工商管理Essay格式：Analysing Online Shopping Behaviour of Google Merchanising Store Customers
本文是工商管理专业的留学生Essay范例，题目是“Analysing Online Shopping Behaviour of Google Merchanising Store Customers（分析谷歌商场顾客的网上购物行为）”，谷歌Analytics分析分析了谷歌Merchandise Store的主页、登录页面、流量来源和活动对转化率和收益的贡献。
The Google Analytics analysis, reviewed Google Merchandise Store’s main pages, landing pages, traffic sources and campaigns on how well they are contributing to conversion and thus revenue.
The reports on Audience and Acquisition showcase what the visitors are doing on the website and if they are being converted. This analysis will give insights and recommendation based on the collected and reviewed data. While evaluating the total number of sessions versus the total number of users for Google Merchandise store as per Figure 1, in the month of May 2017 it can be seen that 70,798 sessions occurred with total users of 55,257. Sessions are usually higher than users because if one user visited the website three times that would be three sessions.
Therefore, at least one visit of 55,257 users would be leading to 55,257 sessions. In this case, because the total session is 70,798; on an average one can analyse that at least 15,541 users would have had multiple sessions which means there is some good loyalty for the website with 1.28 session per user and an average session duration of 2:55 minutes. As a hypothetical example, just because the average session duration is 2 minute 55 seconds, it doesn’t mean that most visitors spend 2:55 minutes on the website, it could mean that a few visitors spend 30 minutes on the website and some spend 0.
因此，至少55257个用户的一次访问将导致55257个会话。在本例中，因为总会话是70798;平均来看，至少15541个用户有过多次访问，这意味着每个用户有1.28次访问，平均会话持续时间为2:55分钟。作为一个假设的例子，仅仅因为平均会话持续时间是2分55秒，这并不意味着大多数访问者在网站上花了2分55分钟，它可能意味着一些访问者在网站上花了30分钟，一些人花了0分钟。
The All Traffic Sources report as per Fig 2 show that the total bounce rate for the website is 44.78%. The traffic coming from Social with median number of users has the highest bounce rate at 62.53% and the highest traffic source of Organic Search has the third largest bounce rate at 50.92% of the total on the website. In this data, one can see that the Social Network and Paid Search should be the final interaction channels which should work towards assisted conversions. Referral, Organic and Direct channels should continue to contribute final conversions along with assisted conversions. Lastly, Display should only focus on assisting conversions in the final stages. So optimizing and channelizing spend on these channels accordingly can bring higher ROI’s.
The new and returning visitors to the Google Merchandise Store should be important as it is essential to retain the existing customers and also find some new ones. When looking at the Behaviour data under Audience section of new vs returning visitors in Google Analytics as per Fig 3, one can see that the new visitors of 50,971 account significantly with 81.39% of the total users and returning visitors are comparatively lower at 11,652 which is only 18.61% of the total users. This can indicate a couple of things, with new visitors seeing such a strong number, it can be seen that the marketing efforts behind brand awareness is done right and people are actually visiting the site for the first time. However, the returning visitor number being relatively smaller could imply that there is not enough incentive or value for people to return to the site. Since the website is not a lead generation site but an e-commerce site, it is imperative to analyse if the visitors are converting by making a purchase and monetising the marketing efforts.
新的和回访游客的谷歌商品商店应该是重要的，因为它是必要的，以保留现有的客户，也找到一些新的。观察行为数据时根据观众的部分新vs返回游客在谷歌分析按图3中,可以看到,50971年的新访客账户显著与总数的81.39%用户并返回游客比较低在11652年只有18.61%的总用户。这可以表明两件事，随着新访问者看到如此强大的数字，可以看出品牌意识背后的营销努力是正确的，人们实际上是第一次访问网站。然而，回访人数相对较少可能意味着没有足够的激励或价值的人返回网站。由于该网站不是一个领先的网站，而是一个电子商务网站，有必要分析访问者是否通过购买和营销努力的货币化转变。
In this case, one can see that the returning visitors have a lower bounce rate at 39.65% compared to the new visitors which are as high as 46.78%. This means that the new visitors spend lower time on the website at 2:32 minutes of session duration and 4.07 average pages per session. On the other hand, the returning visitors come on the website with the decision to purchase and spend more time with 3:55 average session duration and 5:43 average pages per session. The returning visitors were clearly more likely to reach the checkout page and make the transaction. This accounts to the fact that the e-Commerce conversion rate for returning visitors is the highest at 6.60% with US $228,666.69 generated revenue which is 65.65% of the total revenue vs the new visitors at 1.74% with US $119,665.14 generated revenue which is only 34.35% of the total revenue. In short, the returning users have a lower bounce rate, longer average session duration and a higher conversion rate.
Most users are male between the ages of 25-34 from the U.S and speak/read English, therefore it is necessary to further narrow the campaigns so that the organic traffic is a lot more targeted when new visitors come in and there is lower bounce rate. Continuing campaigns that remain relevant w/a bounce rate <30% will lead to higher revenue which can be done by diversifying referral traffic by creating highly relevant and specific ads to each target audience. With all the insights explained above, the analysis show that there seems to be a high amount of referral traffic coming in, particularly through Google Plex with the most successful conversion rate.
大多数美国用户是年龄在25-34岁之间的男性，会说/读英语，因此有必要进一步缩小活动范围，以便当新访客进入时，有机流量更有针对性，反弹率更低。持续保持相关性且反弹率低于30%的广告活动将带来更高的收益，这可以通过为每个目标受众创建高度相关和特定的广告，从而实现推荐流量的多样化。根据以上解释，分析表明似乎有大量的推荐流量，特别是通过谷歌Plex的成功率最高。
The highest revenue that these customers are giving the website should be looked at carefully and optimising the checkout process will make the process easier for these customers to keep returning. After understanding how the customers are converting, other data showed that there were several users that were exiting the site after going on the shopping bag as well as the sign-in page. This means that the website is losing customers even though the completed purchase goal conversion rate is 2.4% which is decent, however there is definite room for improvement. In this scenario, when a customer has an item in the bag and ready to check out, there appears a sign up form and when the customer clicks continue, it brings them back to the home page which is kind of an extra clunky way to get back to the basket which may be hard for customers. So making checkout a smoother and streamlined process can be more helpful. Also, looking at the data with pageviews there seem to be at the most views within 0 to 10 seconds on pages. Those 10 seconds really count and it’s important that you make an impression and that the page is easy to navigate and customers can find what they are looking for when they were on the page. YouTube branded is very popular on the website and users visit this website specifically through YouTube referral traffic. Therefore creating richer paid ads with adding quantity of ads across high traffic areas especially across YouTube will only be more conducive in bringing more traffic to the website.
One of the most fascinating segments to consider about Google Merchandise Store visitors is from which country, region, continent or sub-continent they are coming from. In this report as per Fig 4, the researcher has analysed the location report by selecting from the audience category geo report.
其中一个最吸引人的部分考虑到谷歌商品商店的游客是来自哪个国家，地区，大陆或次大陆，他们来自。在本报告中，如图4所示，研究者从观众类别的地理报告中选取位置报告进行了分析。
The geographical representation of the world indicates the highest quantity of sessions happening in various parts of the world. The consolidated number shows that there was a total of 55,257 users and 70,798 sessions in May 2017, out of which the continent that brought 98% of the total revenue of $348,332 was from the Americas. The actionable metric from this interactive report will be to consider the high traffic coming from the Asian sub-continents with relatively high session duration but are not converting potentially due to the shipping charges or delivery timeline. If the e-Commerce store is for a very niche audience tied to a very specific geographic category in this case the United States, it is probably the best option to divert all the incoming traffic from other geographical locations to social platforms. The Google Merchandise Store ships only to the US and Canada which is only made clear when the visitors reach the Billing Information Page. Hence, users from all other countries drop off on that page.
The acquisition report as per Fig 5 is incredibly valuable to look at the main channels that people are being funnelled to the Google Merchandise Store website.
Organic search leads to highest number of page sessions and highest number of transactions are completed by users that came through Google Plex Referral source. The direct channel which is the second highest source accounts for 23.73% of the total users who are typing in the URL or have the website bookmarked. The e-commerce conversion rate is at 2.30% accounting to the revenue of $49,512.34. The referral source of traffic through email communication to Google staff stands in third place when looking at the users, with lowest bounce rate at 23.85% leading to highest conversion rate at 10.85% accounting $253,884.55.
In the device category report as per Fig 6, the researcher wants to show what devices the visitors are using in order to visit the website. In this case, 68.17% of the total visitors are desktop users who spend the most amount of time on the pages with the lowest bounce rates and account for 98.63% of the revenue with the highest conversion rate at 4.26%. The mobile and tablet users are relatively in the same position with close bounce rates and conversion rates.
The behaviour overview reports explained in this research provides a thorough page content overview in order to analyse the top and bottom performing pages on the website and also understand the performance of every other pages. It sheds light on the user behaviour and their engagement on the Google Merchandise website, analyses how long they are spending time on the website and what particular day and time. The segmentations in this report are to finally gauge if they are going through the marketing funnel, also get an insight on the bounce rates, where the bounces and exits are happening and finally understand the conversions on the products.
行为概览报告解释在这一研究提供了一个全面的页面内容概览，以分析网站的顶部和底部执行的页面，并了解每一个其他页面的性能。它揭示了用户行为和他们在谷歌商品网站上的参与，分析了他们在网站上花了多长时间，以及具体的一天和时间。这份报告的细分是为了最终判断他们是否通过了市场营销渠道，也为了了解反弹率，反弹和退出发生在哪里，并最终了解产品的转化率。
The overview report in the behaviour section as per Fig 7, shows that the homepage is the top performing page on the site with 65,111 page views which is 20.66% of the total page view percentage, followed by the other page basket and so on. The store’s internal audience who are the staff members are using the website who account for the highest page views in the report, while the new users in the graph are the ones who are interested and intend to buy and this number stays stagnant throughout the month with highest bounce rates. This shows the very different traffic and usage patterns specially based on time of the day where weekends are the lowest where the page and product performance drops. The product pages are making the visitors stay for 2 minutes 43 seconds on an average and with a 55% bounce rate on an average at the payments page. This means the company has to encourage visitors to stay longer and complete the payment. This can be done by making the payment process simpler as explained earlier. Having said that, since the returning visitors are bringing the most revenue, it is important to get people to come back to the site and convert them to gain more revenue.
Conversions reports as per Fig. 8 and e-Commerce product performance report as per Fig. 9 give insights on the best-selling products and are very useful and invaluable reports. Just like other reports, these reports state that the total revenue generated on May 2017 was $348,331.83. The top three product pages were Nest Cam Outdoor Security Camera, Nest Cam Indoor Security Camera and the Nest Learning Thermostat 3rd Generation. Nest Cam Outdoor Security Camera contributed to 14% of the total product revenue. Nest’s presence in the main menu tab on the website could be the reason for its high performance and high demand. The unique purchases on these top product pages have a close figure of 283, 285 and 234 respectively. There were no product refund amounts and average of 1.50 quantity. Another important factor to look at is the basket-to-detail-rate which is 41.16% which is the percentage of people that add a product into the shopping basket from the product page and the second one is the buy-to-detail rate which is 14.69% which is the percentage of people that actually purchase a product from going into the product page. In this case, the company needs to prioritize to increase the buy-to-detail-rate figure by making the visitors make enough purchases after adding the products into the basket. On selecting secondary data and drilling down the data further based on regions, this e-Commerce store focuses on United States with the best basket-to-detail-rate for California, however the best buy-to-detail rate for Arizona. So obviously, the volume is variable for different regions and paid campaigns for each regions should be adjusted accordingly. Alternatively, while looking into the time particularly day of the week, it states most of the sales happened over weekdays, therefore the recommendation would be to front load the campaign budget into the beginning of the week and taper it down in to the weekend. Wednesday got the highest basket-to-detail-rate at 57 percent for the top product, therefore the campaign can be focused on basket-to-detail-rate rather than revenue. Key insights on the lost opportunity is that out of 41% of users who reached the cart page, only 14% converted. The rest exited the funnel from the sign in page who are visitors who do not have a Google account set up and also the ones who do not have the time or wish to set up one.
图8所示的转换报告和图9所示的电子商务产品性能报告，提供了对畅销产品的见解，是非常有用和无价的报告。与其他报告一样，这些报告称2017年5月的总收入为348331.83美元。排名前三的产品页面分别是Nest Cam户外安防摄像头、Nest Cam室内安防摄像头和Nest学习恒温器第三代。Nest Cam户外安全摄像头贡献了14%的产品收入。Nest的出现在网站的主菜单选项卡可能是其高性能和高需求的原因。在这些顶部产品页面上的独特购买分别有一个接近的数字283,285和234。无产品退款金额，平均数量为1.50。看另一个重要因素是basket-to-detail-rate比例是41.16%的人将产品添加到购物篮从产品页面,第二个是buy-to-detail率是14.69%的比例的人其实从进入产品页面购买一个产品。在这种情况下，公司需要通过让访问者在将产品放入购物篮后进行足够的购买来提高购买到详细信息的比率。在选择次要数据和根据区域进一步深入数据方面，该电子商务商店以美国为重点，其从购物篮到详细信息的比率最高的是加利福尼亚州，而从购买到详细信息的比率最高的是亚利桑那州。所以很明显，不同地区的付费活动的数量是可变的，每个地区的付费活动都应该相应地调整。或者，当查看时间，特别是一周中的一天，它声明大多数销售发生在工作日，因此建议将前负荷的活动预算到一周的开始，并逐渐减少到周末。周三得到了最高的篮子到细节率57%的顶级产品，因此活动可以集中在篮子到细节率而不是收入。我们发现，41%的用户访问了购物车页面，只有14%的用户转用了。其余的退出漏斗从sign in页面谁是访客谁没有一个谷歌帐户设置，也谁没有时间或希望建立一个。
In this section, the researcher aims at telling a story of Google Merchandise Store as per Fig 10, where return on investment is calculated along with Cost per click, cost per acquisition and engagement levels in the form of impressions. The focus is on giving recommendations for optimising conversions related to the goals and objectives of the e-Commerce store. The shoppers journey on the store starts with Awareness and lays over Interest, Consideration, Purchase, Retention and finally on to Advocacy stages. The various metrics in the above figure effectively maps out the various stages of the shoppers journey and gives a holistic view of all the gaps and successes in that month. While measuring the awareness and engagement KPI’s of the store through Impression metrics, it is important to consider the number of search impressions, retargeting and media impressions, e-mail open-rates, site visits, PR impressions and social impressions in order to optimise the click through rate for all channels. Creative ways to increase the visitors for each of the traffic method is to optimise the title tags for SEO, copy and images for social, paid and display sources and subject lines for email to increase the click through and eventually conversion rate from those mediums. There can be various social contests run to increase visitors and conversions through Social and cause some virality.
在本节中，研究人员的目标是讲述一个谷歌商品商店的故事，如图10所示，其中投资回报与每次点击成本、每次获取成本和印象形式的参与度水平一起计算。重点是为优化与电子商务商店的目标和目标相关的转换提供建议。消费者在商店的旅程从意识开始，经过兴趣，考虑，购买，保留，最后到宣传阶段。上图中的各种指标有效地描绘出了购物者旅程的各个阶段，并给出了当月所有差距和成功的整体视图。在通过印象指标衡量商店的认知度和参与度KPI时，为了优化所有渠道的点击率，考虑搜索印象、重新定位和媒体印象、电子邮件开放率、网站访问量、公关印象和社交印象的数量是很重要的。增加访问者的创造性方法是优化标题标签的SEO，复制和图像的社会，付费和显示来源和主题栏的电子邮件，以增加点击通过和最终从这些媒体的转化率。可以通过各种社交竞赛来增加访客和转化率，并产生病毒式传播。
Google AdWords is clearly not working in this scenario, however there can be attempt made in doing some paid content marketing by integrating Taboola and Outbrain to syndicate the content. The organic traffic to the website are not the highest converters, and the average email subscribers added per month is the market that the store can market to at the lowest variable cost. The recommendation behind increasing the database size would be to segment every email capture for better targeting which is bring ~30 percent+ increase as it does not make sense to show email pop ups to existing subscribers every time they are on the site. The customer lifetime value shows that offering as much value as possible in exchange for an email address or click to buy is essential by optimising landing pages. Maximizing revenue across Social, Paid and Display advertising should be the key focus area, as these channels are the ones with highest cart abandonment rate which has the biggest immediate impact on the revenue. It is important to understand what is important to the customers and where and why are they dropping off. The average e-commerce conversion rate is between 2.5% and 3% based on industry standards (Chaffey, 2018) and the conversion rate for the Google Store is above average at 3.10%. However, with a large number of visitors coming in to the website, the focus is on ways to increase the conversion rate year on year. One huge opportunity is to integrate personalization in to the shopping experience of the customers. Increasing conversion rates on mobile is essential for the store, therefore behavioural targeting using mobile exclusive offers to boost conversions which will be further segmented on customers connected to Wi-Fi. Once we have this data point, it will be easier for the store to predict the conversion rate. Smart couponing will get customers to ‘convert’ for a coupon to drive additional value above improved conversion.
In an attempt to maximize the store’s Average order value of $158.69, it is recommended to upsell or cross sell based on what the customer adds to cart. Likewise, also decrease reliance on coupons by hiding coupon code box for certain traffic sources. There is also a recommendation to create strategy for post-conversion customer engagement and focus on VIP customers where the top 10% of customers generate the highest amount of revenue. Through the google analytics figures for the store, one can see that advocates create the store’s X-factor and virality. They are the lowest CPA channel at $36 with highest LTV generator. Therefore, another recommendation is to identify the top advocates from the referral channel and make the most advantage by creating direct relationships with them and offering a referral program. Since the referred customers are predominantly from Google Plex, they are more loyal and if the store aims at offering a direct incentive to them, 50% of people are more lively to give a referral and people are 4 times more likely to buy when referred by a friend.
为了最大化商店的平均订单价值158.69美元，建议根据客户添加到购物车中的内容进行追加销售或交叉销售。同样，也可以通过隐藏特定流量源的优惠券代码框来减少对优惠券的依赖。还有一个建议是，为转换后的客户粘性制定策略，并专注于VIP客户，其中排名前10%的客户能产生最高的收入。通过谷歌对该商店的分析数据，我们可以看到倡导者创造了商店的x因素和病毒式传播。它们是最低的CPA渠道(36美元)，拥有最高的LTV生成器。因此，另一个建议是，从推荐渠道中找出最重要的倡导者，并通过与他们建立直接关系并提供推荐计划来获得最大的优势。由于推荐的客户主要来自谷歌Plex，他们更忠诚，如果商店的目标是提供直接的激励给他们，50%的人更活跃地给予推荐，人们有4倍以上的可能性购买时，由朋友介绍。
The above custom report as per Fig. 11 suits to the Google Merchandise Store’s business goals of measuring the e-commerce success by measuring the key metrics “Average Order Value” and “Per Session Value” next to the revenue generated and tracks how buying behaviour attributes change based on the various traffic source. When analysing into depth about Google Merchandise store, one can see that the traffic coming from organic has the highest session, but with a relatively low per session value at one dollar.
In this concluding chapter, the researcher highlights some useful, in-depth reports for understanding the Google Merchandising Store’s customers and analysing their online shopping behaviour. The principles behind segmentation within the reports and its usefulness in comparing different groups of data show how majority of the traffic that visit organically do not convert and the direct traffic visiting from Google Plex have the highest conversion rate. When performing in-depth analysis, some recommendations are, to not restrict to the metrics defined in the plan, instead look at the user behaviour of how most of the returning visitors come back with the decision to purchase which should be taken on the KPI. Thinking about this data in context, it is necessary to provide sufficient incentives for most of the visitors to become returning visitors and convert. In an effort to improve the shopping experience for visitors, the recommendation is to only push targeted users through the funnel along with billing information and shipping disclaimers disclosed at the top of the funnel. Making sign in options in the Google account easier or taking that function away could help in not losing buyers with high intent on purchase. Lastly, to convert more visitors and have an increase in transactions and revenue, it would be fundamental to optimize all the channel campaigns design, specially copy, call to actions, and also directing the traffic to other channels for better first and last interaction. This can be better managed by creating micro segments of users that bounced at different stages in the funnel and then remarketing them with customized campaigns.
在这一结论章，研究人员突出了一些有用的，深入的报告，以了解谷歌商品商店的顾客和分析他们的网上购物行为。在报告中分割背后的原则和它在比较不同组数据的有用性显示了访问的大多数流量有机地不转换和从谷歌Plex访问的直接流量有最高的转化率。在进行深入分析时，一些建议是，不局限于计划中定义的指标，而是观察用户行为，即大多数回访用户是如何回到KPI中做出购买决定的。考虑到这些数据的背景，有必要为大多数访问者提供足够的激励，让他们成为回访者并转变。为了改善访问者的购物体验，建议只通过漏斗向目标用户推送账单信息和在漏斗顶部披露的发货免责声明。在谷歌账户中更容易地签入选项或取消这个功能可以帮助不失去购买意愿高的买家。最后，为了转化更多的访问者，增加交易和收入，优化所有的渠道活动设计，特别是复制，呼吁行动，并引导流量到其他渠道，以更好的第一和最后的互动。这可以通过创造在漏斗中不同阶段出现的用户细分，然后通过定制活动重新营销这些用户而得到更好的管理。
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