电子器件商务确当前情况。阅读 · 发布日期 2020-07-23 14:22 · admin
假如做得正确,过虑器容许客户变小1个网站挑选的不计其数的商品仅有这几项配对她们的特殊的要求和权益。 但是,虽然它是1个中间层面客户的电子器件商务商品的访问,大多数数网站出示1个阴邪沉沉的过虑工作经验。 客观事实上,大家215年指标值显示信息,仅有16%的大中型电子器件商务网站出示1个非常好的过虑工作经验。
鉴于过虑的关键性,大家——全部精英团队Baymard科学研究所——在以往的9个月科学研究客户怎样访问、挑选和评定商品在电子器件商务商品目录。 大家查验了这两个检索,category-based商品目录。 本科学研究的关键是1个大经营规模的能用性科学研究 检测19领跑的电子器件商务网站 与真正的最后客户,自说自话的协议书。
虽然检测数百万美元的网站,检测遇到了以上 7年能用性难题 有关商品目录,过虑和排列。 全部这些难题开展了剖析和缩小成93简约的商品目录能用性指南,35的特殊于过虑能用性设计方案和逻辑性。
( 查询大版本号 )
大家接着 规范的5个美国关键电子器件商务网站 在这些93年指南排名网站和学习培训关键电子器件商务网站制作和完成她们的过虑和排列作用。 这致使了1个标准数据信息库超出45标准数据信息点电子器件商务商品文件目录设计方案和特性,在其中175是特殊于过虑的工作经验。 (您能够查询网站的排名,在公布的一部分完成 商品目录和过虑 标准数据信息库)。
在本文中,大家将细心查询1些有关的科学研究成效 客户的过虑工作经验 。 更实际地说,大家将深层次科学研究下列看法：
在本文中,大家将详细介绍这些 7个过虑的看法 ,显示信息你的能用性检测結果,查验标准数据信息和出示最好实践活动实例为造就1个优良的电子器件商务工作经验过虑。1。 仅有16%的网站出示1个优良的过虑的工作经验
假如做得正确,过虑器容许客户只看到她们的本人要求和兴趣爱好相配对的商品,如某1特殊种类的商品或设计风格或一些特点或特性。 比如,客户将会期待看到“茄克”全部商品种别为“男性”(性別过虑器),“冬天”赛季(主题过虑)和能用的尺寸色调“黑色”和“M”(变异过虑器)。 电子器件商务的非常于走进物理学储存和规定市场销售人员“黑人男性的,冬季茄克尺寸中等。”
但是,这些美好的过虑的先决标准是有1个普遍的过虑器能用于客户钻到特殊的特点和商品层面,她们和她们的特殊的权益很关键。 大多数数电子器件商务网站早已达不到这里。 但是,1个好的过虑必须1定工作经验过虑器不但存在,但将在客户非常容易了解的方法和互动交流,逻辑性遵照客户的期待。
( 查询大版本号 )
在检测全过程中,过虑逻辑性和过虑客户页面常常致使贫苦的工作经验,即便在网站投入了資源在商品标识(即过虑能用性)。 客户必须可以精准定位和运用有关的过虑值和使她们的理想化过虑组成从1个网站以便吸引住使用价值的过虑器。 但是1个明显的4%的检测受试者在检测没法寻找1个网站的过虑选项——虽然积极主动找寻她们。 这是相当关键的,考虑到到留意过虑器-客户合理1样不存在的过虑器。 5和6节,随后,大家将详细介绍两个过虑设计方案方式,能合理地处理在其中的1些客户页面的难题。2。 42%欠缺范围特殊滤波器种类
1个好的工作经验规律是,任何充足关键的商品规格型号所示商品目录项也应做为1个过虑器。 另外,因为显示信息在客户眼前的信息内容,该网站是提示客户,标准是关键的(或,在客户新域,教她们是)。 十分的标准,激励客户过虑。
留意 自制 显示信息工作能力的食品类生产加工商(以杯),提示客户,这是1个关键的指标值,可是并沒有出示过虑的食品类生产加工工作能力。 ( 查询大版本号 )镀金 州大多数数茄克的原材料种类,但沒有过虑原材料。 客户感兴趣爱好的羊毛茄克务必历经全部295件上衣。 ( 查询大版本号 )斯台普斯 列出了绝大多数复印机的复印速率但不容许客户过虑249复印机的复印速率。 ( 查询大版本号 )
当1个商品是1组检索結果目录, 朝向层面的检索 应当为客户出示最佳商品特殊的过虑器,不用客户特定1个种别。 大家碰触了大家的检测結果和朝向层面的检索的主题(和怎样仅有4%的顶级网站出示)在第6节” 电子器件商务的检索当今情况 ”。重要信息内容
一直保证每一个种别都有1组与众不同的过虑器对于特殊种类的商品。 最少,商品规格型号包括在目录项必须出示过虑器,但1系列普遍的过虑器将基本上一直必须。3。 2%欠缺主题过虑器
尽管电视机、拍照机、茄克和衬衫都可以以随便坐落于大多数数电子器件商务网站,查询商品,配对1个特殊的“主题”能够基本上不能能。 虽然这样的专题特性一般是普遍的和中间层面客户的选购决策,大家的标准检测显示信息,2%的顶级电子器件商务网站依然欠缺主题过虑器(尽管适用提高到66%,升高了48%大家最终的科学研究和标准检测的电子器件商务检索 )。
( 查询大版本号 )
“这类事儿我太不抗烦。 她们将丧失了我。 假如有好几个网页页面,我始终不容易获得它,”1个主题解释为他找寻1个茄克的春天 镀金 。 “一般你能够挑选冬天茄克,春天外套或茄克的种类。 ”他最后舍弃该网站。
( 查询大版本号 )
“我看这些设计风格是甚么模样。 随后我想,‘Ayhh,这些全是丑恶的。 ”因此,我上去了,看看我是不是能排列(过虑器,艾德。),由“设计风格”之类的,”1个主题解释说当她找寻1种方法来过虑的设计风格。 仅有1个“枕头种类”过虑器能够在陶器谷仓,她挑选尝试,最后运用任意枕头种类看,带她,基本上沒有1个靠谱的方法让客户在网站上寻找有关条目。
梅西百货 出示1个主题“设计风格”过虑器,最后被检测目标的6%。 以上,1个主题被觉得运用“外套设计风格:休闲娱乐”过虑器。 ( 查询大版本号 )
沒有主题过虑选项,查询她们唯1感兴趣爱好的商品经常被不符合理地为客户消耗時间。 特别这样就真实地挑选选购哪一个新项目(s),由于有关商品将任意分散化在1个商品目录。 在检测全过程中,欠缺主题过虑器常常致使网站被抛下,由于储存的目标要末过早得出结果并沒有把她们要想的商品的种类(比如,春天茄克),或更多的情况下,找寻1些有关的物品,将会是掩藏在1个极大的商品目录可能基本上是不能能的。 网站上有主题过虑器,过虑器,有很高的应用率,常常在5%以上。
最简易的方式从技术性上完成主题过虑器是根据手动式标识商品或商品组。 主题种类设计风格的典型事例(休闲娱乐、浪漫、当代),时节(春、假期),应用标准(户外、水下)和purchase-selection主要参数(划算,物有一定的值,高完毕)。 一些种类十分合适于手动式标识(比如,设计风格和时节一般会迅速和精确的人力标识),而别的过虑器必须普遍的行业专业知识来手动式标识(比如,物有一定的值)。重要信息内容
鉴别并出示重要主题过虑器与众不同的网站和商品种类左右文。 这些一般会必须范围特殊(见第2节)。普遍的忽略是设计风格,应用左右文和purchase-selection主要参数。4。 32%欠缺适配性过虑器
( 查询大版本号 )
“哦,我的天哪,我不容易这样做,而并不是1个网站,这是很难掌控。 我会去照相机店铺和我的照相机,寻找1个合适的状况下。 我不容易去考虑到全部这些选项,”1个主题解释说在尝试寻找1个拍摄包,完成沒有方法变小253袋的规格目录。 论述了主题,“我必须去之间往返,照相机较为维度。 随后它还漂亮。”
不管多么的诱惑的价钱,标准多么的杰出,多么的完善的消费者评价发音或有吸引住力的商品商品的设计方案,最后客户不感兴趣爱好,假如商品是不兼容的。 这将会是1个来看,不管商品的别的特性。 这使得适配性过虑器过虑种类的1个最关键的(自然,仅供patibility-dependent商品种类)。 给客户浏览目录的商品适配已有的新项目是相当关键的,。
尽管大多数数网站都有1个“品牌”过虑器,检测说明,这彻底是唯1种类的适配性不够过虑器。 最先,品牌常常有好几个系列或商品与不一样的适配性层面。 比如,全部想到想到笔记本电脑上兼容器不合适全部人,因而,简易地运用过虑器“想到”不容易给客户全部商品的目录与她们独特的想到笔记本电脑上适配。 其次,几个适配性依靠、第3方商品是1个关键的考虑到要素。 比如,1个“生产制造商”或“品牌”过虑器不容易为客户出示的详细目录配对的MacBook笔记本袖子。重要信息内容
(见章节4和6的“ 电子器件商务科学研究:更好的导航栏和归类指南 “patibility-dependent更多商品,包含详细的探讨互连适配的商品在商品网页页面。)5。 1 +过虑值必须断开,但是32%做差
全部3种方式导致了比较严重的能用性难题。 前两个主要表现最差,而断开是主要表现最好是的3种方式——但要是是客户页面的完成十分重视细节。 在深层次细节以前必须完成1个主要表现优良的断开设计方案,大家先扼要详细介绍关键难题的两个方式。答:显示信息全部过虑值
显示信息全部过虑值在1长串让客户很难能可贵到别的过虑种类的简述。 在这里, L.L. Bean 被觉得在9像素高显示信息(减去访问器和实际操作系统软件chrome)。 ( 查询大版本号 )
比如,在检测全过程中,客户会看到1个品牌过虑器与1个品牌3个显示屏过虑值,使它不能能出示的附加的过虑器种类简述以下。 绝大多数的检测目标彻底忽略下列附加的过虑器种类过虑值的长串,一般被长期性过虑栏拉伸两个显示屏或更多。 积极主动的留意,大家的商品目录和过虑指标值显示信息,仅有1小一部分(2%)的关键电子器件商务网站现阶段应用这类方式。B .应用内联可翻转地区
内联可翻转地区,如图所示 斯台普斯 为检测目标,导致好几个互动难题,定义和interaction-wise。 ( 查询大版本号 )
完成内联可翻转地区是更普遍的- 24%的关键电子器件商务网站应用此方式。 但是,它并沒有实行任何更好,由于它带来了1系列难题。 最关键的难题(也无法处理)以下:
翻转 在 翻转(即嵌套循环翻转窗格)变为了并不是1个非常非常容易为客户把握定义。 内联可翻转地区会被置放在更大的可翻转地区的web网页页面,规定客户了解的差别,以免难题。
内联可翻转地区常常会引起“scroll-hijacking”,即客户翻转网页页面时,她们会要想翻转过虑目录,反之亦然。 客户务必持续地观念到自身的要是她们想翻转电脑鼠标光标的部位。 换句话说,1个主导page-browsing方式在互联网上,竖直翻转网页页面,将遭劫持。 (在触碰机器设备上,宽内联能够捕捉客户可翻转地区,使它基本上不能能翻转网页页面而并不是行内翻转地区。)
(假如你想进1步探寻内联可翻转地区的难题,大家 查验发现深层 )。C .删掉过虑值
最终大家检测方式实行比别的两个。 断开的益处是给客户的简述不一样的过虑种类。 这很关键,由于1个欠缺经常引发大家的主题让可伶的过虑挑选仅仅由于她们趋向于与过虑值,第1次很长的目录的过虑器。 断开的另外一个关键益处是,当客户寻找感兴趣爱好的1个过虑器种类时,她们还可以获得1个详细的简述过虑值在该种类(根据点击断开连接)。 因而,断开,融合别的两种方式的益处。
断开过虑值让客户能用的过虑种类简述——看到这里 丽 和全部能用的值在1个种类(当点击断开连接)。 ( 查询大版本号 )
但是,特性优异的断开观查仅有当客户俯览断开联络的风险性是积极主动的插口。 客观事实上,在断开连接检测网站上沒有充足显著,它实行(最少)两个别的方式1样不尽人意,由于一些客户觉得断开目录显示信息全部能用的过虑值。 现阶段,标准检测显示信息,仅有6%的关键电子器件商务网站的断开联络设计方案不够。 尽管并不是许多,但它依然是值得提及的1些完成断开,检测说明是合理的:
依据滤波器的设计方案,前1过虑值能够显示信息附加的值被断开。 网站上显示信息值以前删掉太少——比如,少于6使用价值观——客户常常会被断开的缘故。 1好几个值显示信息时,受试者的过虑种类的简述刚开始快速降低。 (这些数据沒有发现硬限定,但取决于过虑器和过虑的设计方案种类能用。)
在断开集以前,过虑值应当中最受欢迎的,并不是按字母次序或配对的数量。 客户常常会扫描仪特殊的过虑器的名字值,而并不是1个过虑器种类的名字。 比如,她们将扫描仪1个网页页面的笔记本电脑上充家用电器“想到”过虑器,而并不是1个过虑器种类取名为“适配。 ”因而,untruncated值是“意味着”的过虑种类,因而应挑选客户最有将会了解到当看网页页面。
北部的专用工具 列出了时兴品牌过虑器目录时断开(推动最著名的值)。 扩张时,按字母次序列出的值给可预测分析性。 ( 查询大版本号 )
断开的过虑值(1 +),而并不是显示信息全部值或应用内联可翻转地区。 保证客户留意到断开,显示信息1值开启断开以前,显示信息默认设置值,客户最有将会鉴别(即最受欢迎),日风格断开连接设定它有别于过虑值。6。 仅有16%的积极主动促进关键的过虑器
1般来讲,在检测全过程中电子器件商务的网站,大家发现客户觉得归类是网站提议她们挑选,而传统式的过虑栏选项是被大多数数客户是彻底可选的。 规劝式设计方案的标准后,大多数数的网站,因而,有很多种别,必须推动一些过虑器或过虑组成。 好运的是,1种清楚的归类方式出現了在检测网站怎样合理推动1套高宽比关键的过虑器-虽然完成必须很多的过虑设计方案细节的地区。
当检测目标检索 亚马逊 ,一些范畴会高宽比有关的过虑器在商品目录。 这升职将检测目标向更多的挑选管理决策,而并不是访问过度普遍的商品目录。 除提高在商品目录中,过虑的过虑值都维持详细的侧面栏(1个关键细节)。 ( 查询大版本号 )
推动比较有限并挑选过虑值的数量实际意义仅有在绝大部分的客户有兴趣爱好或运用可得到明显的效益。 由于提高滤波器激励客户运用它,明智的应用技术性和慎重,防止诱惑客户过度狭小的过虑目录。 比如,不必简易地应用这项技术性website-wide不管在每一个种别是最受欢迎的过虑器。 在实践活动中,你会常常必须手动式副牧师的种别构造,确保提高过虑器的应用。
沃尔玛 必须更进1步的技术性和推动与笔记本电脑上和键入种类和重要购置过虑器,1致主要参数为客户想买1台笔记本电脑上。 ( 查询大版本号 )
提高过虑器不1定全部同样种类的必须。 她们将会仅仅是1个最关键的商品组成过虑器以前,客户能够申请办理時间进1步伐查具体的商品目录。 客观事实上,提高1次过虑器乃至能够申请办理好几个过虑器为客户出示受欢迎的滤波器组成的捷径。
沒有推动过虑器应用banner-like图型。 大家检测的1些网站营销推广视觉效果盒装的过虑器。 这致使1些科目彻底忽略她们,即便盒子包括了她们正在找寻的滤波器种类——全部因为横幅失明。重要信息内容
假如大家看看过虑特性的关键电子器件商务制造行业,大家看到,特性千差万别。 下面7个最具优点的电子器件商务制造行业早已层叠条形图绘图。 行“可接纳的特性”供参照,勾勒了1个“可接纳”的门坎(但并不是好的)过虑特性——最少依据典型检测遇到的难题。 留意特性差别考虑到制造行业;比如,1个服饰网站必须过虑器少于电子器件网站因为它携带的商品种类,因而,必须1个更优秀的设计方案的过虑器来完成更高的分数。
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虽然有着最低的阻碍出示了1个优良的过虑的工作经验,服饰网站非常是最差全部制造行业的过虑特性,因为1个悲剧的过虑选项不够和贫苦过虑插口。 低于规范的过虑插口将会因为有意优先选择于1个清楚的艺术美学和详实的插口(1箱 不正确的简易 )。 虽然解决商品种类只必须比较有限数量的过虑种类(与别的制造行业相比),很多服饰网站乃至欠缺基础的过虑选项,例如商品原材料和客户评级。
体育和喜好网站过虑的特性不佳。 尽管一部分缘故是1个简易的网站优先选择级艺术美学,相近于服饰制造行业,另外一个缘故将会是混和的视觉效果,spec-driven商品竖直制造行业。 很多商品在这些网站常常是非常视觉效果(玩具、户外用具、体育器械、喜好机器设备),但是,很多也是有两3个技术性特性,将会彻底否定自身,假如她们不配对客户的规范,比如特性、体重和年纪。 因而,客户将有更繁杂的过虑要求为体育和喜好商品比她们一般按时服饰网站。
电子器件及办公制造行业历年来是在其中的1个电子器件商务的制造行业客户出示多种多样过虑器,由于发现很多商品会为客户基本上不能能。 当近看平平淡淡无奇的过虑特性在电子器件和办公室,难题常常是穷光蛋过虑逻辑性和插口。 非常普遍的缺点包含下列:只容许挑选1个过虑值,沒有数据滤波器的界定的范畴,和欠缺对制造行业术语的解释。 虽然1般很多的滤波器种类出示1些电子器件商品和办公室的网站,商品的技术性特性,好几个特性的客户的选购决策是相当关键的——依然致使欠缺适配性过虑器(见本文的第4节)和欠缺范围特殊过虑种类(见第2节)。
家和硬件配置网站出示优良的过虑特性。 这十分合乎该制造行业的技术性特性,和分数都可以以解释为1种历史时间关心出示充足的过虑器(非常是适配性过虑器),它容许客户寻找特殊的洗衣机或无绳钻,考虑她们特殊的规范。 但是,可伶的商品数据信息和构造化商品的广泛欠缺标准抑止滤波特性。
身心健康和美容网站有优良的过虑特性。 公平公正地说,身心健康和美容商品有更少的重要商品特性(数量是1个列外),这代表着网站能够心存侥幸过虑器与高宽比spec-driven商品比要简易很多。 电子器件商务网站在别的制造行业,因而,不可该实体模型其过虑工作经验在身心健康和美容网站,由于她们的过虑要求极可能不一样。
大经营规模商人巨大和多元化化的商品文件目录,商品数据信息构造有严苛的规定,解决和归类——全部的物品都可以以极为艰难。 相融合的混和文件目录高宽比spec-driven和视觉效果商品种类,和大经营规模商人最繁杂的过虑要求。 但是,很显著,最大经营规模的商人都了解这些挑戰并获得了十分积极主动勤奋处理她们,常常根据优秀的过虑逻辑性和数据信息后解决。 这将致使多种多样过虑器出示(包含范围特殊的),这是品质的1个关键缘故商家网站完成最好的过虑特性,乃至考虑到到客户的更繁杂的过虑规定。提升电子器件商务过虑
1些网站做的积极主动潜心于过虑和資源花在商品标识。 针对那些网站,很多难以释怀的filter-related能用性难题与调剂客户的期待和网站完成(实际来讲,过虑设计方案和逻辑性)。 过虑从而意味着的机遇大大提升项目投资收益率,大多数数大中型电子器件商务供货商早已在商品标识和数据信息搜集。
保证过虑能用性,一直保证每一个种别都有1组与众不同的过虑器它包括对于特殊种类的商品。 最少,商品规格型号包括在目录项必须出示过虑器,但1系列普遍的过虑器将基本上一直必须。 现阶段,42%的顶级电子器件商务网站欠缺范围特殊滤波器种类数她们的关键商品竖直。
鉴别并出示重要主题过虑器与众不同的网站和商品种类左右文。 这些一般会必须范围特殊,普遍的忽略是欠缺设计风格,应用左右文或选购挑选主要参数。 现阶段,2%欠缺主题过虑器。
任何商品种别包括patibility-dependent商品(配件、集成化系统软件、备件、耗材等)必须1个适配性过虑器。 这一般是1个过虑器,容许客户特定1个实体模型的名字和电話号码,但它也将会是1个过虑器更通用性的标准,1个过虑器等尺寸、工作能力或能量。 现阶段,32%的网站售卖patibility-dependent商品欠缺适配性过虑器。
的过虑值(1 +)应当被断开,而并不是显示信息所有(2%)或应用内联可翻转地区(24%)。 来保证客户留意到断开,做几件事:显示信息1值断开集以前,保证默认设置显示信息值是客户最将会的值鉴别(即最受欢迎);日风格断开连接自身设定它有别于过虑值。
When done right, filters enable users to narrow down a website’s selection of thousands of products to only those few items that match their particular needs and interests. Yet, despite it being a central aspect of the user’s e-merce product browsing, most websites offer a lacklustre filtering experience. In fact, our 215 benchmark reveals that only 16% of major e-merce websites offer a reasonably good filtering experience.
Given the importance of filtering, we — the entire team at the Baymard Institute — spent the last nine months researching how users browse, filter and evaluate products in e-merce product lists. We examined both search- and category-based product lists. At the core of this research was a large-scale usability study testing 19 leading e-merce websites with real end users, following the think-aloud protocol.
Despite testing multi-million dollar websites, the test subjects ran into more than 7 usability problems related to product lists, filtering and sorting. All of these issues have been analyzed and distilled into 93 concise guidelines on product list usability, 35 of which are specific to filtering availability, design and logic.
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We subsequently benchmarked 5 major US e-merce websites across these 93 guidelines to rank the websites and learn how major e-merce websites design and implement their filtering and sorting features. This has led to a benchmark database with more than 4,5 benchmark data points on e-merce product list design and performance, of which 1,75 are specific to the filtering experience. (You can view the websites’ rankings and implementations in the publicly available part of the product lists and filtering benchmark database).
In this article we’ll take a closer look at some of the research findings related to the users’ filtering experience. More specifically, we’ll delve into the following insights:
Only 16% of major e-merce websites provide users with a reasonably good filtering experience. This is often due to a lack of important filtering options, but from the benchmark data it’s clear that poor filtering logic and interfaces are also causal issues.
42% of top e-merce websites lack category-specific filter types for several of their core product categories.
2% of top e-merce websites lack thematic filters, despite selling products with obvious thematic attributes (season, style, etc).
Of those websites that deal with patibility-dependent products, 32% lack patibility filters (for example, selling smartphone cases without a filter for device type or size).
Testing showed that 1+ filtering values require truncation — yet 32% of websites either have insufficient truncation design, causing users to overlook the truncated values (6%) or use what testing found to be even more troublesome, inline scrollable areas (24%).
Only 16% of websites actively promote important filters on top of the product list (a prerequisite when relying more on filters than on categories).
Filtering performance varies greatly by industry, with electronics and apparel websites generally suffering from insufficient filters (for each of their unique contexts), while hardware websites and mass merchants take the lead in the filtering game.
In this article we’ll walk through each of these seven filtering insights, showing you the usability test findings, examining the benchmark data and presenting best practice examples for creating a good e-merce filtering experience.1. Only 16% Of Websites Provide A Good Filtering Experience
When done right, filters enable users to see only the products that match their individual needs and interests, such as products of a particular type or style or with certain features or attributes. For example, a user might want to see all products in the “jackets” category for “men” (gender filter), for the “winter” season (thematic filter) and available in the color “black” and size “M” (variation filter). It’s the e-merce equivalent of walking into a physical store and asking a salesperson for “a black, men’s, winter jacket in size medium.”
However, a prerequisite to these wonderful powers of filtering is having a vast range of filters available for the user to drill into the particular features and product aspects that are important to them and their particular interests. Most e-merce websites already fall short here. However, a good filtering experience requires the necessarily filters not only to be present, but to be presented in a way that’s easy for the user to grasp and interact with and whose logic follows user expectations.
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Benchmarking the 5 top-grossing US e-merce websites across the 93 product list guidelines identified in the usability study revealed generally mediocre performance. Analyzing the 1,75 performance scores specific to filtering availability, filtering logic and filtering interfaces reveals that:
34% of websites have a poor filtering experience, severely limiting their users’ ability to browse products — even when they have the most basic of product requirements;
5% of websites offer a passable filtering experience — by no means good and with several areas that could be improved;
only 16% of websites provide a good filtering experience, with sufficient filtering types available, a balanced filtering design and a filtering logic that aligns well with user expectations (although, even among these few good websites, most still have room for refinement).
In sections 2, 3 and 4 in this article, we’ll walk through the test findings for three of the core filtering types that typically cause issues: category-specific filters, patibility filters and thematic filters — because 6% of major e-merce websites lack one or more of these.
During testing, the filtering logic and filtering user interface often led to a poor experience, even on websites that have invested resources in product tagging (i.e. filter availability). Users need to be able to locate and apply relevant filtering values and to make their desired filtering binations in order to draw value from a website’s filters. Yet a notable 4% of test subjects were at some point during testing unable to find a website’s filtering options — despite actively looking for them. This is critical, considering that unnoticed filters are — to the user — effectively the same as nonexistent filters. In section 5 and 6, then, we’ll walk through two filtering design patterns that proved effective at solving some of these user interface issues.2. 42% Lack Category-Specific Filter Types
Most of the time, users are interested in filtering a product list across category-specific attributes, and not just the website-wide attributes (such as brand, price, user ratings, etc.). An example would be filtering a list of cameras by camera-specific attributes, such as megapixels, zoom level and lens mount — attributes that aren’t particularly meaningful for other types of electronics, such as TVs.
For example, sleeping bags would need a temperature rating filter, while furniture would need a color filter, and hard drives a capacity filter, and so on. A massive 42% of top e-merce websites lack such category-specific filtering types for several of their core product verticals.
A good rule of thumb is that any product specification that is important enough to be shown in a product list item should also be available as a filter. Moreover, by virtue of displaying the information in front of the user, the website is reminding the user that that specification is important (or, in the case of users new to the domain, teaching them that it is). The very display of the specification, then, encourages users to filter by it.
otice how Williams-Sonoma displays the capacity of its food processors (measured in cups) — reminding users that this is an important metric — but then offers no way to filter the food processors by capacity. (View large version)
Gilt states the material for most jacket types, but without a materials filter. Users who are interested in wool jackets would have to go through all 295 jackets. (View large version)Staples lists the printing speed of the majority of its printers but does not allow users to filter its 249 printers by printing speed. (View large version)
During testing, when users encountered websites that lack basic category-specific filtering, they would give up because they realized they would have to manually locate the items they want by browsing a generic product list containing hundreds of items (for example, to find jackets made of wool, food processors with capacities greater than 14 cups, etc.). Users often took quite a while to fully grasp that a website doesn’t offer such filters, with most simply assuming that “It must be there somewhere,” and not believing that the website could neglect such basics — and being forced to look through hundreds of products.
When a product list is a set of search results, faceted search should present the user with the best-matching product-specific filters, without the user having to specify a category. We touched on our test findings and the topic of faceted search (and how only 4% of top websites offer this) in section 6 in “The Current State of E-Commerce Search.”KEY TAKEAWAY
Always ensure that each category has a unique set of filters specific to the type of product. At a minimum, the product specifications included in the list items will need to be available as filters as well, but a wider array of filters will nearly always be needed.3. 2% Lack Thematic Filters
Thematic browsing patterns are quite mon in physical retail stores, where any sales assistant would be able to help visitors with mon requests, such as “a casual shirt,” “a spring jacket,” “a high-end pocket camera” or “an LED TV with good value for the money.” However, this is no easy task on most e-merce websites.
While TVs, cameras, jackets and shirts can all be easily located on most e-merce websites, viewing products that match a certain “theme” can be nearly impossible. Despite such thematic attributes often being both mon and central aspects of the user’s purchasing decision, our benchmarking revealed that 2% of top e-merce websites still lack thematic filters (although support for it has grown to 66%, up from 48% since our last study and benchmarking of e-merce search).
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“I’m too impatient for this kind of thing. They would have lost me. If there were multiple pages, I would never have gotten through it,” one subject explained as he looked for a jacket for the spring season on Gilt. “Normally you can choose between winter jackets, spring jackets or the type of jacket.” He ended up abandoning the website.
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“I’d look at these to see what the style is like. And then I’d think, ‘Ayhh, these are all ugly.’ So, I go up again, to see if I can sort a little [filter, ed.], by ‘style’ or something,” one subject explained while she looked for a way to filter by style. With only a “pillow type” filter available on Pottery Barn, she had few options to try and ended up applying a random pillow type to see where that would take her — hardly a reliable way for users to find relevant items on a website.
Macy’s offers a thematic “style” filter, which ended up being used by 6% of test subjects. Above, one subject is seen applying a “Coat Style: Casual” filter. (View large version)
Without thematic filtering options, viewing only the products of interest to them was often unreasonably time-consuming for users. This was especially the case when it came to actually selecting which item(s) to purchase, because the relevant products would be randomly scattered across a product list. During testing, a lack of thematic filters often led to website abandonment because the subjects prematurely concluded either that the store didn’t carry the type of product they wanted (for example, spring jackets) or, more often, that finding the few relevant items that might be hidden somewhere in a vast product list would be nearly impossible. On websites that do have thematic filters, the filters had very high usage rates, often above 5%.
The easiest way to technically implement thematic filters is by manually tagging products or groups of products. Typical examples of thematic types are style (casual, romantic, modern), season (spring, holiday), usage conditions (outdoors, underwater) and purchase-selection parameters (cheapest, value for money, high end). Some types are well suited to manual tagging (for example, style and season will often be both fast and aurate for a human to tag), whereas other filters require extensive domain knowledge to manually tag (for example, value for money).KEY TAKEAWAY
Identify and offer key thematic filters unique to the website and product-type context. These will often need to be category-specific (see section 2). Common omissions are style, usage context and purchase-selection parameters.4. 32% Lack Compatibility Filters
Some products are patibility-dependent — that is, a product’s relevance is determined entirely by its patibility with another product that the user already owns or plans on buying. Typical patibility-dependent products are aessories (for example, a case for a laptop that has to fit), products used in conjunction with other products (an audio system that needs to plug into a TV and media players), spare parts (a laptop adapter that needs to have a charger tip and power rating that matches the user’s laptop) and consumables (ink that has to fit an exact printer model).
Finding a spare adapter for a laptop or buying a camera and matching case might sound like trivial tasks, but it turned out to be extremely difficult for our test subjects, who had a pletion rate of only 35%. This means that 65% had to give up or, worse, ended up purchasing a product that they believed was patible but was in fact not.
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“Oh my gosh, I wouldn’t do this — not on a website which is this difficult to navigate. I would go to a camera store with my camera and find a case that fits. I wouldn’t go about looking into all of these options,” one subject explained while trying to find a camera bag and realizing there was no way to narrow the list of 253 bags by size. The subject elaborated, “I’d need to go back and forth between this and the camera to pare the dimensions. And then it also has to look nice.”
No matter how enticing the price, how great the specifications, how perfect the customer reviews pronounce the product to be or how appealing the product’s design, the end user will not be interested if the product is inpatible. This could be a dealbreaker, regardless of the product’s other attributes. This makes patibility filters one of the most important filtering types (for patibility-dependent product types only, of course). Giving users aess to a list of products that are patible with the item they already own is vital, then.
Despite patibility filters being a prerequisite for finding and purchasing patible items, 32% of websites that sell patibility-dependent products have no patibility filters.
While most websites have a “brand” filter, tests showed that this is pletely inadequate as the only type of patibility filter. First, brands often have multiple series or products with different patibility aspects. For example, all Lenovo adapters will not fit all Lenovo laptops; so, simply applying a filter for “Lenovo” would not give the user a list of all products patible with their particular Lenovo laptop. Secondly, for several patibility dependencies, third-party products are a major consideration. For example, a “manufacturer” or “brand” filter would not provide the user with a full list of matching sleeves for their MacBook laptop.KEY TAKEAWAY
Any product category that contains patibility-dependent products (aessories, integrated systems, spare parts, consumables, etc.) will need a patibility filter. This will often be a filter that allows the user to specify their model name and number, but it could also be a filter for a more generic specification, such as for size, capacity or power.
(See sections 4 and 6 of “An E-Commerce Study: Guidelines for Better Navigation and Categories” for more on patibility-dependent products, including a discussion of plete interlinking to patible products on product pages.)5. 1+ Filtering Values Require Truncation, Yet 32% Do It Poorly
We tested three dominant patterns for displaying lists of 1+ filtering values:
displaying all filtering values in one long list,
using inline scrollable areas,
truncating the filtering values.
All three methods caused severe usability issues. The first two performed the worst, while truncation proved to be the best performing of the three methods — but only as long as it was implemented with great attention to details of the user interface. Before diving into the details required to achieve a well-performing truncation design, let’s briefly present the core problems with the first two methods.A. DISPLAYING ALL FILTERING VALUES
The problem observed with displaying all filtering values in one long list is that it makes it impossible for the user to get an overview of the different filtering types available.
Displaying all filtering values in one long list makes it difficult for users to get an overview of the other filtering types. Here, L.L. Bean is being viewed on a 9-pixel-tall display (minus the browser and OS chrome). (View large version)
During testing, users would see, for example, a brand filter with one to three screens of brand filtering values within — making it impossible to get an overview of the additional filter types offered below. The majority of test subjects pletely overlooked the additional filter types below the long list of filtering values and were generally overwhelmed by the long filtering sidebar stretching two screens or more. On a positive note, our product list and filtering benchmark shows that only a small fraction (2%) of major e-merce websites currently use this pattern.B. USING INLINE SCROLLABLE AREAS
Some lists of filtering values are given their own scrollable area (i.e. the area can be scrolled independent of the rest of the page), causing several interaction problems for the majority of test subjects, as well as conceptual challenges for a smaller group of subjects.
Inline scrollable areas, as seen here on Staples, caused multiple interaction problems for test subjects, both conceptual and interaction-wise. (View large version)
Implementing inline scrollable areas is far more mon — 24% of major e-merce websites use this pattern. It did not, however, turn out to perform any better, because it es with a host of problems on its own. The most significant problems (which are also difficult to solve) are the following:
Scrolling within scrolling (i.e. nested scrolling panes) turned out to be not a particularly easy concept for users to grasp. The inline scrollable area would be placed within the larger scrollable area of the web page — requiring the user to understand the difference in order to avoid problems.
Users who wanted to apply a filter could not get an overview of all filtering options because the scrollable area was constrained in height. The usability problem, thus, shifted from not getting an overview of filtering types to not getting an overview of filtering values within each type.
Inline scrollable areas often caused “scroll-hijacking,” whereby the user would scroll the web page when they wanted to scroll the filtering list, or vice versa. The user had to be constantly aware of their mouse cursor’s position whenever they wanted to scroll. In other words, a dominant page-browsing pattern on the web, vertical page scrolling, would be hijacked. (On touch devices, wide inline scrollable areas can trap the user, making it almost impossible to scroll the page instead of the inline scroll area.)
(If you want to further explore the problems of inline scrollable areas, we examine the findings in depth elsewhere.)C. TRUNCATING FILTERING VALUES
The last pattern we tested turned out to perform better than the other two. Truncation has the benefit of giving users an overview of the different filtering types. This is important because a lack of one often caused our subjects to make poor filtering selections simply because they were inclined to interact with the filtering values that were first in the very long list of filters. The other main benefit of truncation is that, when users find a filter type of interest, they also have the option of getting a full overview of filtering values within that type (by clicking the truncation link). Truncation, therefore, bines the benefits of the other two methods.
Truncated filtering values gives users an overview of both the filtering types available — as seen here on REI — and all available values within a type (when the truncation link is clicked). (View large version)
However, the superior performance of truncation was observed only when the risk of users overlooking the truncation link was actively addressed in the interface. In fact, on the tested websites where the truncation link wasn’t sufficiently distinct, it performed (at least) as poorly as the two other patterns, because some users assumed that the truncated list showed all available filtering values. Currently, benchmarking shows that only 6% of major e-merce websites have a truncation link that is inadequately designed. While that’s not many, it would still be worthwhile to touch on some of the implementations of truncation that testing showed to be effective:
Depending on the design of the filter, up to 1 filtering values can be displayed before the additional values are truncated. On websites that display too few values before truncating — for example, fewer than 6 values — users would often be confused by the reason for the truncation. When more than 1 values were displayed, the subjects’ overview of the filtering types began to drop rapidly. (These numbers were not found to be hard limits, but depended on the design of the filter and the number of filtering types available.)
Before truncation sets in, the filtering values should be listed in order of popularity, not alphabetically or by number of matches. Users will often scan for the name of a specific filter value, rather than the name of a filter type. For example, they will scan a page of laptop chargers for a “Lenovo” filter, rather than for a filter type named “patible with.” Consequently, the untruncated values are “representatives” of the filtering type and should therefore be the options that users are most likely to recognize when glancing at the page.
The truncation link should be clearly styled, distinguishing it as an interactive element different from the filtering values right above it. Important clues include the following: using the website’s default link styling (color and/or underlining), using spatial indicators such as a plus sign (+) or arrow icon, indicating the number of matches in the link’s name (“View 23 more”), indenting differently than the filtering values (i.e. breaking the vertical alignment), and visually fading the last value in the truncated list.
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Northern Tool lists brand filters by popularity when the list is truncated (promoting the most recognizable values). When expanded, the values are listed alphabetically to give predictability. (View large version)
More test findings on proper truncation design are explored further in this article.KEY TAKEAWAY
Truncate long lists of filtering values (1+), rather than displaying all values or using inline scrollable areas. To ensure that users notice the truncation, display up to 1 values before triggering the truncation, display default values that users are most likely to recognize (i.e. the most popular), and style the truncation link to set it apart from the filtering values.6. Only 16% Actively Promote Important Filters
Some categories have certain filters that are highly important and beneficial for the user to consider. However, displaying these merely as traditional filters in a filtering sidebar runs the risk of users either overlooking these options or not understanding the importance of making a selection.
Generally, during testing of e-merce websites, we observed that users view categories as something the website suggests they select, whereas the traditional filtering sidebar options are perceived by most users as being purely optional. Following the principles of persuasive design, most websites, therefore, have a number of categories that need to promote certain filters or filter binations. Luckily, a clear pattern emerged during testing for how websites can effectively promote a single set of highly important filters — although implementation requires a number of filtering design details to be in place.
When test subjects searched Amazon, certain scopes would have highly relevant filters promoted atop the product list. This promotion nudged the test subjects towards more informed filtering decisions, instead of browsing overly broad product lists. Besides being promoted atop the product list, the filter values are kept intact in the filtering sidebar (an important detail). (View large version)
For example, if a user navigates to a “movies” category, a highly important filter type to consider would be “format,” with filtering values such as “DVD,” “Bluray” and “digital download” as the types that would be important to most users’ process of selecting a product.
Another example would be a “digital cameras” category, where “camera type” would be a highly important filter to consider, with filtering values such as “point and shoot,” “DSLR,” “mirrorless” and “bridge.”
Promoting a limited and select number of filtering values makes sense only if the vast majority of users either have an interest in or would benefit significantly from applying them. Because a promoted filter encourages users to apply it, use the technique intelligently and sparingly, and avoid luring users into overly narrow filtered lists. For example, don’t simply use the technique website-wide for whatever is the most popular filter in each category. In practice, you will often need to manually curate those categories that have a structure that warrant the use of promoted filters.
Walmart takes the technique one step further and promotes a mix of laptop-size and input-type filters that align well with key purchasing parameters for users looking to buy a laptop. (View large version)
Promoted filters don’t necessarily all need to be of the same type. They could simply be a bination of the most important product filters that users can apply before spending further time investigating the actual product list. Indeed, promoted filters could even apply multiple filters at once to provide the user with a shortcut to popular filter binations.
Two additional implementation details to consider:
Keep the promoted filtering values in the filtering sidebar, too (i.e. in addition to the “promotion” placement). Because users are trained that a filtering sidebar contains all available filters, the promoted filter must be represented in the filtering sidebar as well, since some users will look for the filtering value there.
Never promote filters using banner-like graphics. A few of the websites we tested had promoted filters that were visually boxed. This caused some of subjects to pletely overlook them, even when the boxes contained the very filter type they were looking for — all due to banner blindness.KEY TAKEAWAY
For select categories where an initial filtering selection would be relevant and would benefit the vast majority of users, consider promoting those few filtering values above the product list (for example, using buttons, text links or thumbnails).7. Filtering Performance Varies Greatly By Industry
If we look at filtering performance within the major e-merce industries, we see that performance varies greatly. Below, the seven most dominant e-merce industries have been plotted as stacked bar charts. The row “aeptable performance” is for reference and depicts the threshold for an “aeptable” (but not good) filtering performance — a minimum based on the typical issues that test subjects encountered. Note that the performance difference takes the industry into aount; for example, an apparel website needs fewer filters than an electronics website due to the type of products it carries and, therefore, needs a less advanced design for its filters to achieve a higher score.
(View large version)
Despite having the lowest barrier to provide a good filtering experience, apparel websites notably have the worst performance of all industries for filtering, due to an unfortunate bination of inadequate filtering options and poor filtering interfaces. The subpar filtering interfaces are likely due to a deliberate prioritization of aesthetics over a clear and informative interface (a case of false simplicity). Despite dealing with a product type that requires only a limited number of filtering types (pared to other industries), many apparel websites lack even basic filtering options, such as for product material and user ratings.
Sports and hobby websites suffer from poor filtering performance as well. While part of the reason is a prioritization of simple website aesthetics, similar to the apparel industry, another cause may be the mix of visual- and spec-driven product verticals in the industry. Many products on these websites tend to be fairly visual (toys, outdoor goods, sports equipment, hobby equipment), yet many also have two to three technical attributes that could pletely invalidate themselves if they don’t match the user’s criteria, such as performance, weight and age. Consequently, users will have more plex filtering needs for sports and hobby products than they typically do for regular apparel websites.
The electronics and office industry has historically been one of those e-merce industries that offer users a broad variety of filters, simply because finding many products would otherwise be nearly impossible for users. When looking closer at the lacklustre filtering performance in electronics and office, the problem is often poor filtering logic and interfaces. Particularly mon flaws include the following: allowing only one filtering value to be selected at a time, no user-defined ranges for numeric filters, and a lack of explanation of industry jargon. Despite a generally high number of filter types being offered on several electronics and office websites, the products’ technical nature — several attributes of which are vital to the user’s purchasing decision — still result in a lack of patibility filters (see section 4 of this article) and a lack of category-specific filtering types (see section 2).
Home and hardware websites offer decent filtering performance. This aligns well with the technical nature of the industry, and the score can be explained by a historical focus on offering sufficient filters (in particular, patibility filters), which enables users to find the particular washing machine or cordless drill that meets their specific criteria. However, poor product data and a widespread lack of structured product specifications hold back filtering performance.
Health and beauty websites have decent filtering performance as well. In fairness, health and beauty products have fewer key product attributes (quantity being an exception), which means the websites can get away with much simpler filters than ones with highly spec-driven products. E-merce websites in other industries, therefore, should not model their filtering experience on health and beauty websites because their filtering needs are likely different.
Mass merchants have vast and diverse product catalogs that have strict requirements for product data structures, processing and categorization — all things that can be incredibly difficult to get right. Combine that with a mixed catalog of highly spec-driven and visual product types, and mass merchants have the most plex filtering needs. Yet, it is clear that most mass merchants are aware of these challenges and have made very active efforts to resolve them, often through advanced filtering logic and data post-processing. This leads to a broad variety of filters being offered (including category-specific ones), which is one of the main reasons mass merchant websites achieve the best filtering performance — even taking their users’ more plex filtering requirements into aount.Improving E-Commerce Filtering
Overall, the filtering performance of the websites we benchmarked is passable at best. When it es to filtering, the majority of even the top e-merce websites e up short pared to physical retail, where a customer request such as “a light casual spring jacket in size medium” or “a rugged case for this digital camera” isn’t out of the ordinary.
Some websites do actively focus on filtering and spend resources on product tagging. For those websites, many of the lingering filter-related usability issues have to do with aligning user expectations and website implementation (specifically, filtering design and logic). Filtering thus represents an opportunity to vastly improve the return on investment that most large e-merce vendors have already made in product tagging and data collection.
Filtering on e-merce websites is a major topic that obviously cannot be fully explored in a single article. However, the filtering insights covered in this article hopefully lay the foundation for understanding the current state of e-merce filtering and for creating a good filtering experience:
While mediocre filtering performance is often due to a lack of important filtering options, benchmarking also reveals that filtering logic and filtering interfaces cause severe problems for users. When looking at the users’ entire filtering experience, only 16% of the top 5 US e-merce websites offer a good experience, while 5% offer a passable filtering experience, and 34% have a poor filtering experience, without filters for users’ most basic product preferences.
To ensure filtering availability, always ensure that each category has a unique set of filters specific to the type of products it contains. At a minimum, the product specifications included in the list items will need to be available as filters as well, but a wider array of filters will nearly always be needed. Currently, 42% of the top e-merce websites lack category-specific filter types for several of their core product verticals.
Identify and offer key thematic filters unique to the website and product-type context. These will often need to be category-specific, and mon omissions are a lack of style, usage context or purchase selection parameters. Currently, 2% lack thematic filters.
Any product category that contains patibility-dependent products (aessories, integrated systems, spare parts, consumables, etc.) will need a patibility filter. This is often a filter that allows the user to specify a model name and number, but it could also be a filter for a more generic specification, such as a filter for size, capacity or power. Currently, 32% of websites that sell patibility-dependent products lack patibility filters.
Long lists of filtering values (1+) should be truncated rather than be displayed in full (as 2% do) or use inline scrollable areas (24%). To ensure that users notice the truncation, do a few things: display up to 1 values before the truncation sets in; make sure the default displayed values are the values that users are most likely to recognize (i.e. the most popular); and style the truncation link itself to set it apart from the filtering values.
For select categories where an initial filtering selection would be relevant and would benefit the vast majority of users, consider promoting those few filtering values above the product list (for example, using buttons, text links or thumbnails). Currently, only 16% actively promote highly important filters on top of the product list.
Filtering performance varies greatly by industry, and the key players in your industry might not be a good source of inspiration. Even when adjusted for the different levels of filtering needs, websites in the apparel, electronics and sports industries are significantly behind in the filtering experience offered by mass merchant and hardware websites.
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