Artificial intelligence innovates and gets smarter over time by using advanced training methods. One of these complex methods is deep reinforcement learning (DRL). This process, which combines both deep learning (DL) and reinforcement learning, uses data and rewards to help AI choose the most efficient path and actions to take to achieve an end goal.
Look at DRL in more detail (and simpler terms) to better understand how you can use it to improve your content marketing strategies.
Key Takeaways:
- Deep reinforcement learning (DRL) is a form of machine learning that combines deep learning with reinforcement learning.
- DRL helps you analyze more complex situations.
- You can understand your audience and their needs by using DRL to track their behavior.
- DRL’s insights will also help you find the best topics and keywords to achieve high rankings.
What is Deep Reinforcement Learning?
Deep reinforcement learning is a form of machine learning (ML) that combines deep learning’s neural networks—which allow AI to act human-like—with reinforcement learning’s ability to adjust based on trial and error. Together, it helps AI complete more complex tasks and analyze data with greater accuracy.
Companies worldwide are adopting machine learning into their business practices to improve their processes. Of those businesses, 65% say ML helps them make decisions. Companies that used machine learning in sales and marketing saw a 50% increase in leads. Nearly 60% of marketers use ML to personalize their content.
A friendly introduction to deep reinforcement learning, Q-networks, and policy gradients
AI Terms and Definitions You Need to Know
Here are the four AI learning methods mentioned earlier and how they compare:
- Machine learning is the umbrella term for how artificial intelligence learns by using and adjusting algorithms to perform tasks.
- Deep learning relies on data to make decisions and complete tasks. This process is known as a “neural network” because it helps AI process information similar to a human brain.
- Reinforcement learning analyzes different ways to perform tasks to find the best way that maximizes rewards while minimizing punishments.
- Deep reinforcement learning combines deep learning and reinforcement learning to analyze more complex situations that can’t be solved with data or based on trial and error alone.
How Does Deep Reinforcement Learning Work (in Simple Terms)?
Deep reinforcement learning learns from rewards and punishments to find the best method to complete a task. However, its neural network, acquired from deep learning, gives DRL an added level of understanding.
The image below shows the process of DRL. The diagram shows how the agent will act in an environment. The agent is whoever or whatever is performing the task (like a robot, program, or character in a game). The environment is where the agent is located.
To picture the DRL process, imagine, for example, that you’re playing a video game. Your character might attempt to complete a level multiple times. If you succeed at the level, you’ll continue playing using the same strategy. However, if you fail, you’ll adjust how your character moves through its environment to avoid the same results. This is similar to how DRL performs actions.
At the same time, DRL collects data about its environment to make better decisions about its actions. For example, telling a photographer to “shoot” will yield different results than telling a hunter to “shoot.” While the action is the same, different environments produce different results.
5 Ways to Use Deep Reinforcement Learning in Content Marketing
There are limitless uses for machine learning in content marketing. However, according to the 2019 CMO Survey, about 56% of marketers use machine learning to both personalize their content and to unlock predictive analytics to gain more customer insights.
Here are five DRL strategies you can use in your next marketing campaign:
1. Identify Powerful Keywords and Topics
You already know that keywords and SEO can help your content rank high on searches as long as you use them along with quality content. However, choosing the best keywords is a careful balance of finding phrases with the most search queries combined with minimal competition.
DRL analyzes huge volumes of popular phrases to identify the most relevant topics and keywords for your SEO strategy. You can also use DRL to create even more personalized content with secondary keywords and questions you can address .
2. Streamline Your Content Creation
Deep learning is one of the foundational building blocks of predictive text and natural language processing (NLP). AI writing learns from other materials across the internet and suggests sentences and ideas for your content that will help it rank with the highest-ranked content. Semrush’s 2021 State of Content Ops & Outsourcing report showed that 12% of content writers already use AI writing technology in content creation.
3. Understand How Your Audience Engages with Your Content
Your social posts’ likes and comments only give you a part of the bigger audience engagement picture. DRL can show how long people spend on each page, how many viewers visit your website, and which channels produce the most leads. Seeing how your audience behaves on your website and interacts with your content helps you create more content your audience will engage with.
4. Analyze the Results of Your Marketing Campaign
Use DRL to track each customer’s journey, analyze your marketing campaign results, and learn from its performance to improve future suggestions. For example, DRL can look at sales growth, website views, and conversions to analyze your content’s success.
5. Budget for Your Future Content
Forecasting your content marketing needs can be difficult in today’s ever-changing environment. However, deep reinforcement learning combines financial data from the past with future predictions to make more accurate financial forecasts to help you create your budgets.
Additionally, DRL can predict your return on investment. About 87% of companies use AI to help with sales forecasting, according to a 2017 Statista survey.
Maximize Your Impact Through Deep Reinforcement Learning
You can stay ahead of your competition and create content that connects and converts by using DRL to analyze emerging trends and understand your audience’s content preferences better than before. Our content marketing platform, Contrend, connects artificial intelligence to the content creation process so you can make more informed decisions.
Contact us to learn about our platform and start making smarter content marketing choices using machine learning and DRL.
