英文原文
The focus of the new analytics is the complete mapping of every customer journey that takes place in your shop. The analyzable core of the Customer Journey, i.e. the path from the front page of your shop to leaving the page, ideally after the purchase is completed, is the Click Through Rate (CTR). Essentially, it represents the proportion of customers who have clicked on an article or advertising material and is displayed in the analytics tool as follows: A certain amount of loss at each step of this process is completely normal in most cases, even unavoidable, but outliers can provide important clues for improvement. Your goal is to keep the exit rate, i.e. the percentage of customers who leave your shop before making a purchase, as low as possible. Your shop's stock is also relevant for the interpretation of the values. A shop for branded clothing will generally have a CTR between 0% and 100% at all steps, whereas a shop for mechanics, where you can buy nuts and bolts individually, could have a shopping basket click rate of well over 100%. In such cases it is often appropriate to look at the individual user session. In the following you will find some explanations of the most important KPIs displayed in the Analytics Tool and initial interpretation approaches for them. Search Clicks per search should generally remain relatively constant across all products, especially for shops with a specialized range of products. However, if, for example, a search term has a high number of searches, but only a low percentage of clicks, you can check the following potential exit reasons, for example: Product is not offered Product is out of stock Product was not found Point one leaves you only one option for action, but point two could be improved, for example, by devaluating out-of-stock articles more strongly in the search results. Point three might be an indication of an error in the search logic of your shop, e.g. by which accessories for an article are weighted over the actual product and displace it from the top results. Shopping Cart If a much sought-after term has an acceptable CTR, but an unusually large number of users finish their shopping without adding the item to their shopping cart, you should first check the most common sources of error in this case as well. Product is out of stock Product exists in different sizes/colors/etc. - most popular is out of stock Product has special conditions Product details / pictures are not sufficiently available The first two points are similar to the search step - perhaps the delivery status or time is only displayed on the product page or can only be filtered by size there. Here it is also important to know the customer base: If your customers are looking for fan shirts of a metal band and these are marked as generally available in the search view, the high exit rate could be due to the fact that only sizes XS and S are available. FactFinder's Personalization module, suitable ranking rules for variants, and frequent delta updates of product data can help here. Special delivery conditions apply to products such as Amazon Plus items, i.e. an item that appears attractive at first glance can only be purchased together with another, or is only available from a certain total purchase value. Here you can maximize customer goodwill and minimize exit rates by clearly marking such special conditions on the search results page. Last but not least, the product detail page must of course also convince the visitor. Can they find the exact specific product data for technical products? Are clothing articles equipped with sufficient, high-resolution product images from all angles on models? Are there any product preview videos for DIY products that give the customer an insight into the construction? Purchase The last and most important step in the customer journey. Dropouts in this phase are primarily due to three reasons: Payment methods Shipping Lengthy registration process If, for example, the only payment options you offer are credit card and advance payment by bank transfer, you will particularly discourage younger customers (credit card) and generally all urgent buyers (advance payment). If the customers are jumping off in rows despite PayPal and invoice as an option, the problem could also be with your shipping conditions. If you still charge 8€ for shipping even with a three-figure value of goods, this can quickly drive your customers into the arms of the competition, even if you offer the best prices. Last but not least, a lack of user-friendliness could also drive the potential buyer away. The user has worked out the perfect trouser + shoe combination in 90 minutes, but between him and the dopamine boost of the purchase made there is now a multi-page questionnaire with a manually entered invoice address, delivery address and various decisions, e.g. newsletter, data processing, etc. As with payment options, the speed of the process is becoming increasingly important today. Sometimes it is better to pay the 3% surcharge for PayPal Express Checkout, just to avoid having to type in your own address twice - without umlauts, of course. Conclusion Of course, all this was only an extremely superficial view of the world of data-driven online shops. The KPIs collected in the shop can directly help you to fine-tune functions of your site, such as suggest and campaigns, and to track the results of your tuning in real time.
中文翻译
新分析功能的重点是完整描绘您商店中发生的每一次客户旅程。客户旅程的可分析核心，即从您商店首页到离开页面的路径（理想情况下是在完成购买之后），是点击率（CTR）。从本质上讲，它代表了点击了某一商品或广告材料的客户比例，并在分析工具中如下显示： 在此过程的每一步出现一定量的流失在大多数情况下是完全正常的，甚至是不可避免的，但异常值可以为改进提供重要线索。您的目标是尽可能降低退出率，即在购买前离开您商店的客户百分比。您商店的库存也与解读这些数值相关。一个品牌服装店在所有步骤的点击率通常在0%到100%之间，而一个可以单独购买螺母和螺栓的机械商店，其购物车点击率可能远超100%。在这种情况下，通常适合查看单个用户会话。接下来，您将找到分析工具中显示的一些最重要KPI的解释及其初步解读方法。 搜索 每个搜索的点击次数通常应在所有产品中保持相对恒定，特别是对于产品范围专业的商店。然而，如果例如某个搜索词的搜索次数很高，但点击率很低，您可以检查以下潜在的退出原因： 产品未提供 产品缺货 未找到产品 第一点只给您一个行动选择，但第二点可以通过例如在搜索结果中更强烈地降低缺货商品的权重来改进。第三点可能表明您商店的搜索逻辑存在错误，例如，某商品的配件权重超过了实际产品，并将其从顶部结果中挤掉。 购物车 如果一个备受追捧的词具有可接受的点击率，但异常多的用户在未将商品添加到购物车的情况下就结束了购物，您也应首先检查这种情况下的最常见错误来源。 产品缺货 产品存在不同尺寸/颜色等 - 最受欢迎的缺货 产品有特殊条件 产品详情/图片不足 前两点与搜索步骤相似——或许配送状态或时间仅在产品页面显示，或者只能在那里按尺寸筛选。了解客户群在这里也很重要：如果您的客户正在寻找某金属乐队的粉丝T恤，并且在搜索视图中这些T恤被标记为普遍有货，那么高退出率可能是因为只有XS和S码有货。FactFinder的个性化模块、合适的变体排名规则以及频繁的产品数据增量更新可以在此提供帮助。 特殊配送条件适用于像亚马逊Plus商品这样的产品，即一个初看起来很有吸引力的商品只能与另一个商品一起购买，或者只有在达到一定的总购买价值时才可购买。在这里，您可以通过在搜索结果页面上清楚地标记此类特殊条件来最大化客户好感并最小化退出率。 最后但同样重要的是，产品详情页当然也必须说服访客。他们能找到技术产品的确切具体产品数据吗？服装商品是否配备了足够、高分辨率的模特全角度产品图片？DIY产品是否有产品预览视频，让客户了解其构造？ 购买 客户旅程中最后也是最重要的一步。此阶段的流失主要有三个原因： 支付方式 配送 冗长的注册过程 例如，如果您提供的唯一支付选项是信用卡和银行转账预付款，您将特别劝退年轻客户（信用卡）和所有急需购买者（预付款）。如果尽管有PayPal和发票作为选项，客户仍然成批离开，问题也可能出在您的配送条件上。即使商品价值达到三位数，您仍然收取8欧元的运费，这会迅速将您的客户推向竞争对手的怀抱，即使您提供最好的价格。 最后，缺乏用户友好性也可能赶走潜在买家。用户花了90分钟搭配出完美的裤子+鞋子组合，但在他与完成购买带来的多巴胺提升之间，现在却隔着一个多页的问卷，需要手动输入发票地址、送货地址以及各种决定，例如是否订阅新闻通讯、数据处理等。 与支付选项一样，流程的速度在今天变得越来越重要。有时，为了避免自己输入两次地址（当然是没有变音符号的），支付PayPal快速结账的3%附加费是值得的。 结论 当然，所有这些只是对数据驱动在线商店世界的一个极其肤浅的看法。在商店中收集的KPI可以直接帮助您微调网站的功能，例如推荐和营销活动，并实时跟踪您调整的结果。
文章概要
本文阐述了如何通过分析客户旅程来优化在线商店。文章以点击率（CTR）为核心指标，将客户旅程分解为搜索、购物车和购买三个关键阶段。通过识别每个阶段顾客流失的常见原因，如缺货、不明确的配送条件或繁琐的注册流程，商家可以有针对性地进行改进，从而有效降低顾客流失率，提升最终的购买转化。
高德明老师的评价
TA沟通分析评价：这篇文章精彩地展示了如何运用“成人自我状态”进行数据分析，来优化商店（代表“父母自我状态”）与顾客（其“儿童自我状态”寻求即时满足）之间的沟通。当顾客的期望在购物流程中得到满足时，便形成了一次顺畅的“互补沟通”，最终导向购买。分析流失点，就如同识别并修正导致“交错沟通”的脚本，这为创造更和谐、更成功的交易互动开辟了充满希望的未来。
焦点解决心理学评价：本文的视角与焦点解决的理念高度契合，它不纠结于“为什么顾客会离开”，而是着眼于“如何让更多顾客顺利完成购买”这一期望的未来。通过分析点击率（CTR）等关键绩效指标（KPIs），文章在寻找“例外”——即那些成功进入下一环节的顾客行为，并从中发现有效的方法。这种对成功经验的关注和对微小改善的持续追踪，展现了构建解决方案的强大力量，预示着一个不断自我完善和持续增长的未来。
佛学专家角色评价：从佛学视角看，客户旅程恰如一段因缘和合的过程。顾客的购买意愿是“因”，购物过程中的种种设置为“缘”。文章通过数据分析来观察和理解这些因缘，是一种深刻的“觉察”。商家优化流程、减少障碍的努力，是在创造善缘，帮助顾客的善愿（购物需求）得以圆满。这种利他的行为本身就在积累福德，每一次成功的购买不仅是商业的成功，也是一次令众生（顾客）欢喜的善行。