Reinforcement learning (RL) is a field in machine learning that involves training software agents to determine the ideal behavior within a specific environment that is suitable for achieving optimized performance. In reinforcement learning, an agent is rewarded for any positive behavior (to encourage such actions) and punished for any negative behavior (to discourage such actions). Ultimately, an agent can learn the desired behavior that maximizes the total reward.
This nascent technology is being applied in various spheres to escalate processes and maximize outputs. In digital marketing, reinforcement learning is promising to revamp the industry and modernize various operations.
Here are five examples of application of reinforcement learning in digital marketing.
1. Creating personalized recommendations
Personalized product recommendations provide customers with the personal touch they need to make purchase decisions. However, when delivering individualized recommendations at scale, digital marketers often encounter various obstacles, such as popularity biases, extensive or limited customer data, and customers’ constantly evolving intents.
Reinforcement learning is proving to be capable of solving dynamic digital marketing problems so that high-quality recommendations can be delivered that resonate with customers’ specific preferences, needs, and behavior.
For example, a team of researchers from the Chinese Nanjing University and Alibaba Group introduced a reinforcement learning algorithm, called Robust DQN, and demonstrated its capability to stabilize the estimation of reward and deliver efficient online recommendations – even in real-world, dynamic environments.
When the researchers applied Robust DQN to the largest e-commerce platform in China, Taobao (which is owned by Alibaba), the algorithm achieved optimized performance in delivering individualized recommendations to customers.
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2. Optimizing advertising budgets
The challenge that most digital marketers face is how to optimize their promotion efforts and get the most value from every dollar spent. Of all the hundreds of ads posted, which ones are delivering the most return on investment (ROI)? Which campaigns are proving to be costly and need to be stopped from running? Which ones are attracting the most loyal customers?
Getting credible answers to such questions can be overwhelming. However, reinforcement learning is promising to provide online marketers with easy and reliable methods for maximizing the returns on their investments.
For example, to illustrate that RL can assist in bid optimization, a group of researchers from the Alibaba Group developed a multi-agent reinforcement learning (MARL) algorithm and used it in advertisement auctions. Interestingly, the algorithm showed impressive results: the MARL bids resulted in 240% higher ROI with the same budget spent.
Another group of researchers from Tianjin University and the Alibaba Group demonstrated how advertising budgets can be optimized by using an algorithm that assigns ad slots based on how user interests change dynamically. The researchers proposed a constrained two-level structured reinforcement framework that aims to adaptively expose advertising products to customers based on their likelihood to make the purchase decision, and thus increase the advertising ROI.
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3. Selecting the best content for advertisements
Most online marketers find difficulties in choosing the right content that can assist in achieving their advertising goals. However, by leveraging reinforcement learning, which is rewards-based and links positive actions to desired results, the process of selecting the best content for advertising campaigns can be simplified and optimized.
For example, Baidu, the Chinese equivalent to Google, deployed a deep reinforcement learning algorithm, called Moonrise, and recorded significant improvements in search relevance and ad performance. Previously, Baidu had been relying on supervised learning models, which proved to be incapable of providing the desired results, especially when several variables were under consideration.
After implementing Moonrise, the algorithm could suggest better keywords, videos, photos, and other content from Baidu’s extensive library, allowing advertisers to make the best choices on the content to use for targeting. With the deep RL algorithm, Baidu has realized increased conversions and overall ads effectiveness.
4. Increasing customer lifetime value
In digital marketing, the customer lifetime value is an important metric that can assist in projecting the amount of revenue earned during the entire relationship with a customer. Instead of taking a myopic approach and concentrating on short-term results, you should aim at optimizing the lifetime value of your customers and running a successful online business model into the future.
While there are various traditional methods for increasing the customer lifetime value, adoption of reinforcement learning is proving to be a very promising option. For example, researchers from Adobe proposed an RL-based optimization algorithm that displayed personalized ad recommendations for maximizing the lifetime value of customers instead of the traditional approach, where the number of immediate clicks is maximized. By optimizing the customer lifetime value, you generate personalized offers that lead to higher ROI in the long run.
5. Predicting customers’ responses to price plan changes
How to initiate pricing changes, especially price increases, is often a major headache for most digital marketers. Without reliable methods for forecasting buyer reactions, most marketers usually make mistakes when implementing the changes, resulting in costly regrets.
However, with reinforcement learning, you can model forward-looking customers’ actions and appropriately predict their reactions to price plan changes. For example, a researcher from the New York University Tandon School of Engineering created an Inverse Reinforcement Learning (IRL) algorithm that simulates the best upgrade marketing offers by forecasting the future behavior of the targeted group.
This way, it’s possible to gauge the attractiveness of various pricing plans to customers when changes are initiated, allowing you to minimize mistakes. For example, as a cloud storage provider, you can use the IRL algorithm to predict that a group of users with high consumption habits are likely to buy an extra 10GB of storage space in the next 90 days, and can be willing to pay an extra $10 per month for it.
Ready to Deploy Reinforcement Learning?
Reinforcement learning is promising to revolutionize the digital marketing industry and take things a notch higher. As the above examples show, if adopted at scale, this state-of-the-art technology will result in massive improvements and enhance the quality of online marketing outputs.
Are you excited?
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