
Learn to transform millions of data points from Facebook into actionable marketing insights by combining data science with social media marketing, using Python for Facebook analytics to drive campaigns.
Learn to analyze Facebook campaigns with Python, explore the marketing funnel, audience types, and metrics to evaluate campaign performance.
Explore social media analytics for paid ads, covering KPIs like conversion rates, cost per click, and ROAS, and learn to use Facebook ads manager with Python to analyze a campaign.
Explore how the marketing funnel moves customers from awareness to action, using brand awareness, testimonials, and buy now calls to action, with ongoing engagement to convert interest into purchases.
Explore how the Facebook ads manager structures a marketing campaign into campaigns, ad sets, and ads, with awareness, consideration, and conversion objectives guiding key metrics and optimization.
Explore how a campaign splits into ad sets with assets targeting distinct audiences, using placements on Facebook and Instagram, while budget optimization allocates the overall budget to maximize conversions.
Learn how Facebook campaigns end at the individual ad, with text, creative, headlines, and CTAs, and how AB testing compares video and image ads within ad sets.
Explore core, custom, and lookalike audiences on Facebook, from demographics and interests targeting to custom audiences built from email lists, and lookalikes that match your existing users for scalable marketing.
Explore how the Facebook pixel tracks user actions on your site, identifies visitors, and enables targeted ads with custom and lookalike audiences who initiated checkout but didn't complete.
Analyze social media campaigns using key metrics and KPIs to measure sales, reach, and engagement on Facebook, and derive data-driven insights to optimize future marketing strategies.
Explore reach and impressions as top-funnel metrics on Facebook, counting unique users and ad exposures to gauge audience size, campaign visibility, and potential ad fatigue.
Learn how the click-through rate (ctr) measures ad engagement by comparing clicks to impressions on Facebook ads, and how to optimize creative and targeting for better performance.
Discover how link clicks represent the subset of total ad clicks that lead users to a destination, such as a website, when users click a call-to-action link.
Conversions track the completion of desired actions in a marketing funnel; monitor drop-offs from slow mobile pages to unfriendly sign-up forms to boost conversions.
Assess ad value by tracking CPM, CPC, and PR to measure return on investment and avoid overspending. CPM indicates cost per thousand impressions, while targeting and campaign type influence it.
The CPC, or cost per link click, measures the cost of each ad click, and Facebook optimizes CPC in engagement campaigns, letting you compare headline or creative in AB tests.
Understand CPR, or cost per result, and its interchangeable terms, and see how spend per conversion determines cost per sale on Facebook, with optimization to reduce costs.
Learn to calculate ROAS by dividing total ad revenue by ad spend, using 50,000/5,000 as an example; ROAS differs from ROI and may require a three-week assessment window.
Congratulations on mastering new terminology and metrics in this section. Keep practicing the calculations, and refer to the cheat sheet for formulas like CPM and CTR in the lesson resources.
Learn to navigate Facebook Ads Manager to create and manage campaigns, understand the interface and ad creation steps, and explore options to set up your own account or follow along.
Learn to create your first Facebook ads manager campaign by selecting a traffic objective under consideration, naming the campaign, and configuring budget options before moving to an ad set.
Create an ad set for a campaign, define audience from all countries, females aged 18–34 interested in fitness, maximize landing page views, and configure daily or lifetime budgets.
Create and optimize Facebook ads by configuring an ad, ad set, and multiple ads with distinct content, headlines, and creatives; preview placements, track events, and compare performance to maximize ROI.
Examine a social media analytics dataset in a Jupyter notebook using pandas to load, preview, and summarize ad set metrics from a May 2022 conversion campaign with a purchase objective.
Analyze Facebook campaign performance by summing reach, impressions, link clicks, landing page views, and results to reveal funnel bottlenecks; highlight 450k reach, 1.1m impressions, 83 conversions, and €4,000 spent.
Calculate CPM, the cost per thousand impressions, by dividing total spend by impressions and multiplying by 1000, yielding €6.55 for this campaign.
Calculate CPR, the cost per result, by dividing total spent by total results, removing zero-result rows to avoid infinity; the average CPR is €63.8.
Explore how the 365 data science team's ad sets use a consistent naming convention to analyze assets by audience segments and Facebook placements with the same purchase objective.
Learn to categorize ad sets as hot, warm, or cold based on familiarity, analyze metrics with pandas to compare reach, impressions, click-through rate, conversions, and cost per result across categories.
Explore how to categorize ad sets into lookalike and detailed audiences, compute their performance averages, and visualize differences using pandas, seaborn, and matplotlib.
Compare lookalike audiences with detailed targeting, noting similar clickthrough rates but higher conversions and cost per acquisition for detailed targeting in conversion campaigns, while lookalikes boost reach for brand awareness.
Analyze hot ad sets to optimize top of funnel performance by filtering dataframes to hot assets and visualizing reach, impressions, and CPM with seaborn and matplotlib bar charts.
Compare hot assets using CPM and reach, then assess engagement and mid and bottom-funnel metrics to guide budget reallocation for higher conversions.
Analyze warm wall assets to compare reach and impressions; video ads show higher engagement and more link clicks but a 75% drop-off to landing pages, reducing conversions.
Compare cold ad sets by asset type to contrast lookalike and detailed targeting, using bar charts of top funnel metrics to reveal performance and cost efficiency.
Analyze ad set performance by comparing reach, impressions, and CPM across look-alike and detailed targeting assets, identifying cost-effective strategies and engagement drivers.
Analyze conversions and cost per result of ad assets, compare lookalike and detailed targeting assets, and assess spend by the 365 data science team to identify cost-efficient reallocations.
Ready to combine the power of data science with social media marketing and become an invaluable asset to any business?
Do you want to unlock the potential of social media, a tool used by 77% of enterprises, and help businesses skyrocket their growth in today's data-driven world?
Imagine being able to extract actionable insights from social media platforms, optimize marketing strategies, and directly influence business success—all through the power of data.
In the Python for Social Media Analytics course, you’ll gain the dual expertise of a data scientist and a social media marketer, putting you at the cutting edge of business intelligence. With this combined skillset, you’ll not only be a data expert but also someone who understands the nuances of marketing—making you indispensable to companies aiming to boost their social media ROI and drive growth through data-driven decisions.Such abilities give you a competitive edge over data science professionals without domain expertise.
Unique features of This Python For Social Media Analytics Course
Real-World Data: Utilizes real social media data acquired through 365 DataScience's marketing campaigns.
Practical Problem Solving: Emphasizes a pragmatic, data-driven approach to address real-world business challenges.
Comprehensive Skillset: Equips you with the in-demand skillset of social media analytics, differentiating you from other data professionals and enhancing your employability.
Topics Covered
Python
Social Media Analytics
Marketing
Data Analysis
What You’ll Learn
This course prepares you to extract insights from social media data, mainly focusing on Facebook marketing.
A structured learning journey
1. Introduction to Social Media Analytics
The first part of this course is designed to teach you the basics of social media analytics, and how it is used to drive marketing success.
I will also teach you about the marketing funnel, which is a model that illustrates a consumer’s journey with any brand.
By the end of this section, you will be familiar with the fundamentals of social media marketing.
2. Social Media Marketing Terminology
This course segment takes you through the jargon commonly used in digital marketing.
You’ll learn the difference between terms like ads, ad sets, and campaigns, along with data collection mechanisms like the Facebook (Meta) Pixel.
By the end of this section, you’ll have a firm grasp of marketing concepts to work closely with marketing teams and aid in driving organizational success.
3. Social Media Metrics
Social media marketers track various KPIs to measure the success of ad campaigns.
These metrics are then analyzed to improve the performance of future marketing initiatives iteratively.
This is where data analysts come in. Marketing teams work closely with data practitioners to uncover insights from social media performance metrics.
But to deliver data-driven recommendations and improve campaign performance, you must first understand how marketing success is measured.
This section guides you through the metrics tracked in Facebook campaigns and shows how they are calculated with real-world examples.
4. Creating an Ad Campaign on Facebook Ads Manager
This section covers how to use Facebook Ads Manager, which allows you to create and manage your ads on the Facebook platform.
This will give you an understanding of how marketers create social media campaigns, allowing you to work closely with marketing teams and develop an understanding of the strategies employed during the campaign creation process.
By the end of this segment, you’ll understand how social media marketers create, run, and track advertisements on the Facebook platform.
5. Analyzing a Real Marketing Campaign with Python
This is the final part of this course, building on top of all the concepts taught in the previous sections.
Having gained a grasp of various social media metrics and feeling at ease with the Ads Manager platform, you’ll now learn to analyze a Facebook marketing campaign performance using Python.
The dataset we will analyze in this section belongs to a real marketing campaign launched by 365DataScience.
We’ll extract data-driven insights beyond surface-level information to reveal how 365DataScience’s marketing strategies performed.
By the end of this section, you’ll know how to analyze social media data with programming languages like Python.
You’ll also be capable of enhancing a business’s Facebook marketing strategy by delivering quantifiable recommendations backed by data.
Just go ahead and subscribe to this course! If you don't acquire these skills now, you will miss an opportunity to distinguish yourself from the others. Don't risk your future success! Let's start learning together now!