Streaming on Facebook Live can be a powerful marketing strategy for startups and businesses to share knowledge, provide value, get exposure and collect high-quality leads. When preparing your Facebook Live session upfront by researching about your target audience and building a detailed agenda, the session can boost your business dramatically.
As a chief of product and technology of my previous startup dealing with fraud detection, I decided to try Facebook Live as a new marketing strategy when it was still fairly new. Back then, once a new Facebook Live session was up, relevant people got Facebook notifications to join the session, which increased the exposure even more.
There are many posts talking about how to better build your Facebook live session in terms of topics to cover, building an agenda, camera angles, session duration, etc. But there is one piece of the puzzle that business owners and marketers often tend to forget or not pay attention to — when is the best time to stream your Facebook Live session?
Answering this question can be done using an educated guess based on your familiarity with the target audience, For example:
- Pregnant moms are ready to consume your Live session on Monday afternoon.
- Teenagers at the ages of 18–22 are in the right mindset on Saturday morning.
But nowadays, when 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.
Almost every marketing platform or social network opens API services that you, as a technological entrepreneur, can easily consume. This data, when analyzed properly, can be derived to valuable conclusions that can drive your business objectives way beyond your competitors.
This approach is often called Data-driven decisions.
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 actually stop guessing and start making data-driven decisions.
I like to think of data-driven decisions as crowd-sourcing. If you had a chance to watch this TED talk by Lior Zoref, where Lior invited an ox to the stage and asked the audience to guess what its weight is, you were probably overwhelmed by how accurate the crowd’s average was compared to the real weight of the ox: 1,792lbs vs. 1795lbs!
When you’re making guesses regarding 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 300lbs or 8000lbs, which will probably cost your business a lot of unnecessary expenses.
But, if you’re using the wisdom of the crowd to make data-driven decisions, which you can do in almost any decision you make online, you’ll most likely be ahead of every other individual, or in business terms, ahead of your competitors.
Although I’m not a pure marketer, with basic data analysis skills, I can push my business forward dramatically in all aspects, including marketing.
In this post, I’m going to walk you through a practical step-by-step guide about how to access Facebook data, analyze it based on our needs on deriving conclusions about when is the optimized time to broadcast on Facebook Live.
In order to follow this guide you need:
- A Facebook account
- A Facebook group you would like to analyze (if it’s a private one, you need to be a group member)
- Python 2.7 installed
- Jupyter notebook installed
- Facebook graph API Python library installed
A Jupyter notebook as a highly recommended tool for data analysis in Python. It has a lot of highlights, but on top of all, it enables you to run snippets of code and save the results in memory, instead of running all of 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.
Although it’s not essential, I always recommend working inside a Python virtual environment. Here is a post I wrote about the advantages of a virtual environment when using Python.
Finally, I highly recommend working on an Ubuntu environment when doing data-analysis using Jupyter notebooks.
Step 1: Getting the Facebook group ID
In order 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.
Lookup-id.com is a nice tool you can use to easily find the ID of a group based on its URL. Copy the URL of your group and paste it in the search bar.
In this post, we will use the group: Web Design and Development.
ID: 319479604815804
Step 2: Getting to know the Graph API Explorer
In order to get the most out of Facebook API, besides documentation, Facebook has developed a playground for developers called the Graph API Explorer.
The Graph API Explorer enables us to easily get a temporary access token and start examining the capabilities that Facebook API has to offer.
Click on Get Token, and then on Get User Access Token. Don’t select any permission, just click Get Access Token.
Facebook API has many endpoints you can use, but in this guide, we are going to use two main endpoints:
- The group endpoint
- The reactions endpoint
In order to figure out the structure of the response you’re expecting to get from a specific endpoint, you just need to specify the endpoint URL and click 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:
319479604815804/feed
and hit Submit.
You should now see the last posts from the group’s feed in a JSON structure, containing 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:
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.
This way you can play around with all of the features the Facebook API has to offer.
Another wonderful tool for examining API endpoints of APIs which don’t offer a playground is Postman. You can read more about this tool, as well as essential tools for web developers here.
Step 3: Our plan and assumptions
Our goal is to find the optimized time interval to have a Facebook Live session in the chosen group that contains our target audience. In order to do that, we assume that we the more activity there is in the group at a specific time, the most likely our Facebook Live session with gain more traction.
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.
In order to do that, we are going to grab the last 5000 posts from the group’s feed and plot the distribution of the times they were updated on.
We assume that longer posts indicate on more activity in the group because members spend more time in the group writing them. Therefore, our next step will be to take into consideration the length of each post in the distribution.
Reaction on Facebook is probably a great indication of people engaging with a specific post. Therefore, our last step will be to collect the total number of reactions for each post and take it into account in the distribution of activity over weekdays and hours.
Because Reactions probably come after the post, we should be cautious using this data analysis approach.
Step 4: Let’s analyze some data!
In order to start a Jupyter notebook, you should probably execute:
ipython notebook
and then choose New → Python 2.
In order to analyze and plot the data, we are going to use numpy and matplotlib libraries. These are very popular Python libraries you should use in order to better analyze your data.
Let’s import all the libraries we need:
import matplotlib.pyplot as plt
import numpy as np
import facebook
import urlparse
import datetime
import requests
and specify our access token and group 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:
graph = facebook.GraphAPI(ACCESS_TOKEN)
Now we want to grab the posts from the group’s feed. In order to avoid errors during the API calls, we will limit each API call to 50 posts and iterate over 100 API calls:
posts = []
url = "{}/feed?limit=50".format(GROUP_ID)
until = None
for 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:
plt.bar(weekdays.keys(), weekdays.values(), color='g')
plt.show()
(0 represents Monday)
plt.bar(hours_of_day.keys(), hours_of_day.values(), color='r')
plt.show()
All times specified in IST.
Only with this very basic analysis, we can already learn a lot about better and or worse time slots for broadcasting on this group. But it seems not informative enough. Maybe because the data is divided into 2 graphs and missing some critical information.
Let’s try to present a heat map of the data, that enables us to see 3D information:
plt.imshow(hours_of_week, cmap='hot')
plt.show()
Well, this is much better! We can clearly see that the group is very active on Mondays to Fridays between 6 am and 10 am.
Now let’s try to take into consideration to post length and see how it affects the results:
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:
This is nice but should be treated with caution. On one hand, we can clearly see a very specific point in time that is the optimized time slot to have our Facebook Live session. On the other hand, it might be an outlier of a super long post.
I’ll leave it to you to figure it out in your next data analysis project. What I suggest you do is to take a larger amount of posts or grab an older batch of 5000 posts from the group’s feed.
In order to take reactions into account when analyzing the data, we need to make another API call for each post, because it’s a different API endpoint:
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
The reason we used a low-level approach here by specifying the exact HTTP request and not using the Facebook Python library is that it doesn’t support the last version of the Facebook API that is required when querying the Reactions endpoint.
The heat map generated from this data:
We can conclude that the three approaches we used agreed on Monday and Wednesday, 6–7am.
Conclusions
Data analysis can be challenging and often requires creativity. But it also exciting and very rewarding.
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.
I encourage you to try and use data analysis to make data-driven decisions in your next business move. And on top of that, start thinking in terms of data-driven decisions.
You can find the Github repository here.
If you enjoyed this post, please show your appreciations by 👏 it. It’s extremely important to me to understand what to write about next.
This post was published originally on CodingStartups.
CodingStartups is a community built for technological entrepreneurs: developers with entrepreneurial mindset.