Automate four aspects of an ad campaign setup to improve results

Challenge

Increase the efficacy of Meta ads by helping advertisers use tools to automate their ad campaigns.

Overview

In 2022 digital advertising saw a significant shift in the market. During the previous two years, digital advertising and commerce had seen incredible growth, but with new advertising platforms emerging and changes in consumer behavior, Meta needed to re-tool to remain competitive in the ad space. One of these tools was a suite of AI products to support advertisers in setting up and running campaigns.

Using AI to automate ad campaigns offers numerous advantages that can enhance advertising effectiveness:

  1. Targeting Precision: AI-driven tools can analyze vast amounts of user data to identify relevant audiences with high precision. This allows advertisers to target their ads to the most promising demographics and individuals, increasing the likelihood of reaching potential customers who are more likely to engage with their ads.

  2. Real-Time Optimization: AI-powered algorithms can continuously monitor ad performance and adjust targeting, bidding, and ad creative in real-time. This dynamic optimization ensures that ad spend is focused on the most effective campaigns, maximizing return on investment (ROI).

  3. Personalization: AI can analyze user behavior and preferences to deliver personalized ad experiences, tailoring ad content to individual users' interests. Personalized ads tend to perform better and resonate more with the target audience.

  4. Automated A/B Testing: AI can conduct A/B testing on various ad elements such as images, copy, and calls-to-action, quickly identifying the most effective combinations for different audience segments. This saves time and resources while refining ad strategies.

  5. Ad Performance Prediction: AI algorithms can forecast the performance of ad campaigns based on historical data and market trends. Advertisers can make informed decisions about budget allocation and strategy, reducing the risk of ineffective campaigns.

  6. Ad Scheduling Optimization: AI can determine the best times and days to display ads based on user activity patterns and historical data. This ensures ads are shown when the target audience is most likely to be engaged.

  7. Budget Optimization: AI can dynamically adjust ad spending across different campaigns and ad sets based on performance data. It allocates more budget to higher-performing ads, preventing wasteful spending on underperforming ones.

  8. Ad Compliance Monitoring: AI tools can help ensure that ads comply with platform policies and relevant regulations. This minimizes the risk of ad disapproval or suspension.

  9. Scaling Efficiency: Automating ad campaigns with AI allows advertisers to efficiently scale their efforts without substantially increasing the workload. This is especially beneficial for businesses with large or complex advertising strategies.

  10. Competitive Edge: As AI-driven advertising becomes more prevalent, using advanced automation tools can provide a competitive advantage by optimizing campaigns more effectively than traditional manual approaches.

Overall, leveraging AI to automate ad campaigns with Meta empowers advertisers to make data-driven decisions, improve targeting, and optimize performance, leading to higher ROI and more successful ad campaigns.

See below for a sample of the content created for CAPI. If you would like to see the entire course with acitvities, you can find it here.

Machine learning in advertising

This lesson prepares you to:

  • Identify the value of machine learning.

  • Understand which questions machine learning can help answer.

  • Choose the best objective and optimization event to guide the system.

Questions machine learning can help answer:

Machine learning is now a fundamental part of digital marketing and helps show the right ads to the audience you want to reach. As you learn more about how we use machine learning, you can better understand how to set up your ad campaigns to achieve the optimal return on investment.

Machine learning uses serial algorithms and predictive analytics to find the best audience at the right time at the best price. It also processes countless data points in real-time to help you answer questions such as:

What is liquidity?

To get the most out of machine learning, you must first understand liquidity. Liquidity is the condition that gives the delivery system the flexibility to move ad spend where it’s likely to achieve the best results. This course covers four aspects of your campaign where you can increase liquidity:

  • budget

  • placement

  • audience

  • creative

Guide the system towards your business goal

Machine learning isn't going to replace the role humans play in advertising. It's up to you to select the right ad objectives and optimization events for your business goals.

Let's look at an example. The marketing team at Jasper’s Market, a national grocery chain, wants to expand its digital presence. They recently launched an app that allows people to have organic produce shipped directly to their door. Their goal is to increase the number of people who install their app.

It’s clear the best ad objective for this example is app promotion. However, selecting the right objective alone wouldn't yield the business results the team wants in the most efficient way. What about people who already have the app? Unless you use data sources or exclusions, Ads Manager may continue to show your ads to people who already have the app.

While machine learning can make complex calculations easily, it's only as good as the guidance and flexibility you provide. To guide the system toward better results, the team should use their data to exclude the people who already have the app installed.

The ad delivery system therefore needs input to guide it toward achieving your business goal.

Once your objectives and optimization events are selected, you should consider broadening parameters to increase liquidity across four areas: budget, placement, audience, and creative. This gives machine learning the flexibility to allocate budgets across ad sets, identify the right audience, select the best placements and choose the best creative elements.

Knowledge check

This is a static image, to select an answer please go to the course page.

EXPLORE OTHER PROJECTS

  • CONTENT DEVELOPMENT: TECHNICAL EDUCATION FOR CONVERSIONS API

    Supporting Meta users with the implementation of Conversions API (CAPI)

    Learn More

  • CURRICULUM DEVELOPMENT: HEADSPACE FOR EDUCATORS PROGRAM

    Expanding Headspace’s program into the classroom.

    Learn More

  • PROGRAM DEVELOPMENT: CYBERSECURITY TECHNICAL TRAINING

    Building a tool-agnostic, vocational, online security program from the ground up.

    Learn more

  • CURRICULUM DEVELOPMENT: NEEDFINDING WORKSHOP

    Adapting traditional curriculum based on audience needs and constraints.

    Learn More

  • CONTENT DEVELOPMENT: ADVERTISING AUTOMATION

    Teaching users to utilize machine learning and automation tools.

    Learn More

LET’S CHAT