In a rapidly evolving world where new products hit the market constantly , every company strives for maximizing their sales and improving their bottom line .One of the ways to do this is to increase visibility and brand recognition for which they may use a powerful marketing tool in their arsenal: Advertising.

However, as the world changes, so do the strategies and technologies utilized by people, and one such transformative force is Artificial Intelligence (AI).Artificial intelligence is a field of computer science that focuses on creating systems and technologies capable of performing tasks that typically require human intelligence. In the confluence of these two fields emerges the realm of computational advertising. This atypical field combines the best of both worlds and enables the use of data driven insights in advertising.It facilitates the effective allocation of resources and elevates the overall effectiveness of marketing campaigns.

The primary goal of computational advertising is to deliver the right ad to the right person at the right time in a highly personalized and relevant manner. This involves analyzing vast amounts of data, including user behavior, demographics, and context, to determine the most suitable ad to display.

One of the ways we can implement computational advertising is through reinforcement learning.. Reinforcement Learning enhances the precision and efficiency of marketing campaigns in many ways

It helps determine the best placements for ads across different platforms and publishers. This ensures that ads are featured in the most relevant and high-performing locations. It optimizes resource allocation and budgetingIt learns from user behavior to offer personalized product or service recommendations, increasing the likelihood of conversions. And it maximizes user engagement. Using Reinforcement Learning , the advertiser can constantly refine the advertisements.

But it doesn't come without its drawbacks, the primary cause for concern being privacy. As reinforcement learning algorithms train on user data, customer data may be exploited for nefarious purposes.Advertisers must be careful to adhere to privacy regulations and ensure the protection of user data. It is also quite complex and resource intensive and often requires teams of employees trained in data analytics and machine learning.And conversion rates alone do not equate to meaningful user engagement.Metrics alone may not capture the full picture of a campaign's success

As a field at the intersection of machine learning and advertising, computational advertising is susceptible to the inherent weaknesses and challenges that both of these domains encounter. computational advertising algorithms can face algorithmic biases reaching only a fraction of the desired target demographic and they are vulnerable to Ad blockers as well . This reduces the overall efficiency of the marketing campaign.

Interdisciplinary Collaboration :
Having considered the merits and drawbacks of computational advertising, we can now make an informed assessment regarding whether a specific company should make the transition from their conventional advertising approach to this innovative method. Nevertheless, the decision to adopt this approach ultimately rests with the company's management.