工商管理Essay格式：Analysing Online Shopping Behaviour of Google Merchanising Store Customers
本文是工商管理專業(yè)的留學(xué)生Essay范例，題目是“Analysing Online Shopping Behaviour of Google Merchanising Store Customers（分析谷歌商場(chǎng)顧客的網(wǎng)上購(gòu)物行為）”，谷歌Analytics分析分析了谷歌Merchandise Store的主頁(yè)、登錄頁(yè)面、流量來(lái)源和活動(dòng)對(duì)轉(zhuǎn)化率和收益的貢獻(xiàn)。
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個(gè)用戶的一次訪問(wèn)將導(dǎo)致55257個(gè)會(huì)話。在本例中，因?yàn)榭倳?huì)話是70798;平均來(lái)看，至少15541個(gè)用戶有過(guò)多次訪問(wèn)，這意味著每個(gè)用戶有1.28次訪問(wèn)，平均會(huì)話持續(xù)時(shí)間為2:55分鐘。作為一個(gè)假設(shè)的例子，僅僅因?yàn)槠骄鶗?huì)話持續(xù)時(shí)間是2分55秒，這并不意味著大多數(shù)訪問(wèn)者在網(wǎng)站上花了2分55分鐘，它可能意味著一些訪問(wèn)者在網(wǎng)站上花了30分鐘，一些人花了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.
新的和回訪游客的谷歌商品商店應(yīng)該是重要的，因?yàn)樗潜匾模员Ａ衄F(xiàn)有的客戶，也找到一些新的。觀察行為數(shù)據(jù)時(shí)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。這可以表明兩件事，隨著新訪問(wèn)者看到如此強(qiáng)大的數(shù)字，可以看出品牌意識(shí)背后的營(yíng)銷(xiāo)努力是正確的，人們實(shí)際上是第一次訪問(wèn)網(wǎng)站。然而，回訪人數(shù)相對(duì)較少可能意味著沒(méi)有足夠的激勵(lì)或價(jià)值的人返回網(wǎng)站。由于該網(wǎng)站不是一個(gè)領(lǐng)先的網(wǎng)站，而是一個(gè)電子商務(wù)網(wǎng)站，有必要分析訪問(wèn)者是否通過(guò)購(gòu)買(mǎi)和營(yíng)銷(xiāo)努力的貨幣化轉(zhuǎn)變。
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)美國(guó)用戶是年齡在25-34歲之間的男性，會(huì)說(shuō)/讀英語(yǔ)，因此有必要進(jìn)一步縮小活動(dòng)范圍，以便當(dāng)新訪客進(jìn)入時(shí)，有機(jī)流量更有針對(duì)性，反彈率更低。持續(xù)保持相關(guān)性且反彈率低于30%的廣告活動(dòng)將帶來(lái)更高的收益，這可以通過(guò)為每個(gè)目標(biāo)受眾創(chuàng)建高度相關(guān)和特定的廣告，從而實(shí)現(xiàn)推薦流量的多樣化。根據(jù)以上解釋，分析表明似乎有大量的推薦流量，特別是通過(guò)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.
其中一個(gè)最吸引人的部分考慮到谷歌商品商店的游客是來(lái)自哪個(gè)國(guó)家，地區(qū)，大陸或次大陸，他們來(lái)自。在本報(bào)告中，如圖4所示，研究者從觀眾類(lèi)別的地理報(bào)告中選取位置報(bào)告進(jìn)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.
行為概覽報(bào)告解釋在這一研究提供了一個(gè)全面的頁(yè)面內(nèi)容概覽，以分析網(wǎng)站的頂部和底部執(zhí)行的頁(yè)面，并了解每一個(gè)其他頁(yè)面的性能。它揭示了用戶行為和他們?cè)诠雀枭唐肪W(wǎng)站上的參與，分析了他們?cè)诰W(wǎng)站上花了多長(zhǎng)時(shí)間，以及具體的一天和時(shí)間。這份報(bào)告的細(xì)分是為了最終判斷他們是否通過(guò)了市場(chǎng)營(yíng)銷(xiāo)渠道，也為了了解反彈率，反彈和退出發(fā)生在哪里，并最終了解產(chǎn)品的轉(zhuǎn)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.
圖8所示的轉(zhuǎn)換報(bào)告和圖9所示的電子商務(wù)產(chǎn)品性能報(bào)告，提供了對(duì)暢銷(xiāo)產(chǎn)品的見(jiàn)解，是非常有用和無(wú)價(jià)的報(bào)告。與其他報(bào)告一樣，這些報(bào)告稱2017年5月的總收入為348331.83美元。排名前三的產(chǎn)品頁(yè)面分別是Nest Cam戶外安防攝像頭、Nest Cam室內(nèi)安防攝像頭和Nest學(xué)習(xí)恒溫器第三代。Nest Cam戶外安全攝像頭貢獻(xiàn)了14%的產(chǎn)品收入。Nest的出現(xiàn)在網(wǎng)站的主菜單選項(xiàng)卡可能是其高性能和高需求的原因。在這些頂部產(chǎn)品頁(yè)面上的獨(dú)特購(gòu)買(mǎi)分別有一個(gè)接近的數(shù)字283,285和234。無(wú)產(chǎn)品退款金額，平均數(shù)量為1.50。看另一個(gè)重要因素是basket-to-detail-rate比例是41.16%的人將產(chǎn)品添加到購(gòu)物籃從產(chǎn)品頁(yè)面,第二個(gè)是buy-to-detail率是14.69%的比例的人其實(shí)從進(jìn)入產(chǎn)品頁(yè)面購(gòu)買(mǎi)一個(gè)產(chǎn)品。在這種情況下，公司需要通過(guò)讓訪問(wèn)者在將產(chǎn)品放入購(gòu)物籃后進(jìn)行足夠的購(gòu)買(mǎi)來(lái)提高購(gòu)買(mǎi)到詳細(xì)信息的比率。在選擇次要數(shù)據(jù)和根據(jù)區(qū)域進(jìn)一步深入數(shù)據(jù)方面，該電子商務(wù)商店以美國(guó)為重點(diǎn)，其從購(gòu)物籃到詳細(xì)信息的比率最高的是加利福尼亞州，而從購(gòu)買(mǎi)到詳細(xì)信息的比率最高的是亞利桑那州。所以很明顯，不同地區(qū)的付費(fèi)活動(dòng)的數(shù)量是可變的，每個(gè)地區(qū)的付費(fèi)活動(dòng)都應(yīng)該相應(yīng)地調(diào)整。或者，當(dāng)查看時(shí)間，特別是一周中的一天，它聲明大多數(shù)銷(xiāo)售發(fā)生在工作日，因此建議將前負(fù)荷的活動(dòng)預(yù)算到一周的開(kāi)始，并逐漸減少到周末。周三得到了最高的籃子到細(xì)節(jié)率57%的頂級(jí)產(chǎn)品，因此活動(dòng)可以集中在籃子到細(xì)節(jié)率而不是收入。我們發(fā)現(xiàn)，41%的用戶訪問(wèn)了購(gòu)物車(chē)頁(yè)面，只有14%的用戶轉(zhuǎn)用了。其余的退出漏斗從sign in頁(yè)面誰(shuí)是訪客誰(shuí)沒(méi)有一個(gè)谷歌帳戶設(shè)置，也誰(shuí)沒(méi)有時(shí)間或希望建立一個(gè)。
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.
在本節(jié)中，研究人員的目標(biāo)是講述一個(gè)谷歌商品商店的故事，如圖10所示，其中投資回報(bào)與每次點(diǎn)擊成本、每次獲取成本和印象形式的參與度水平一起計(jì)算。重點(diǎn)是為優(yōu)化與電子商務(wù)商店的目標(biāo)和目標(biāo)相關(guān)的轉(zhuǎn)換提供建議。消費(fèi)者在商店的旅程從意識(shí)開(kāi)始，經(jīng)過(guò)興趣，考慮，購(gòu)買(mǎi)，保留，最后到宣傳階段。上圖中的各種指標(biāo)有效地描繪出了購(gòu)物者旅程的各個(gè)階段，并給出了當(dāng)月所有差距和成功的整體視圖。在通過(guò)印象指標(biāo)衡量商店的認(rèn)知度和參與度KPI時(shí)，為了優(yōu)化所有渠道的點(diǎn)擊率，考慮搜索印象、重新定位和媒體印象、電子郵件開(kāi)放率、網(wǎng)站訪問(wèn)量、公關(guān)印象和社交印象的數(shù)量是很重要的。增加訪問(wèn)者的創(chuàng)造性方法是優(yōu)化標(biāo)題標(biāo)簽的SEO，復(fù)制和圖像的社會(huì)，付費(fèi)和顯示來(lái)源和主題欄的電子郵件，以增加點(diǎn)擊通過(guò)和最終從這些媒體的轉(zhuǎn)化率。可以通過(guò)各種社交競(jìng)賽來(lái)增加訪客和轉(zhuǎn)化率，并產(chǎn)生病毒式傳播。
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(jià)值158.69美元，建議根據(jù)客戶添加到購(gòu)物車(chē)中的內(nèi)容進(jìn)行追加銷(xiāo)售或交叉銷(xiāo)售。同樣，也可以通過(guò)隱藏特定流量源的優(yōu)惠券代碼框來(lái)減少對(duì)優(yōu)惠券的依賴。還有一個(gè)建議是，為轉(zhuǎn)換后的客戶粘性制定策略，并專注于VIP客戶，其中排名前10%的客戶能產(chǎn)生最高的收入。通過(guò)谷歌對(duì)該商店的分析數(shù)據(jù)，我們可以看到倡導(dǎo)者創(chuàng)造了商店的x因素和病毒式傳播。它們是最低的CPA渠道(36美元)，擁有最高的LTV生成器。因此，另一個(gè)建議是，從推薦渠道中找出最重要的倡導(dǎo)者，并通過(guò)與他們建立直接關(guān)系并提供推薦計(jì)劃來(lái)獲得最大的優(yōu)勢(shì)。由于推薦的客戶主要來(lái)自谷歌Plex，他們更忠誠(chéng)，如果商店的目標(biāo)是提供直接的激勵(lì)給他們，50%的人更活躍地給予推薦，人們有4倍以上的可能性購(gòu)買(mǎi)時(shí)，由朋友介紹。
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，研究人員突出了一些有用的，深入的報(bào)告，以了解谷歌商品商店的顧客和分析他們的網(wǎng)上購(gòu)物行為。在報(bào)告中分割背后的原則和它在比較不同組數(shù)據(jù)的有用性顯示了訪問(wèn)的大多數(shù)流量有機(jī)地不轉(zhuǎn)換和從谷歌Plex訪問(wèn)的直接流量有最高的轉(zhuǎn)化率。在進(jìn)行深入分析時(shí)，一些建議是，不局限于計(jì)劃中定義的指標(biāo)，而是觀察用戶行為，即大多數(shù)回訪用戶是如何回到KPI中做出購(gòu)買(mǎi)決定的。考慮到這些數(shù)據(jù)的背景，有必要為大多數(shù)訪問(wèn)者提供足夠的激勵(lì)，讓他們成為回訪者并轉(zhuǎn)變。為了改善訪問(wèn)者的購(gòu)物體驗(yàn)，建議只通過(guò)漏斗向目標(biāo)用戶推送賬單信息和在漏斗頂部披露的發(fā)貨免責(zé)聲明。在谷歌賬戶中更容易地簽入選項(xiàng)或取消這個(gè)功能可以幫助不失去購(gòu)買(mǎi)意愿高的買(mǎi)家。最后，為了轉(zhuǎn)化更多的訪問(wèn)者，增加交易和收入，優(yōu)化所有的渠道活動(dòng)設(shè)計(jì)，特別是復(fù)制，呼吁行動(dòng)，并引導(dǎo)流量到其他渠道，以更好的第一和最后的互動(dòng)。這可以通過(guò)創(chuàng)造在漏斗中不同階段出現(xiàn)的用戶細(xì)分，然后通過(guò)定制活動(dòng)重新?tīng)I(yíng)銷(xiāo)這些用戶而得到更好的管理。
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