Many industries today necessarily rely on data, especially in advertising, where brands have much more information to process. More data creates an advantage in a competitive marketplace for consumer attention. In contrast, more data can also be overwhelming.
Due to development of artificial intelligence, tools such as, machine learning makes life much easier, streamlining the data analysis process for brands.
What is 'The State of Machine Learning' Today?
Machine learning, one of the inventions falling under the artificial intelligence umbrella has made tremendous progress in recent years due to some key factors. The average consumer is much more comfortable with the tech providing usefulness to their daily lives.
The falling cost of computing power is one cause for improvement, according to a research. Widespread adoption of internet-connected devices can also be credited. These two factors have arguably led the biggest players in the tech space to invest heavily in advancing AI technology.
Today, there are more than a dozen applications of machine learning for brands and advertisers. Below mentioned are some of the most popular ways brands and advertisers are taking advantage of AI.
According to a 2016 study conducted by 451 Research and Blazent, predictive analytics were found to be the best of machine-learning technology by IT executives in North America. Understanding how someone might behave in the future requires some past analysis. Through rapid data analysis, machine learning can accelerate the process of discovering trends by examining past customer behavior.
Netflix is one of the most distinguished brands putting predictive analytics into practice. The recommended movie users see on the front page of their Netflix screen is curated by an algorithm, which offers recommendations on movies, shows, documentaries and other content it believes a user will like based on the previous watches.
Audience specificity is a key to success, basically in advertising. Machine learning makes this a reality by allowing brands to target potential customers with precise accuracy. As a result, ad campaigns are being optimized abundantly.
Facebook Business Manager provides a firsthand glimpse of how machine learning makes life much easier for advertisers. For instance, every time someone creates a target audience based on interest, machine learning has assisted that process, in the sense that the interests an advertiser can target on Facebook are all generated by a machine-learning algorithm that analyzes information that users voluntarily put up to their Facebook profiles.
Putting a price tag on a product requires intensive market research and in-depth analysis. Machine learning can help brands and advertisers reinforce those tasks.
One such way machine learning is doing it is through dynamic pricing. So, dynamic pricing correlates price and sales trend data along with other variables such as inventory available. The method has gained importance in the course of time, especially in the live events industry.
For example, many professional sports teams have adopted dynamic pricing into their sales and marketing strategies. Factors such as the win/loss records of home and away teams are taken into consideration by machine-learning technology, which then generates a generous ticket price. Ticketmaster was one of the early adopters of dynamic pricing, implementing the system for events as early as 2011.
Lastly, the advancement of technology in our time has made life easier, particularly for brands working to ensure their products are looked upon by the right customers. With appropriate strategy and execution, machine-learning capabilities like the examples described have potential to make life easier for the modern-day advertisers.