg5E06z9Ps8ZcIpHtDHiacUV7aA8RSSXSJokbQzkVz0LFnQzA5VFcGrKQ/640?wx_fmt=gif&tp=webp&wxfrom=5&wx_lazy=1" style="width: 50%; margin-bottom: 20px;"></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">作者:TalkingData 于洋</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">本文原发于:中国营销网</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">照片</span><span style="color: black;">源自</span>于Pexels</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;">关于TalkingData</strong></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">TalkingData 成立于2011年,是国内领先的数据服务<span style="color: black;">供给</span>商。TalkingData秉承“数据改变企业决策,数据改善人类生活”的愿景,围绕TalkingData SmartDP数据智能平台(TalkingData数据中台)构建“连接、安全、共享”的数据智能应用生态,致力于用数据+科技的能力为合作伙伴创造价值,<span style="color: black;">帮忙</span><span style="color: black;">商场</span>企业和现代社会实现以数据为驱动力的智能化转型。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;"><span style="color: black;">举荐</span>阅读:</span></strong></p><a style="color: black;"><span style="color: black;"><img src="https://mmbiz.qpic.cn/mmbiz_png/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPg9rOgZ1e27dvFia2Otg6983O1cypTGEicqeacO4jgfhDRvBA0nZWf739w/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></span></a>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">明明数据和工具都有,为啥还是做<span style="color: black;">欠好</span>智能化营销?丨营销十日谈</p><a style="color: black;"><span style="color: black;"><img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgLcmry4n1ficOdHiasxN47h7r2wCpRVGOrv8QefEvaYTGoiaLsDjs5HBYQ/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></span></a>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">线下门店复苏之路,后疫情时代<span style="color: black;">怎样</span><span style="color: black;">更加多</span>获客?</p><a style="color: black;"><span style="color: black;"><img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgK4iaKZT1RFUcQXbQdnj7yJ3AW9HfI7HYBvcvIOgqHH22wdwtUyQmibOg/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></span></a>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">除了“祖传”,地摊经济还能用到<span style="color: black;">那些</span>营销哲学?</p><img src="https://mmbiz.qpic.cn/mmbiz_jpg/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgQbTBdJB4WSDWDVXwJRoh4jB88vqmq4GcZ5MsIjg1bDic0Cwll9ibCRicQ/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgmiavndrc8PaibsPqVuP9JaDHpVKRzryGaLoDQicgZKEMWMCg8qKuxXMIA/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;">
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;">TalkingData——用数<span style="color: black;">据述</span>话</strong></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">每日</span>一篇好<span style="color: black;">文案</span>,欢迎分享关注</strong></p><img src="https://mmbiz.qpic.cn/mmbiz_gif/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgFs45BP8SCEosEqvfKkjTRpq0cEDN5mcfz2wxqWnqrysy3yEUSzupAw/640?wx_fmt=gif&tp=webp&wxfrom=5&wx_lazy=1" style="width: 50%; margin-bottom: 20px;"><img src="https://mmbiz.qpic.cn/mmbiz_gif/8biaFoWLFLS3dwuhY2ujk0F7hOAHd4YPgxSiafZGhDpicVdvibNfxyyDH7SyLHwt0kjbNwibsOW9YXAEaE2RUzNmphg/640?wx_fmt=gif&tp=webp&wxfrom=5&wx_lazy=1" style="width: 50%; margin-bottom: 20px;">
感谢您的精彩评论,为我带来了新的思考角度。 在遇到你之前,我对人世间是否有真正的圣人是怀疑的。
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