AI, NLG, and Machine Learning

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One of the segments where AI has made a significant impact is advertisement and marketing -- by streamlining the delivery of advertisement and content to the most optimal destinations.

By Bipal Shakya
May 7, 2020

Artificial intelligence (AI), in one form or another, is almost everywhere in the modern world. However, unlike how it is usually demonstrated in the movies, its existence is not always apparent or quite as dramatic.

One of the segments where AI has made a significant impact is advertisement and marketing. You can see proof of it in how every Amazon dashboard is different for each individual or how Netflix uniquely curates its movies for each of its users.

It is barely any secret that the world of marketing and advertisement is a lucrative one. Super Bowl advertisements go for millions of dollars, and corporate advertising agencies are worth billions of dollars. This is perhaps rightly so because advertisements have an incredibly strong impact on a general mass of people.

Understanding the value of this market is where AI comes in, to streamline the delivery of advertisement and content to the most optimal destinations that result in the highest probability of customer acquisition.

AI-powered advertisements enable critical insights to be generated from a vast amount of data. Furthermore, AI allows us to draw complex relationships, combinations, and permutations based on cross-domain data. This further enables multidimensional advertisement analysis, with variables acting from infinite and ever-growing tangents. People not having to manually look at each and every possible pattern not only cuts down on costs but also opens possibilities of discovering patterns and opportunities that would not have been otherwise uncovered.

The reduction in cost also allows intelligent advertisement services to be accessible beyond big corporate companies. It allows smaller businesses to leverage the services without having to bear the brunt of a heavy price tag that is usually associated with the services of big ad agencies.

In addition to that, these intelligent advertisements could get even better leverage on reachability when coupled with globally popular platforms, such as Instagram, Facebook, TikTok, and Google. Most of these apps and platforms already provide insightful end-user data from their own sites, as well as others, which further augments the quality of the data we require to further personalise and therefore greatly increases the efficiency of advertisements and marketing endeavours.

Components of AI advertisement

Intelligent advertisement systems are complex and immensely powerful. But they are built on fundamental principles. Essentially, intelligent advertising is all about connecting a product or advertisement content with similarities—similarities between one product and other products, or between a product and people, or even between a product and patterns.

In a rudimental sense, there are three distinct components of AI advertisement:

1. Advertisement content recommendation and target marketing

There are several fundamental models that enable intelligent advertisement via recommendation and target marketing:

Popularity model
The popularity model is quite straightforward. This is essentially a counter against each product, where the popular products (counted either by views, likes, or any other form of interaction) are recommended to everyone. The limitation to this model is that this is not a holistic assessment, and it only counts in a product’s general popularity as a factor.

User-user collaborative model
This model aims to bring collaboration between users to recommend the most appropriate products. Its goal is to find similarities between customers and to recommend products across customers with similar interests and tastes. The primary challenge this model has is that as the user base grows, the model becomes more and more complex and slow because it needs to define relationships between exponentially growing entities.

Item-item collaborative model
To tackle the problems posed by collaborative filtering models, this model focuses on associating similarities between products or items instead of customers. However, the challenge exists to define the features of the product items that would enable the model to define relationships between product items.

Association rule mining model
This model eschews defining relationships between products based on their features and rather focuses on behavioural patterns from customers. An example of this would be: If 90 percent of customers who interacted with Item A also interacted with Item B, when a new customer interacts with Item A, the new customer could be recommended Item B, too.

Hybrid filtering model
Each of these models has their pros and cons and are not very powerful on their own. Therefore, a complete audience targeting or recommendation system will integrate a combination of them and will layer them along with business logic filters (such as geographical targets or age demographic targets) to get the most appropriate result.

2. Advertisement creation or suggestive creation

In addition to simply gaining better insights about the customers, AI in advertising is also helping content creators gain better insights about the advertising contents. AI models could generate data-driven contents that analyse the appropriate components to build advertising content. For example, based on the target demographic and data available, the AI model could build or suggest a marketing content’s colour palette, word usage, or even placements.

3. Reports and insights

This is the resulting compilation of intelligent advertising. In addition to being an excellent way of visualising and reporting estimated viewership and performance of each campaign or content, the result data also enables the system to learn from itself and to correct or adjust its assumptions for increased accuracy and cost efficiency. Adding automation of business logics to intelligent ad outcomes could further increase efficiency on action and reaction to the reports generated. For example, if a discount campaign did not perform well enough to hit the optimum profit threshold, the campaign could be halted. This would ensure that discounts and promotion campaigns are cost-optimised to the most.


Like any industry, AI marketing has its own set of challenges, one of which is talent acquisition. Acquiring the right people for this domain is especially challenging because of the unique blend of computer expertise and marketing acumen required for the role.

Secondly, since the domain heavily depends on personal data collection, it has had a lot of brushes with privacy and security concerns. The personalised data collection enables the AI ad system to curate advertisements unique to each user, but this also means that heightened security measures and active data breach deterrents are essential to the system.

Finally, a rather unique challenge that this domain faces is design and user experience. Advertisement is intimately tied to design and “look and feel” of the contents. And when these design-critical advertisement contents go out through an automated system to a vast and complex network of destinations, it becomes an immensely challenging task to ensure that the content design looks equally appealing and consistent throughout its diverse destinations.

In Conclusion

Products are never going to go away, and neither are advertisements. But it would be interesting to see how this domain evolves, as we find more ways to interact with technology. Home IoT’s smart fridges encourage customer’s direct interaction with products. And virtual reality technology could redefine and recreate an entirely different virtual world. These developments are promising technologies that are augmenting the horizons for intelligent advertisement systems to evolve towards.

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