亚马逊账户中为何会出现负数余额,在试验结束前，亚马逊官方会不定时为您更新试验结果

亚马逊账户中为什么会发生负数账户余额,在试验结束前，亚马逊官方会不按时为您升级试验結果引言

亚马逊账户中为什么会发生负数账户余额？：我为什么会具备负数账户余额？假如您应付花费和进行的退款在付款时间内超出销售总额，则您的商家账户余额很有可能为负。您的支付汇报将给予您帐户全部存进和开支账款的详尽一览。花费：花费因商家种类、所卖产品及其常用服务项目而异在试验结束前，亚马逊官方会不按时为您升级试验結果试验結果在试验结束以前，大家每过几个星期都是会升级一次您的试验結果。您可以根据点一下试验操作面板中的试验名字来浏览您的結果。切勿依据初期結果结束试验，由于这种結果很有可能具备虚假性。应等候试验结束，随后再确定要发

亚马逊账户中为什么会发生负数账户余额？

我为什么会具备负数账户余额？

假如您应付花费和进行的退款在付款时间内超出销售总额，则您的商家账户余额很有可能为负。您的支付汇报将给予您帐户全部存进和开支账款的详尽一览。

花费：花费因商家种类、所卖产品及其常用服务项目而异。您可转至支付汇报买卖一览菜单栏，并点一下一个订单信息总额度，查看订单等级的花费详细信息。您也可参考amazon服务项目商业服务解决方法协义和“我要开店”花费价格表，掌握相关您商家帐户的花费信息内容。

产品退款：假如您对于已接到支付的某件产品进行了退款，则amazon将向您缴纳退款所有额度及其退款服务费，并减掉该订单信息一切可用的销售佣金花费。比如，假如某一产品分类的销售佣金为 15%，而您要对总市场价格为 10.00 美金的产品向顾客退款，则您将见到 8.80 美金的扣费（10.00 美金的总市场价格 0.30 美金的退款服务费 - 1.50 美金的销售佣金）。相关退款服务费的其他信息，客户程序amazon服务项目商业服务解决方法协义。

亚马逊官网全文详细信息：

Why do I have a negative balance?

Your seller account may have a negative balance if the amount of fees you owe and refunds you have issued exceeds your sales during a settlement period. YourPayments Reportprovides a detailed summary of all credits and charges applied to your account.

Fees: Fees differ depending on your seller type, the items you sell, and the services you use. You can see order-level details of fees you have incurred by going to theTransaction Viewtab of yourPayments Reportand clicking on an order total amount. You can also refer to theAmazon Services Business Solutions Agreementand theSelling on Amazon Fee Schedulefor information on fees associated with your seller account.

Product refunds: If you issue a refund for a product for which you have already received payment, Amazon will charge you the full amount of the refund, plus the refund administration fee, minus the cost of any applicable referral fees from the order. For example, if you refund a customer the $10.00 total sales price of a product in a category with a 15% referral fee, you will see a charge for $8.80 ($10.00 total sales price $0.30 refund administration fee - $1.50 referral fee). For more information about refund administration fees, please refer to theAmazon Services Business Solutions Agreement.

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在试验结束前，亚马逊官方会不按时为您升级试验結果

试验結果

在试验结束以前，大家每过几个星期都是会升级一次您的试验結果。您可以根据点一下试验操作面板中的试验名字来浏览您的結果。切勿依据初期結果结束试验，由于这种結果很有可能具备虚假性。应等候试验结束，随后再确定要公布哪一版商品描述。

提醒：试验结束后，请尽量在“A 商品描述管理工具”中再次递交获得胜利商品描述，进而公布该商品描述。

表述結果

大家依据在试验期内搜集的数据信息，测算公布每一条商品描述很有可能造成的一系列危害。大家会归纳对于试验中全部已申请注册 ASIN 的結果。大家给予下列哪种結果：

某一版本的商品描述很有可能更强。比如，假如数据显示版本 A 更强的概率为 75%，则代表着在我测算出的很有可能危害中，公布版本 A 有 75% 的可能性会提升销售量/销售总额。

针对每一版商品描述，大家会表明： 销售量、销售总额、转换率、每一位唯一真实身份来访者选购的产品总量及其分发给该版本的样本数。转换率就是指在试验中见到 A 商品描述并选购了货品的唯一真实身份来访者所占的百分数。样本数就是指查询了每一版商品描述的唯一真实身份来访者的总数。每一位唯一真实身份来访者选购的产品总数相当于销售量除于样本数。

预计年化收益率危害。系统软件仅会为顺利完成的试验添充此一部分。它表明了公布获得胜利商品描述版本在下面的一年内将产生的销售量及销售总额提高预计值。针对置信水平较高的获得胜利商品描述，您会注意到绝大多数预计危害都较为积极主动。针对置信水平较低的获得胜利商品描述，您会注意到“较弱实例”的危害可能是消极的。这是由于试验期内实际效果较弱的商品描述事实上仍有可能伴随着時间的变化实际效果更强一些。

预计年化收益率危害

要预测分析一年内的危害，大家会测算获得胜利商品描述的日均销售量提高数，随后乘于 365。这是一个估计值，在其中沒有考虑到周期性、价钱转变或别的危害您具体业务流程的要素；此值仅作参考，我们不能确保一切盈利提高。

【很有可能】列表明的是大家测算的很有可能結果范畴的正中间值 (50%)。【最好实例】和【较弱实例】列表明这种最后的 95% 可信区间。

无判定結果

试验结束后也许会表明无判定結果，或表明某一版本的商品描述好于另一个版本的置信水平较低的結果。但是，这种結果依然很有使用价值。

下列是造成试验得到无判定数据的一些缘故：

您对商品描述所做的变更力度过小，没法明显更改顾客个人行为

总流量不足高，不能明确置信水平较高的获得胜利商品描述

您检测的2个版本的商品描述在促进销售量层面有着类似的实际效果

大部分消费者在进行选购决策时并不尊重您对商品描述做的变更

在试着了解无判定結果时，请参照您的试验假定。比如，依据您的变更內容，无判定結果会告诉你某类种类的商品描述不值项目投资，因为它不容易危害顾客个人行为。或是，您可以得知二种推销商品的形式有同样实际效果。您可以运作别的试验来认证您在以前检测中的发觉。

试验方式

这种相关试验方式的详细说明可协助您掌握大家如何选择试验获胜版本，完成新项目实际效果；但是，试验并不是强制性规定。

试验根据本人顾客帐户。在试验期内，每一个见到您的商品描述的顾客帐户都被视作试验的一部分。顾客会任意分派到某一版本的商品描述，只需在试验期内鉴别到该顾客，系统软件便会为其表明同一项商品描述，不会受到机器设备种类或别的要素的危害。样本数不包括无法识别顾客的网页页面的浏览量。大家也许会从样版中全自动删掉一些种类的数据信息（如统计分析出现异常值），以提升結果的精确性。

大家应用贝叶斯分析方式来剖析试验結果。这代表大家会依据实体模型和具体试验結果搭建一个概率分布方式。大家会汇报后验概率遍布的均值效用值（就产品总数转变来讲）及其 95% 可信区间（也称之为“可信区间”），并在试验期内依据从开始至今搜集的全部试验数据信息每星期升级。获得胜利解决的置信度就是指概率遍布时会对产品销售造成充分危害的結果所占的百分数。

要预测分析一年的危害，大家会测算试验期内每日获得和丧失的解决销量的均值误差，随后乘于 365。大家依据后验概率遍布给予知名度 95% 可信区间。

亚马逊官网全文详细信息：

Experiment Results

We’ll update your experiment results every few weeks until it ends. You can access your results by clicking on the experiment name in theexperiments dashboard. Don’t end your experiment based on early results, as those can be misleading. Instead, wait for your experiment to end before deciding what content to publish.

Tip:When your experiment ends, make sure to publish the winning content by re-submitting it in the A content manager.

Interpreting Results

Based on the data collected during the experiment, we calculate a range of possible impacts of publishing each piece of content. Results are aggregated for all enrolled ASINs in an experiment. We provide a few kinds of results:

Probability that one version of content is better. For example, if we say there is a 75% probability that Version A is better, that means that 75% of the possible impacts that we calculated show a likely positive units/sales lift from publishing Version A.

For each version of content, we show: Units, sales, conversion rate, units sold per unique visitor, and sample size assigned to that version. The conversion rate is the percentage of unique visitors in the experiment who saw the A content and made a purchase. Sample size is the count of unique visitors who saw each version of content. Units per unique visitor is units divided by sample size.

Projected one-year impact. This section is only populated for completed experiments. It shows an estimate of the possible incremental units and sales over the next year from publishing the winning version of content. For high-confidence winners, you will notice that most of the projected impacts are positive. For low-confidence winners, you will notice that the ‘worse case’ impact may be negative. This is because there is still a chance that the content that performed worse during the experiment may actually perform better over time.

Projected One-Year Impact

To project one year impact, we calculate the average daily sales increase of the winning content and multiply by 365. This is an estimate which doesn’t take into account seasonality, price changes, or other factors that would affect your business in the real world; it is provided for informational purposes only and we cannot guarantee any incremental benefits.

TheLikelycolumn shows the median (the 50th percentile) of the range of possible outcomes we calculated. TheBest CaseandWorse Casecolumns show the 95% confidence interval of those outcomes.

Inconclusive Results

An experiment can end with results that are inconclusive, or results that show a low confidence that one version of content is better than another. However, these results can still be valuable.

Here are some reasons why an experiment may have inconclusive results:

The change you made to your content was too small to significantly change customer behavior

There wasn’t enough traffic to determine the winning content with high confidence

The two versions of content you tested were similarly effective in driving sales

The change you made to your content isn’t something that most customers care about when making a purchase decision

Refer to your experiment hypothesis when trying to make sense of inconclusive results. For example, depending on what you changed, an inconclusive result can tell you that a certain type of content isn’t worth investing in because it doesn’t affect customer behavior. Or, it can tell you that two ways of merchandising your product are similarly effective. You can run additional experiments to confirm what you’ve learned from your earlier tests.

Experiment Methodology

These notes on experiment methodology may help you understand how we choose an experiment winner and project impact; however, this is not required to run an experiment.

Experiments are based on individual customer accounts. During an experiment, each customer account that sees your content is considered part of the experiment. Customers are randomly assigned to view one version of content will see that content persistently for the duration of the experiment regardless of device type or other factors, as long as the customer can be identified. Visits to your page where a customer cannot be identified are not included in the sample size. We may automatically remove certain types of data from the sample to improve the accuracy of results, such as statistical outliers.

We use a Bayesian approach to analyze experiment results. This means we construct a probability distribution based on a model as well as the actual results of the experiment. We report the mean effect size (in terms of change in units) as well as the 95% confidence interval (also known as a credible interval) of the posterior probability distribution, which is updated weekly during the experiment based on all experiment data collected since the start. The confidence of a winning treatment is the percentage of outcomes in the probability distribution that show a positive unit sales impact.

To project one year impact, we compute the average difference between the winning and losing treatment sales per day for the duration of the experiment so far and multiply by 365. We provide a 95% confidence interval for the impact which is based on the posterior probability distribution.

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