Why is Machine Learning a Next Big leap in Marketing and Advertising?


Why is Machine Learning a Next Big leap in Marketing and Advertising?


The notion of Artificial Intelligence is not limited but it has expanded to different branches as machine learning, deep learning and others. Reinforcement learning (RL) is one of the sub-division of machine learning. Currently, it is advancing at a fast pace and is being used in a myriad domain, such as industries, healthcare, data science, education, business and marketing.


Google’s release of the new Framework for Reinforcement Learning (RL) research:

Seeing the further development in RL, Google recently announced a new framework for flexible and reproducible Reinforcement Learning research. With the aim of discovering new directions for RL, the new framework is claimed to be simple, easy to use, reproducible and enable the researchers to benchmark their ideas instantly against the established methods.

Before diving deeper, it is better to know about reinforcement learning first so that you have a clear idea of how it is going to take marketing to the next level.


Introduction of Reinforcement Learning:

Reinforcement learning is a decision-making approach with the goal of optimizing the output after experiences when interacting with the environment. The agents now enable to solve the complex problems by keeping previous reward or punishment in consideration.

Enough for the intricate talk! Now here we define it through a simple example. You must have heard this example many times and it is true in the context of RL.

When seeing the candle, a child wants to touch the flame, not knowing of the fact that it can burn his hands. But once he touches and burns the finger, he learnt that fire is harmful and never touches that again.

Put RL in this context which learns from the environment and learns to give more goal-oriented results in the next step.


Machine Learning: How can it change the game of advertising and marketing?

Today machine learning is supplementing humans in many ways to perform the best in the least possible time. Designing, music, games, finance, data research, industries, marketing and the list is quite long, so it’s better to jump to our topic.


  1. Data collection for maximizing the ad exposure:

The search behaviour of the customer leaves a footprint for the marketers who use the valuable information for knowing about the intentions and interest of the target audience.

There are many sources of information, preferably from social sites, ratings and reviews. Machine learning helps marketers to get such a big volume of data accurately to target the prospects and customers at the right time and place.


  1. Lookalike targeting:

For this sort of targeting, machine learning creates a customer profile that helps to reach the certain purpose. It suggests reaching the audience which is interested in lookalike ads.

In this way, the marketers are able to make the target audience take the desired actions. This will improve the click rates and visits of prospects and customers to the respective website.


  1. Market Forecasting:

What if you know about the performance of ads before actually served. It can help to resolve many problems and maximize the ads campaign to get an optimal outcome.

This works by interpreting the past ads and similar ads from different advertisers. It covers many aspects of the ads, both minor and major.


  1. Searching through images:

Bing, eBay, Wayfair are few examples of businesses which offer visual search.

Especially in e-commerce businesses, customers can now search the required product through the image. And guess how is it possible?

Definitely through machine learning!


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