用戶參與度與活躍度的區(qū)別

disclaimer: I don’t work for Yammer, this is a public data case study, I’ve written it in a narrative format to make this case study more engaging to read.

免責(zé)聲明：我不為Yammer工作，這是一個公共數(shù)據(jù)案例研究，我以敘述性格式編寫了該案例研究，以使該案例研究更具閱讀吸引力。

It’s Monday morning…you sit down at your desk with a cup of coffee — your eyes barely open. Suddenly the head of product taps you on your shoulder and slams his laptop down on your desk.

今天是星期一早上……您坐在辦公桌旁喝咖啡，幾乎睜不開眼睛。 突然產(chǎn)品的頭部輕拍您的肩膀，將筆記本電腦砸向您的書桌。

“How did our weekly engagement drop 21% in a month?!”

“我們的每周參與度如何在一個月內(nèi)下降21％？！”

*Sighhhhhhh*

* Sighhhhhhh *

You crack your knuckles, and put down your coffee — “l(fā)et’s take a look”.

您打開指關(guān)節(jié)，放下咖啡-“讓我們看一下”。

Hmmm…at first glance it looks our weekly engagement levels have been steadily declining since July 28. At Yammer, we define ‘engagement’ as any interaction with the product that makes a “server call” (aka if the user clicked on something).

嗯……乍看之下，自7月28日以來，我們的每周參與度一直在穩(wěn)步下降。在Yammer，我們將“參與度”定義為與產(chǎn)品進行“服務(wù)器呼叫”的任何互動(也就是用戶點擊了某項內(nèi)容)。

In general, most companies look at user engagement as one of their main KPIs (key performance indicators) for overall growth & health of their product.

通常，大多數(shù)公司將用戶參與度視為產(chǎn)品整體增長和健康狀況的主要KPI(關(guān)鍵績效指標(biāo))之一。

To those who haven’t heard of us, Yammer is a social network for enterprises (similar to Workplace by Facebook). Employees can send messages, search the intranet, share documents, and post updates.

對于從未聽說過我們的人來說， Yammer是企業(yè)的社交網(wǎng)絡(luò)(類似于Facebook的Workplace)。 員工可以發(fā)送消息，搜索Intranet，共享文檔以及發(fā)布更新。

診斷問題 (Diagnosing the Problem)

Before spending too much time diagnosing the root cause for a KPI shift, I first go through this mental checklist to ensure the root cause wasn’t caused by non-product factors such as advertising, bugs, errors, etc.

在花太多時間診斷KPI變動的根本原因之前，我首先查看此心理檢查清單，以確保根本原因不是由非產(chǎn)品因素引起的，例如廣告，錯誤，錯誤等。

Diagram Illustrated by Richard Yang理查德·楊(Richard Yang)圖解

Time: Time is a good factor to consider since irregular trends are easier to spot when graphed. Here are the most common time-related data questions to ask yourself:

時間 ：時間是一個值得考慮的好因素，因為在繪制圖形時不規(guī)則趨勢更容易發(fā)現(xiàn)。 以下是最常見的與時間相關(guān)的數(shù)據(jù)問題：

• Is this drop in engagement sudden or gradual? ? If it’s sudden then check with the engineering team if any deployments were made, if its gradual then it may be related to a change in user behaviour.
  參與度下降是突然的還是逐漸的？ ?如果突然，請咨詢工程團隊是否進行了任何部署，如果部署是逐步的，則可能與用戶行為的更改有關(guān)。
• Is it a one time occurrence or a recurring issue? ? a one time occurrence is likely due to a technical issue or marketing campaign while recurring issues could be due to behavioural changes.
  是一次性發(fā)生還是反復(fù)出現(xiàn)的問題？ one一次發(fā)生可能是由于技術(shù)問題或市場營銷活動，而經(jīng)常發(fā)生的問題可能是由于行為更改。
• Do we see specific times during the day or specific days of the week where the drop is more pronounced? ? Drops localized to a specific time or day would likely indicate a technical issue — such as failed data refreshes that generally occur over the weekend.
  在一天中或一周中的特定幾天中，我們看到特定的時間了嗎？ local本地化到特定時間或日期的丟棄可能表明存在技術(shù)問題，例如通常在周末發(fā)生的數(shù)據(jù)刷新失敗。

Region: Regional engagement levels are important to consider since companies may roll out features with specific regions when testing new products or regional internet censorship/ laws that can limit usage. Some questions to ask are:

地區(qū) ：考慮區(qū)域參與度很重要，因為公司在測試新產(chǎn)品或可能限制使用的區(qū)域互聯(lián)網(wǎng)審查/法律時可能會在特定區(qū)域推出功能。 要問的一些問題是：

• Do we see the drop in engagement tied to a particular region or is it globally distributed? Certain features might perform better or worse in specific locales due to cultural differences in consumer behaviour.
  我們是否看到與特定區(qū)域相關(guān)的參與度下降？還是全球分布？ 由于消費者行為的文化差異，某些功能在特定區(qū)域可能會表現(xiàn)得更好或更差。
• Have there been any recent changes in internet censorship laws related to the region with drops in engagement?
  由于參與度下降，與該地區(qū)相關(guān)的互聯(lián)網(wǎng)審查法律最近是否發(fā)生了變化？

Other Features / Products: keeping general tabs on other products/features within the same space is useful for identifying potential changes at a company level. Some questions to ask are:

其他功能/產(chǎn)品 ：將其他產(chǎn)品/功能的常規(guī)選項卡放在同一空間內(nèi)對于識別公司級別的潛在更改很有用。 要問的一些問題是：

• Are other features or products also experiencing similar drops in engagement? ? if the drop is prevalent across other products and features, then the issue would be a larger problem that requires involvement from multiple teams to investigate.
  其他功能或產(chǎn)品的參與度是否也在下降？ ?如果下降趨勢在其他產(chǎn)品和功能中普遍存在，那么問題將是一個更大的問題，需要多個團隊的參與才能進行調(diào)查。
• Are other similar features or products within the ecosystem experiencing a proportional increase in engagement? ? users could switch to using a different product or feature to fulfil their use-cases leading to cannibalization of Yammer.
  生態(tài)系統(tǒng)中的其他類似功能或產(chǎn)品是否正在按比例增加參與度？ ?用戶可以切換到使用其他產(chǎn)品或功能來滿足他們的用例，從而使Yammer蠶食。

Platform: depending on the platform users engage with Yammer, there are differences in UX display and ENG release processes, you want to narrow down if the issue is technical and isolated to a particular platform. Some common questions to ask are:

平臺 ：根據(jù)使用Yammer的平臺用戶的不同，UX顯示和ENG發(fā)布過程有所不同，如果問題是技術(shù)性的且僅針對特定平臺，則希望縮小范圍。 一些常見的問題是：

• Do we see the decline in occurring across specific platforms (ie. mobile, desktop, tablet) or across all platforms?
  我們是否看到在特定平臺(即移動，臺式機，平板電腦)或所有平臺上出現(xiàn)的下降？
• Is the decline android or iOS or other operating system specific?
  衰退的android或iOS或其他操作系統(tǒng)是否特定？

Industry/ Competitors: keeping track of how the Yammer is performing in the industry and having knowledge of general trends is useful for market space awareness. Competitor information may be difficult to acquire, but we can still leverage news articles, google trend searches and third party data to derive high-level insights.

行業(yè)/競爭對手 ：跟蹤Yammer在行業(yè)中的表現(xiàn)并了解總體趨勢有助于提高市場空間意識。 競爭對手的信息可能很難獲取，但是我們?nèi)匀豢梢岳眯侣剤蟮?#xff0c;谷歌趨勢搜索和第三方數(shù)據(jù)來獲得高級見解。

Google Trend Search — Comparing Yammer with top competitorsGoogle趨勢搜索-將Yammer與頂級競爭對手進行比較

Broken Feature / Tracking Code: It is also possible that certain features or the tracking code is broken which affects how events are logged. The mapping hierarchy of events that a user triggers when they interact with the application is important to determine where a user is dropping off.

功能/跟蹤代碼損壞 ：某些功能或跟蹤代碼也可能損壞，這會影響事件的記錄方式。 用戶在與應(yīng)用程序進行交互時觸發(fā)的事件的映射層次結(jié)構(gòu)對于確定用戶在何處下車很重要。

Bots and Search Crawler Engines: most major websites have high activity from bots but it is difficult to determine the events triggered by bots. High traffic websites are also guided by SEO and changes to search engine indexes can cause significant changes in traffic.

機器人程序和搜索爬蟲引擎 ：大多數(shù)主要網(wǎng)站的機器人活動都很活躍，但很難確定由機器人程序觸發(fā)的事件。 高流量的網(wǎng)站也受到SEO的引導(dǎo)，搜索引擎索引的更改可能會導(dǎo)致流量的重大變化。

Major Events: it is also possible for one-off events to cause influxes or major decreases in engagement such as holidays, successful campaigns and negative press.

重大事件 ：一次性事件也可能導(dǎo)致大量涌入或參與度大幅下降，例如假期，成功的競選活動和負面新聞。

數(shù)據(jù)約束 (Constraints of the Data)

In this case study, the only datasets we have available to manipulate are: time, region, platforms and event tracking. If you’d like to see the source data and code for analysis please see the embedded notebook link below:

在此案例研究中，我們唯一可操作的數(shù)據(jù)集是：時間，區(qū)域，平臺和事件跟蹤。 如果您想查看源數(shù)據(jù)和代碼以進行分析，請參見下面的嵌入式筆記本鏈接：

*Note that all Plotly charts are interactive, you can click and drag to zoom in the chart or click categories in the legend to filter.

*請注意，所有Plotly圖表都是交互式的，您可以單擊并拖動以放大圖表，也可以單擊圖例中的類別進行過濾。

假設(shè) (Hypotheses)

Potential Cause 1: engagement dropped due to failed data refreshes or technical outages on a specific week day or time of day

可能的原因1 ：由于在特定的工作日或一天中的某天數(shù)據(jù)刷新失敗或技術(shù)中斷而 導(dǎo)致 參與度下降

• As per the graphs above, Saturdays and Sundays have the lowest engagement rates which makes sense since Yammer is used mostly for work.

  根據(jù)上面的圖表，周六和周日的參與率最低，這是有道理的，因為Yammer主要用于工作。

• When checking engagement by Time of Day (based on the 24-hour clock) it looks like 9 am is when users interact with Yammer the most frequently but no other time of day stands out.
  當(dāng)按一天中的時間(基于24小時制)檢查參與度時，用戶與Yammer進行互動的頻率最高，但一天中其他時間都沒有，這似乎是上午9點。
• Since the drop in engagement was gradual over the span of a few months and not localized to a particular day or time we can assume that the cause is not likely due to a one time server / technical outage or data refresh failure.
  由于參與度的下降是在幾個月的時間內(nèi)逐漸發(fā)生的，并且沒有局限于特定的日期或時間，因此我們可以假定原因可能不是服務(wù)器/技術(shù)故障或數(shù)據(jù)刷新失敗一次。

Potential Cause 2: decrease in engagement due to regional outages or locale based feature testing

潛在原因2 ：由于區(qū)域中斷或基于區(qū)域設(shè)置的功能測試而導(dǎo)致參與度降低

• Since Yammer is used by multiple regions, it would be best to analyze the top 5 regions based on number of users.
  由于Yammer被多個區(qū)域使用，因此最好根據(jù)用戶數(shù)量來分析前5個區(qū)域。
• From the graph above, the United States had the most significant drop in active users while other regions have experienced smaller decreases.
  從上圖可以看出，美國的活躍用戶下降幅度最大，而其他地區(qū)的下降幅度較小。
• It would be useful to quickly check with other teams if there were any new releases or product upgrades localized to only the States.
  快速與其他團隊核對是否有僅本地化的新版本或產(chǎn)品升級會很有用。

Potential Cause 3: decrease in engagement is because of failing to acquire and grow users over time — potentially due to tracking errors or bugs interrupting the sign-up flow or the product not providing the right value to users

潛在原因3 ：參與度下降是由于無法隨著時間的推移獲取和增長用戶而引起的-可能是由于跟蹤錯誤或錯誤干擾了注冊流程，或者產(chǎn)品未能為用戶提供正確的價值

• Events (actions) in Yammer are put into the following categories: login events, messaging events, search events and sign up funnel events.
  Yammer中的事件(操作)分為以下幾類：登錄事件，消息事件，搜索事件和注冊漏斗事件。
• From the graph above, it is clear that users are able to navigate through the signup funnel, but other events are seeing a decrease in engagement levels.
  從上圖可以明顯看出，用戶可以瀏覽注冊渠道，但其他事件的參與度卻有所下降。

• Growth or activation rate is a closely tracked metric for all companies — since it means Yammer is providing the intended value for new customers.
  增長率或激活率是所有公司密切跟蹤的指標(biāo)-因為這意味著Yammer為新客戶提供了預(yù)期的價值。
• Based on the chart above, the growth rate remains normal as it continues to be high during the weekday and low on weekends.
  根據(jù)上面的圖表，增長率保持正常，因為它在工作日中繼續(xù)保持較高水平，而周末則保持較低水平。
• With no sign up (growth) issues, the drop in engagement can be assumed to be from older / existing users.
  在沒有注冊(增長)問題的情況下，參與度下降的原因可以認為是老用戶/現(xiàn)有用戶。

Potential Cause 4: decrease in engagement due to lack of retention from older users

潛在原因4 ：參與度下降是由于缺少老年用戶的保留

• Doing a cohort analysis is one of the most common ways to track retention for any group of users. It helps to paint a better picture about Yammer’s product effectiveness for users in the long run.

  進行同類群組分析是跟蹤任何一組用戶的保留率的最常見方法之一。 從長遠來看，它有助于為用戶更好地描繪Yammer產(chǎn)品的有效性。

• After segmenting the users by age, it’s quite evident that those who signed up more than 10 weeks prior to May 1 have decreasing levels of engagement over time. Looking at each cohort individually, as the weeks progress, their engagement levels also decrease.
  在按年齡對用戶進行細分之后，很明顯，那些在5月1日之前簽約超過10周的用戶的參與度會隨著時間的推移而下降。 逐個查看每個群組，隨著星期的進展，他們的參與度也會降低。
• As the problem is related to mature users, the issue is unlikely to be related to a one-time spike from marketing traffic or bots and search crawlers — which can cause unsustainable engagement blitzs.
  由于該問題與成熟用戶有關(guān)，因此該問題不太可能與來自營銷流量或漫游器和搜索爬蟲的一次性高峰有關(guān)，這可能會導(dǎo)致不可持續(xù)的用戶參與度。

• The product has a problem with “stickiness”, since it seems like our users become less engaged with our product over time.

  該產(chǎn)品存在“ 粘性 ”問題，因為隨著時間的流逝，我們的用戶似乎越來越不喜歡我們的產(chǎn)品。

Potential Cause 5: decrease in engagement due to our weekly digest emails failing to reach users for its intended purpose.

潛在原因5 ：由于我們的每周摘要電子郵件未能達到其預(yù)期目的而無法吸引用戶，因此參與度下降。

• To engage and re-engage users, Yammer sends weekly digest emails and weekly re-engagement emails.
  為了吸引和重新吸引用戶，Yammer每周發(fā)送摘要電子郵件和每周重新參與電子郵件。
• Based on the charts above, the number of weekly emails sent and weekly emails opened have been increasing but there was a significant drop in the number of weekly click throughs while the weekly re-engagement metrics were normal.
  根據(jù)上面的圖表，每周發(fā)送的電子郵件和打開的每周電子郵件的數(shù)量一直在增加，但是每周的點擊率卻有明顯下降，而每周的重新參與度指標(biāo)是正常的。

• A decrease in click through rates even though open rates are increasing could mean that the weekly digest email content is not relevant enough to users or the intended user action is not explicit enough. IF CTR was 0, that would have indicated a broken button or feature.
  即使打開率增加，點擊率也會降低，這可能意味著每周摘要電子郵件內(nèi)容與用戶的相關(guān)性不夠，或者預(yù)期的用戶操作不夠明確。 如果CTR為0，則表明按鈕或功能已損壞。

Potential Cause 6: decrease in engagement due to the type of device users use to navigate Yammer or the operating system.

潛在原因6 ：由于用戶使用導(dǎo)航Y(jié)ammer或操作系統(tǒng)的設(shè)備類型減少了參與度。

• As seen in the graphs above, users who use phones are seeing a much more pronounced drop in engagement
  如上圖所示，使用電話的用戶的參與度下降更為明顯
• This could be due to the recent changes or releases for Yammer’s mobile app
  這可能是由于Yammer的移動應(yīng)用程序最近發(fā)生了更改或發(fā)布

• Digging further for sanity, it is useful to compare the same metric between different operating systems since there are different code versions for the app per OS
  進一步挖掘合理性，比較不同操作系統(tǒng)之間的同一指標(biāo)非常有用，因為每個操作系統(tǒng)的應(yīng)用程序代碼版本不同
• It looks like regardless of the operating system, users who access Yammer via a phone have been less engaged over time
  不管使用什么操作系統(tǒng)，通過電話訪問Yammer的用戶隨著時間的推移似乎很少參與
• This scenario leads me to assume that there is an issue with retaining Yammer phone users over time, potentially due to UX design problems or dysfunctional features leading to discontent.
  這種情況使我假設(shè)，隨著時間的流逝，保留Yammer電話用戶存在問題，這可能是由于UX設(shè)計問題或功能失調(diào)導(dǎo)致不滿。

結(jié)論 (Conclusion)

From our analysis, we can infer that long term mobile users who access Yammer become disengaged over time due to usability issues or irrelevant content. Despite relatively high email open rates, we have observed much lower click through rates from the weekly digests. This likely means there’s an issue with our email content e.g. relevance or quality.

根據(jù)我們的分析，我們可以推斷出，由于可用性問題或不相關(guān)的內(nèi)容，訪問Yammer的長期移動用戶會隨著時間的流逝而失去聯(lián)系。 盡管電子郵件的打開率相對較高，但我們發(fā)現(xiàn)每周摘要的點擊率要低得多。 這可能意味著我們的電子郵件內(nèi)容存在問題，例如相關(guān)性或質(zhì)量。

Given the multiple different hypotheses for user engagement drop-off, I would discuss validation strategies with other disciplines such as engineering, design, marketing, and product. Validation strategies include, AB testing, feature roll-backs, and locale segmentation.

考慮到用戶參與度下降的多種不同假設(shè)，我將與工程，設(shè)計，營銷和產(chǎn)品等其他學(xué)科討論驗證策略。 驗證策略包括AB測試，功能回滾和區(qū)域劃分。

使用過的技術(shù)的學(xué)習(xí) (Learnings with Technologies Used)

• Since I began my data journey with SQL, I’m much more comfortable doing joins and aggregations with SQL than Pandas. I decided to use SQLite for all of the more complicated data manipulations and found that most of the functions and syntax is the same as MySQL. The major differences in my use case is with the date and time functions. I would recommend for individuals to use SQLite to query data frames with SQL syntax if that is your preferred language of choice.

  自從我開始使用SQL進行數(shù)據(jù)之旅以來，與Pandas相比，使用SQL進行連接和聚合要舒適得多。 我決定將SQLite用于所有更復(fù)雜的數(shù)據(jù)操作，并發(fā)現(xiàn)大多數(shù)功能和語法與MySQL相同。 我的用例的主要區(qū)別在于日期和時間函數(shù) 。 我建議個人使用SQLite來查詢SQL語法的數(shù)據(jù)幀(如果這是您的首選語言)。

• I used Plotly’s Python graphing library for all of the visualizations and interactive charts/ graphs. I like the interactive components: hover, click and drag to zoom into elements and the simple layout of Plotly charts. It is my preferred visualization library it as opposed to Matplotlib, Seaborn and Bokeh. I find it a little difficult to find case specific examples online for how to plot certain graphs so I am uncertain about whether I’ve plotted the line and bar charts in the most efficient manner — love to hear some feedback on the code written.
  我將Plotly的Python圖形庫用于所有可視化效果和交互式圖表/圖形。 我喜歡交互式組件：懸停，單擊并拖動以放大元素和Plotly圖表的簡單布局。 與Matplotlib，Seaborn和Bokeh相比，它是我首選的可視化庫。 我發(fā)現(xiàn)很難在網(wǎng)上找到特定案例的示例來繪制某些圖形，因此我不確定我是否以最有效的方式繪制了折線圖和條形圖-喜歡聽到對編寫代碼的一些反饋。

• Plotly’s interactive charts also don’t render in GitHub. If you want to retain your charts to share the analysis in your Jupyter Notebook / Labs, i’d recommend using nbviewer to render your published notebook.

  Plotly的交互式圖表也不在GitHub中呈現(xiàn)。 如果要保留圖表以在Jupyter筆記本/實驗室中共享分析，我建議使用nbviewer渲染已發(fā)布的筆記本。

You can find the original case here: https://mode.com/sql-tutorial/a-drop-in-user-engagement/

您可以在這里找到原始案例： https : //mode.com/sql-tutorial/a-drop-in-user-engagement/

翻譯自: https://towardsdatascience.com/yammer-investigating-a-sudden-drop-in-user-engagement-7c9c4093c038

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