The marketing scope is growing at a very fast pace with new technologies. Machine learning is contributing significantly to this field. Now, developers use Reinforcement Learning in marketing. This is evolving rapidly with advancements in eCommerce, digital mobile and with changing consumer demographics. A recent study demonstrates how e-commerce will acquire about 17.0% of retail sales by 2022, from a projected 12.9% in 2017. Through these, we can easily predict the trends which show more and more people are moving online for their purchases. They are heavily influenced by their digital activity when doing in-store purchases.
These predictions give reinforcement learning in marketing a wider scope. The business can boom using this technology to a greater extent. In the US market, consumer segments like Hispanics and Millennials, are harder to understand. Due to this, it is difficult to provide personalized and targeted messaging to this audience. Using reinforcement learning in marketing will help the potential seller to sell their product at ease.
Another study conducted showed that nearly 50% of Hispanic and Latino consumers were dependent on social media during their shopping. So, using reinforcement learning in marketing is critical to have a deep understanding of these consumers. This help sellers to provide a customer-centric experience.
What is Reinforcement learning (RL)?
Basically, machine learning is the process to make computers/machines to learn using algorithms. To do autonomously action according to the environment by assessing data from real-world interaction and observation; making predictions or solving problems.
RL is a subset of Machine Learning. Reinforcement, it is evaluating data about the previous activity. Then, acting accordingly with the environment to achieve the best long-term result. Just like the human brain does choice over good or bad before doing a task.
Also, Read | Python For Machine Learning: Good or Not?
How does It Help Marketers?
According to research by Business Insider, AI is the fastest growing marketing technology. It is expected to increase by 53% over the next year. The study also stated one of AI’s main advantages is that it can turn marketers from “reactive to proactive planners”, allowing them to plan campaigns more efficiently; particularly thinking about segmentation, tracking, and keyword tagging.
In other words, marketers can transform complex data generated by varied digital transactions into granular, real-time, usable insight.
RL Adds The Personal touch
To compete with the array of digital content and buying options available to today’s consumers, marketers must ensure messaging resonates on a personal level and forms part of an engaging, streamlined online journey.
Along with this shift will come three core challenges: the rising number of platforms and devices customers use, growing expectations around consistent and personalized engagement, and accelerating demand for new products and experiences.
Reinforcement Learning armed with advanced behavioral insights and predictive ability, marketers can achieve hyper-personalization; where messaging is not only tailored to match a consumer’s current position in their unique journey, but also what is most likely to interest them right now. For example, if a consumer was browsing online with the intent to purchase a new cell phone, RL would allow marketers to analyze their previous behavior to predict when would be the best time to serve a discount offer and get the most positive response from that consumer.
Avoiding ad overload using reinforcement learning for both consumers and brands
The growth of automated, programmatic advertising can sometimes mean there is little control over the frequency of ads, and so it is not surprising that as a result of seeing the same ads too often consumers have resorted to installing ad-blocking software.
RL algorithms can assess reactions to messaging and determine the ideal frequency for consumers. They can also inform real-time bidding activity in the programmatic marketplace, using predictions about consumer behaviour to calculate which display ads to buy. This means advertisers benefit from a more efficient process, and hopefully, as a result, growth in online conversions turn to browse into sales.
A Practical Application of Reinforcement Learning
Using behavioral data like social media likes, browsing the history, and previous purchases, the platform can predict which channels and messaging will elicit the most positive response among defined audiences. Plus, by continually capturing data and tracking patterns, its performance improves with experience: greater insight into what has or hasn’t worked before better informs the targeting decisions of today.
Recently migrated to Google’s Cloud Platform, Logico has improved bidding accuracy by 75%. It has optimized its processing speed to maximize ad inventory and run complex algorithms in less than 100 milliseconds.
As the example above illustrates, the adoption of RL is already showing impressive results for digital marketing campaigns. This can be a good thing for brands wanting to move with consumers through the increasingly complex digital journey to offer more positive experiences. By taking advantage of machines that think like humans, marketers can use the theory of mind to deliver campaigns. This will not only boost brand reputation and sales but also consistently give consumers what they want.