工商管理Essay代寫格式范文：Analysing Online Shopping Behaviour of Google Merchanising Store Customers
本文是工商管理專業(yè)的Essay代寫范例，題目是“Analysing Online Shopping Behaviour of Google Merchanising Store Customers（谷歌連鎖商店顧客在線購物行為分析）”，谷歌Analytics分析了谷歌Merchandise Store的主頁、登陸頁面、流量來源和活動對轉(zhuǎn)化率和營收的貢獻。
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，55257個用戶的至少一次訪問將導(dǎo)致55257個會話。在本例中，因為總會話是70,798;平均來看，至少有15541名用戶擁有多次會話，這意味著該網(wǎng)站具有較高的忠誠度，每個用戶擁有1.28次會話，平均會話持續(xù)時間為2:55分鐘。舉個假設(shè)的例子，僅僅因為平均會話持續(xù)時間是2分55秒，并不意味著大多數(shù)訪問者在網(wǎng)站上花費了2分55分鐘，它可能意味著一些訪問者在網(wǎng)站上花費了30分鐘，一些人花費了0分鐘。
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.
谷歌商品商店的新顧客和回頭客應(yīng)該是重要的，因為留住現(xiàn)有顧客和找到一些新顧客是必要的。觀察行為數(shù)據(jù)時根據(jù)觀眾的部分新vs返回游客在谷歌分析按圖3中,可以看到,50971年的新訪客賬戶顯著與總數(shù)的81.39%用戶并返回游客比較低在11652年只有18.61%的總用戶。這可以表明一些事情，隨著新訪問者的數(shù)量如此之多，可以看出品牌意識背后的營銷努力是正確的，人們實際上是第一次訪問網(wǎng)站。然而，回訪人數(shù)相對較少可能意味著沒有足夠的激勵或價值讓人們再次回到該站點。由于該網(wǎng)站不是一個領(lǐng)先的網(wǎng)站，而是一個電子商務(wù)網(wǎng)站，因此有必要分析訪問者是否通過購買和營銷努力的貨幣化來轉(zhuǎn)化。
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.
大多數(shù)用戶是25-34歲的美國男性，會說/讀英語，因此有必要進一步縮小活動范圍，以便當新訪客進入時，有機流量更有針對性，并有更低的跳出率。持續(xù)的廣告活動，在跳出率小于30%的情況下，將帶來更高的收入，這可以通過為每個目標受眾創(chuàng)建高度相關(guān)和特定的廣告，實現(xiàn)推薦流量的多樣化。根據(jù)以上解釋，分析顯示，似乎有大量的推薦流量進入，特別是通過谷歌Plex獲得最成功的轉(zhuǎn)換率。
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.
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.
有機搜索導(dǎo)致頁面會話的最高數(shù)量和最高數(shù)量的交易完成的用戶，通過谷歌Plex下線來源。直接渠道是第二高的來源，占所有輸入URL或?qū)⒕W(wǎng)站添加書簽的用戶的23.73%。在49,512.34美元的營收中，電子商務(wù)轉(zhuǎn)化率為2.30%。從用戶來看，谷歌員工通過電子郵件溝通的流量推薦來源排名第三，最低的跳出率為23.85%，最高的轉(zhuǎn)換率為10.85%，占253,884.55美元。
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.
在這項研究中解釋的行為概述報告提供了一個全面的頁面內(nèi)容概述，以分析網(wǎng)站上的頂部和底部表現(xiàn)的頁面，也了解每個其他頁面的表現(xiàn)。它揭示了用戶的行為和他們在谷歌商品網(wǎng)站上的參與度，分析了他們在網(wǎng)站上花費的時間和特定的一天和時間。這份報告中的細分市場是為了最終衡量他們是否經(jīng)過了營銷漏斗，同時了解反彈率，反彈和退出發(fā)生在哪里，并最終了解產(chǎn)品的轉(zhuǎn)換。
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.
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.
在本部分中，研究者的目標是講述如圖10所示的谷歌Merchandise Store的故事，其中投資回報與每次點擊成本、每次獲取成本以及印象形式的用戶粘性水平一起計算。重點是為優(yōu)化與電子商務(wù)商店的目標和目標相關(guān)的轉(zhuǎn)換提供建議。消費者在商店的旅程從“意識”開始，然后是“興趣”、“考慮”、“購買”、“留存”，最后是“宣傳”階段。上圖中的各種指標有效地描繪了購物者旅程的各個階段，并給出了該月所有差距和成功的整體視圖。在通過印象指標衡量商店的認知度和用戶粘性KPI時，重要的是要考慮搜索印象、再定位和媒體印象、電子郵件打開率、網(wǎng)站訪問、PR印象和社交印象，以便優(yōu)化所有渠道的點擊率。為每種流量方法增加訪問者的創(chuàng)造性方法是優(yōu)化搜索引擎優(yōu)化標題標簽，為社交、付費和顯示資源復(fù)制和圖像，為電子郵件和主題行增加點擊，并最終從這些媒體轉(zhuǎn)化率。游戲中可以舉辦各種社交比賽，通過社交方式增加用戶數(shù)量和轉(zhuǎn)化率，并引發(fā)病毒式傳播。
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.
為了最大化商店的平均訂單價值158.69美元，建議根據(jù)客戶添加到購物車的內(nèi)容進行追加銷售或交叉銷售。同樣，也可以通過隱藏某些流量源的優(yōu)惠券代碼框來減少對優(yōu)惠券的依賴。還有一項建議是，為轉(zhuǎn)換后的客戶參與創(chuàng)建戰(zhàn)略，并專注于VIP客戶，其中前10%的客戶產(chǎn)生的收入最高。通過谷歌對該商店的分析數(shù)據(jù)，我們可以看到倡導(dǎo)者創(chuàng)造了該商店的x因素和病毒式傳播。他們是CPA最低的36美元渠道，LTV生成器最高。因此，另一個建議是，從推薦渠道中找出最優(yōu)秀的倡導(dǎo)者，并通過與他們建立直接關(guān)系和提供推薦計劃來獲得最大的優(yōu)勢。由于被推薦的客戶主要來自谷歌Plex，他們更忠誠，如果商店旨在為他們提供直接激勵，50%的人會更活躍地進行推薦，人們在朋友推薦后購買的可能性會增加4倍。
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.
在這一結(jié)論章，研究人員強調(diào)一些有用的，深入的報告，以了解谷歌商品商店的客戶和分析他們的網(wǎng)上購物行為。報告中的細分原則及其在比較不同數(shù)據(jù)組時的有用性表明，大多數(shù)有機訪問的流量無法轉(zhuǎn)換，而來自谷歌Plex的直接訪問流量具有最高的轉(zhuǎn)換率。在進行深入分析時，有些建議不局限于計劃中定義的指標，而是著眼于用戶行為，即大多數(shù)回訪用戶是如何返回并決定購買的，這應(yīng)該基于KPI。在此背景下考慮這些數(shù)據(jù)，有必要提供足夠的激勵，讓大多數(shù)訪問者成為回訪者并轉(zhuǎn)化為回訪者。為了改善游客的購物體驗，我們建議只將目標用戶與計費信息和運輸免責聲明一起推送到漏斗頂部。讓谷歌賬戶的登錄選項更容易或取消該功能，可以幫助不失去有強烈購買意向的買家。最后，為了轉(zhuǎn)化更多的訪客，增加交易和收入，優(yōu)化所有渠道活動的設(shè)計，特別是復(fù)制、號召行動，并將流量導(dǎo)向其他渠道，以實現(xiàn)更好的最初和最后的互動，將是最基本的。這可以通過在漏斗的不同階段創(chuàng)建微用戶細分市場，然后通過定制的營銷活動對他們進行重新營銷來更好地管理。
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