facebook 直播

by Ofir Chakon

由Ofir Chakon

什么时候是在Facebook Live上直播的最佳时间？ 我分析了5,000个Facebook帖子以找出答案。 (When is the best time to stream on Facebook Live? I analyzed 5,000 Facebook posts to find out.)

Streaming on Facebook Live can be a powerful marketing strategy for startups and businesses. They can share knowledge, provide value, get exposure and collect high-quality leads.

对于初创企业和企业而言，在Facebook Live上流式传输可能是一种强大的营销策略。 他们可以分享知识，提供价值，获得曝光并收集高质量的线索。

Prepare your Facebook Live session upfront. Researching about your target audience and build a detailed agenda. The session can boost your business dramatically.

预先准备您的Facebook Live会话。 研究您的目标受众并制定详细的议程。 会议可以极大地促进您的业务。

I was Chief of Product and Technology of my previous startup dealing with fraud detection. I decided to try Facebook Live as a new marketing strategy.

我曾在以前的初创公司中负责欺诈检测的产品和技术总监。 我决定尝试将Facebook Live作为一种新的营销策略。

It was still fairly new at the time. Once a Facebook Live session was up, relevant people got Facebook notifications to join the session. This increased the exposure even more.

当时还很新。 Facebook Live会话启动后，相关人员会收到Facebook通知以加入会话。 这进一步增加了曝光。

There are many posts talking about how to better build your Facebook live session. They discuss which topics to cover, building an agenda, camera angles, session duration, and more.

有许多文章讨论如何更好地构建您的Facebook实时会话。 他们讨论了要涵盖的主题，制定议程，摄像机角度，会话持续时间等。

But there is one piece of the puzzle that business owners and marketers often tend to forget or do not pay attention to: When is the best time to stream your Facebook Live session?

但是，企业所有者和市场营销人员往往会忘记或不注意一个难题： 何时才是播放Facebook Live会话的最佳时间？

You can answer this question using an educated guess based on your familiarity with the target audience.

您可以根据对目标受众的熟悉程度，使用有根据的猜测来回答此问题。

For example:

例如：

• Pregnant moms are ready to consume your Live session on Monday afternoon.
  怀孕的妈妈们准备好在周一下午参加您的现场直播。
• Teenagers at the ages of 18–22 are in the right mindset on Saturday morning.
  周六早上，年龄在18-22岁之间的青少年心态正确。

But there is so much data around us that we can use with a few clicks of a button. You actually stay behind if you don’t make a proper usage of some of the data available.

但是我们周围有太多数据，只需单击几下就可以使用。 如果您未正确使用某些可用数据，则实际上您会落后。

Almost every marketing platform or social network opens API services. You, as a technological entrepreneur, can easily consume these. This data can provide valuable conclusions that can drive your business objectives beyond your competitors.

几乎每个营销平台或社交网络都开放API服务。 作为技术企业家，您可以轻松地消费这些。 这些数据可以提供有价值的结论，可以使您的业务目标超越竞争对手。

This approach is often called Data-driven decisions.

这种方法通常称为数据驱动决策 。

Once you start justifying any or at least most of your business decisions using data you own or data you can collect from different resources, you can stop guessing and start making data-driven decisions.

一旦开始使用自己拥有的数据或可以从不同资源收集的数据来证明任何或至少大部分业务决策的合理性，就可以停止猜测并开始制定由数据驱动的决策。

I like to think of data-driven decisions as crowd-sourcing. Lior Zoref was on this TED talk . He invited an ox to the stage and asked the audience to guess its’ weight. If you watched this, you were probably overwhelmed by how accurate the crowd’s average was compared to the real weight of the ox: 1,792 pounds or 1795 pounds!

我喜欢将数据驱动的决策视为众包。 Lior Zoref正在TED演讲中 。 他邀请了一只牛到舞台上，并要求观众猜测其重量。 如果您观看了此内容，那么您可能会不知所措，将人群的平均水平与牛的真实体重进行比较的准确性如何：1,792磅或1795磅！

When you’re making guesses about your business objectives as individuals, you’re not different than any individual sitting in the crowd and trying to evaluate the ox’s weight. You can even be the one who guessed 300 pounds or 8000 pounds, which may cost your business a lot of unnecessary expenses.

当您对个人的业务目标进行猜测时，您与坐在人群中并试图评估公牛体重的个人没有什么不同。 您甚至可以成为猜到300磅或8000磅的人，这可能使您的企业损失很多不必要的费用。

But, if you’re using the wisdom of the crowd to make data-driven decisions, you’ll most likely be ahead of every other individual. In business terms, you’ll be ahead of your competitors.

但是，如果您要利用人群的智慧做出由数据决定的决策，那么您很可能会领先于其他所有人。 从业务角度来讲，您将领先于竞争对手。

I’m not a pure marketer. But with basic data analysis skills I can push my business forward in all aspects, including marketing.

我不是一个纯粹的商人。 但是，凭借基本的数据分析技能，我可以在包括营销在内的各个方面推动业务发展。

I’m going to walk you through a practical step-by-step guide about how to access Facebook data. Then how to analyze it based on our needs about the optimized time to broadcast on Facebook Live.

我将向您介绍有关如何访问Facebook数据的实用分步指南。 然后如何根据我们有关在Facebook Live上播放的最佳时间的需求进行分析。

To follow this guide you need:

要遵循本指南，您需要：

• A Facebook account
  一个Facebook帐户

• A Facebook group you would like to analyze

  您想分析的Facebook群组

  If it’s a private group then you need to be a group member

  如果是私人群组，则您需要成为群组成员

• Python 2.7 installed
  安装了Python 2.7
• Jupyter notebook installed
  安装了Jupyter笔记本

• Facebook graph API Python library installed

  安装了Facebook graph API Python库

A Jupyter notebook is a recommended tool for data analysis in Python. It has a lot of highlights. It enables you to run snippets of code and save the results in memory. So you won’t run all your scripts over and over again every time you implement a minor change. This is crucial when doing data analysis because some tasks can take a lot of execution time.

建议使用Jupyter笔记本进行Python数据分析。 它有很多亮点。 它使您可以运行代码片段并将结果保存在内存中。 因此，您不会在每次实施微小更改时一遍又一遍地运行所有脚本。 这在执行数据分析时至关重要，因为某些任务可能会花费大量执行时间。

Although it’s not essential, I recommend working inside a Python virtual environment. Here is a post I wrote about the advantages of a virtual environment when using Python.

尽管不是必需的，但我建议在Python虚拟环境中工作。 这是我写的关于使用Python时虚拟环境的优势的文章。

I recommend working in an Ubuntu environment when doing data-analysis using Jupyter notebooks.

使用Jupyter笔记本进行数据分析时，建议在Ubuntu环境中工作。

第1步-获取Facebook组ID (Step 1 — Getting the Facebook group ID)

To get data from Facebook API, we need to specify the ID of the entity we want to get data from, in our case, a Facebook group.

要从Facebook API获取数据，我们需要指定要从Facebook组获取数据的实体的ID。

Lookup-id.com is a nice tool you can use to find the ID of a group based on its URL. Copy the URL of your group and paste it in the search bar.

Lookup-id.com是一个不错的工具，可用于根据其URL查找组的ID。 复制您论坛的网址，然后将其粘贴到搜索栏中。

In this article, we will use the group: Web Design and Development.

在本文中，我们将使用以下组： Web设计和开发 。

ID: 319479604815804

ID(标识号)： 319479604815804

第2步-了解Graph API Explorer (Step 2 — Getting to know the Graph API Explorer)

To get the most out of Facebook API besides documentation, Facebook has developed a playground for developers called the Graph API Explorer.

为了最大程度地利用除文档之外的Facebook API，Facebook为开发人员开发了一个称为Graph API Explorer的游乐场。

The Graph API Explorer enables us to get a temporary access token and start examining the capabilities that Facebook API has to offer.

Graph API Explorer使我们能够获取临时访问令牌，并开始检查Facebook API必须提供的功能。

Click on Get Token. Don’t select any permission. Click Get Access Token .

单击获取令牌。 不要选择任何权限。 单击获取访问令牌 。

Facebook API has many endpoints you can use. In this guide, we are going to use two main endpoints:

Facebook API有许多可以使用的终结点。 在本指南中，我们将使用两个主要端点：

• The group endpoint

  组端点

• The reactions endpoint

  React终点

To figure out the structure of the response you’re expecting to get, specify the endpoint URL and click Submit.

要弄清楚您期望得到的响应的结构，请指定端点URL并单击Submit 。

Let’s examine the URL endpoint for grabbing the last posts from the group’s feed. Type this URL in the Graph API Explorer:

让我们检查一下URL终结点，以获取论坛供稿中的最新帖子。 在Graph API Explorer中输入以下URL：

319479604815804/feed

and hit Submit.

然后点击Submit 。

You should now see the last posts from the group’s feed in a JSON structure. It contains the post’s content, its id and the updated time. By clicking on one of the id’s and adding to the end of the URL:

现在，您应该以JSON结构查看论坛供稿中的最新帖子。 它包含帖子的内容，其ID和更新时间。 通过单击其中一个ID并将其添加到URL的末尾：

319479604815804_1468216989942054/reactions?summary=total_count

You should see a list of the reactions for the specific post, and a summary of the total count of reactions.

您应该看到特定帖子的React列表，以及React总数的摘要。

This way you can play around with all the features the Facebook API has to offer.

这样，您就可以使用Facebook API必须提供的所有功能。

Another tool for examining API endpoints of APIs which don’t offer a playground is Postman. You can read more about this tool and essential tools for web developers.

邮递员(Postman)是另一个用于检查不提供游乐场的API的API端点的工具。 您可以阅读有关此工具和面向Web开发人员的基本工具的更多信息。

步骤3 —我们的计划和假设 (Step 3 — Our plan and assumptions)

Our goal is to find the best time to have a Facebook Live session in the group that contains our target audience. To do that, we assume that the more activity there is in the group at a specific time, the most likely our Facebook Live session will gain more traction.

我们的目标是找到在包含目标受众的小组中进行Facebook Live会议的最佳时间。 为此，我们假设在特定时间组中的活动越多，我们的Facebook Live会话最有可能获得更多关注。

So our goal now is to figure out when there is a peak in the group’s activity over time. And by when I mean a specific weekday and time.

因此，我们现在的目标是找出小组活动随时间推移达到峰值的时间。 当我指的是特定的工作日和时间时。

To do that, we are going to grab the last 5,000 posts from the group’s feed. Then we’ll plot the distribution of the times they were updated.

为此，我们将从该小组的Feed中获取最后5,000个帖子。 然后，我们将绘制更新时间的分布图。

We assume that longer posts indicate more activity in the group because members spend more time in the group writing them. So, our next step will be to take into consideration the length of each post in the distribution.

我们假设较长的帖子表明该小组中有更多活动，因为成员在该小组中花费了更多时间来撰写帖子。 因此，我们的下一步将是考虑分发中每个帖子的长度。

Reaction on Facebook is probably a great indication of people engaging with a specific post. Thus, our last step will be to collect the total number of reactions for each post. Then take that into account in the distribution of activity over weekdays and hours.

在Facebook上的React可能是人们参与特定帖子的一个好兆头。 因此，我们的最后一步将是收集每个帖子的reactions总数。 然后在工作日和小时的活动分布中考虑到这一点。

Because reactions may come after the post, we should be cautious using this data analysis approach.

由于帖子发布后可能会发生reactions ，因此使用此数据分析方法时应谨慎。

步骤4 —让我们分析一些数据！ (Step 4 — Let’s analyze some data!)

To start a Jupyter notebook, you should execute:

要启动Jupyter笔记本，您应该执行：

ipython notebook

and then choose New → Python 2.

然后选择新建→Python 2。

To analyze and plot the data, we are going to use the numpy and matplotlib libraries. These are very popular Python libraries you should use to better analyze your data.

为了分析和绘制数据，我们将使用numpy和matplotlib库。 这些是非常流行的Python库，您应该使用它来更好地分析数据。

Let’s import all the libraries we need:

让我们导入所需的所有库：

import matplotlib.pyplot as pltimport numpy as npimport facebookimport urlparseimport datetimeimport requests

and specify our access token and group id:

并指定我们的访问令牌和组ID：

ACCESS_TOKEN = 'INSERT_ACCESS_TOKEN_HERE'GROUP_ID = '319479604815804' # Web Design and Development group

Then, let’s initialize the API object with our access token:

然后，让我们使用访问令牌初始化API对象：

graph = facebook.GraphAPI(ACCESS_TOKEN)

Now we want to grab the posts from the group’s feed. To avoid errors during the API calls, we will limit each API call to 50 posts and iterate over 100 API calls:

现在我们要从论坛的提要中获取帖子。 为了避免在API调用期间出错，我们将每个API调用限制为50个帖子，并迭代100个以上的API调用：

posts = []url = "{}/feed?limit=50".format(GROUP_ID)until = Nonefor i in xrange(100):    if until is not None:        url += "&until={}".format(until)    response = graph.request(url)    data = response.get('data')    if not data:        break    posts = posts + data    next_url = response.get("paging").get("next")    parsed_url = urlparse.urlparse(next_url)    until = urlparse.parse_qs(parsed_url.query)["until"][0]

In each API call, we specify the until parameter to get older posts.

Now, let’s organize the posts into weekdays and hours of the day:

weekdays = {i: 0 for i in xrange(7)}

hours_of_day = {i: 0 for i in xrange(24)}

hours_of_week = np.zeros((7,24), dtype=np.int)for post in posts:    updated = datetime.datetime.strptime(post.get("updated_time"), "%Y-%m-%dT%H:%M:%S+0000")    weekday = updated.weekday()    hour_of_day = updated.hour    weekdays[weekday] += 1    hours_of_day[hour_of_day] += 1    hours_of_week[weekday][hour_of_day] += 1

and then, plot the results using matplotlib bar charts:

然后，使用matplotlib条形图绘制结果：

plt.bar(weekdays.keys(), weekdays.values(), color='g')plt.show()

plt.bar(hours_of_day.keys(), hours_of_day.values(), color='r')plt.show()

With only this basic analysis, we can already learn a lot about better or worse time slots for broadcasting to this group. But it does not seem informative enough. Maybe because the data is divided into 2 graphs and missing some critical information.

仅通过这种基本分析，我们就可以学到更多或更好的时隙广播给该组。 但这似乎还不足以提供信息。 可能是因为数据被分为2个图，并且缺少一些关键信息。

Let’s try to present a heat map of the data, that enables us to see 3D information:

让我们尝试呈现数据的热图，使我们能够查看3D信息：

plt.imshow(hours_of_week, cmap='hot')plt.show()

Well, this is much better! We can see that the group is very active on Monday to Friday between 6:00 am and 10:00 am.

好吧，这好多了！ 我们可以看到，该小组在星期一至星期五的6:00 am至10:00 am之间非常活跃。

Now let’s take into consideration to post length and see how it affects the results:

现在，让我们考虑发布时长，看看它如何影响结果：

weekdays_content = {i: 0 for i in xrange(7)}hours_of_day_content = {i: 0 for i in xrange(24)}hours_of_week_content = np.zeros((7,24), dtype=np.int)for post in posts:    updated = datetime.datetime.strptime(post.get("updated_time"), "%Y-%m-%dT%H:%M:%S+0000")    weekday = updated.weekday()    hour_of_day = updated.hour    content_length = len(post["message"]) if "message" in post else 1    weekdays_content[weekday] += content_length    hours_of_day_content[hour_of_day] += content_length    hours_of_week_content[weekday][hour_of_day] += content_length

The heatmap we get:

我们获得的热图：

This is nice but should be treated with caution. On one hand, we can see a very specific time that is the optimized time slot to have our Facebook Live session. But, it might be an outlier of a super long post.

很好，但应谨慎对待。 一方面，我们可以看到一个非常特定的时间，这是进行我们的Facebook Live会话的最佳时间段。 但是，这可能是超长职位的离群值。

I’ll leave it to you to figure it out in your next data analysis project. Take a larger amount of posts or grab an older batch of 5000 posts from the group’s feed.

我将留给您在下一个数据分析项目中加以解决。 从该小组的供稿中获取更多信息或获取旧的5000条信息。

To take reactions into account when analyzing the data, we need to make another API call for each post.

为了在分析数据时考虑到reactions ，我们需要为每个帖子进行另一个API调用。

This is because it’s a different API endpoint:

这是因为它是不同的API端点：

weekdays_reactions = {i: 0 for i in xrange(7)}hours_of_day_reactions = {i: 0 for i in xrange(24)}hours_of_week_reactions = np.zeros((7,24), dtype=np.int)for i, post in enumerate(posts):    url = "https://graph.facebook.com/v2.10/{id}/reactions?access_token={token}&summary=total_count".format(    id=post["id"],        token=ACCESS_TOKEN    )

headers = {        "Host": "graph.facebook.com"    }

response = requests.get(url, headers=headers)

try:        total_reactions = 1 + response.json().get("summary").get("total_count")    except:        total_reactions = 1

updated = datetime.datetime.strptime(post.get("updated_time"), "%Y-%m-%dT%H:%M:%S+0000")    weekday = updated.weekday()    hour_of_day = updated.hour    weekdays_reactions[weekday] += total_reactions    hours_of_day_reactions[hour_of_day] += total_reactions    hours_of_week_reactions[weekday][hour_of_day] += total_reactions

We used a low-level approach by specifying the exact HTTP request and did not use the Facebook Python library. This is because that library doesn’t support the last version of the Facebook API required when querying the reactions endpoint.

我们通过指定确切的HTTP请求来使用低级方法，而未使用Facebook Python库。 这是因为该库不支持查询reactions端点时所需的Facebook API的最新版本。

The heat map generated from this data:

根据此数据生成的热图：

We can conclude that the three approaches we used agree on Monday and Wednesday, 6:00 am to 7:00 am.

我们可以得出结论，我们使用的三种方法在星期一和星期三上午6:00到上午7:00达成一致。

结论 (Conclusions)

Data analysis can be challenging and often requires creativity. But it also exciting and very rewarding.

数据分析可能具有挑战性，通常需要创造力。 但这也令人兴奋并且非常有意义。

After choosing our time to broadcast on Facebook Live based on the analysis presented here, we had a huge success and a lot of traction during our Live session.

根据此处介绍的分析选择了在Facebook Live上进行直播的时间后，我们在Live环节中取得了巨大的成功并获得了很多关注。

I encourage you to try and use data analysis to make data-driven decisions in your next business move. And start thinking in terms of data-driven decisions.

我鼓励您尝试使用数据分析在下一步业务中做出以数据为依据的决策。 并开始根据数据驱动的决策进行思考。

You can find the Github repository here.

您可以在此处找到Github存储库。

I originally published this on CodingStartups.

我最初将此内容发布在CodingStartups上 。

翻译自: https://www.freecodecamp.org/news/the-best-time-to-stream-on-facebook-live-my-analysis-5-000-facebook-posts-c8346b732d0f/

facebook 直播