Online shoppers are no doubt familiar with the pattern: they’re browsing for the perfect pair of running shoes only to have numerous running shoe brands follow them around the rest of the Internet as they browse news sites and social networks.

They suddenly see ads for local races and foot deodorizers, too. What they likely don’t know is that behind these well-timed, highly-relevant ads is an increasingly sophisticated array of technologies to help these ads land like precision-guided enticements in their browser.

Welcome to advertising in the age of artificial intelligence.

A brief overview on artificial intelligence in CNBC

Like so many industries awash in information, advertising is being consumed and transformed by technology. The buying and selling of ads used to be mediated by humans. With the rise of programmatic advertising, it’s increasingly mediated by machines. Advertising has become Adtech.

Thanks to software powered by artificial intelligence, these machines are smarter than ever - helping marketers reach the right customer, at the right time with the right ad.

## So What Is Programmatic Advertising?

Programmatic advertising is essentially a technology infrastructure that arbitrates the buying and selling of digital advertising. What used to be handled by humans with contracts and insertions orders is now transacted by computers talking to each other. This advertising can land on your laptop, your mobile phone, digital billboards dotting a highway and even smart TVs. Though not very old, programmatic advertising has grown rapidly into the dominant form of digital advertising.

Don’t take our word for it.

## AI-Powered Creativity

Beyond making the the most effective bids, AI can also maximize the effectiveness of your creative assets. Marketers can pool their creative assets - ad copy, images, color schemes, video--into a template that software can then automatically assemble and display to customers groups it thinks will be most receptive.

This Dynamic Creative Optimization, as it’s called, doesn’t just produce a single static ad. It can mix-and-match this assemblage of creative assets based on the data it has on the potential customer to create the most effective pitch for a given customer at a given moment. It can continually test these combinations against internal metrics to improve the effectiveness of a given ad campaign. Maybe ads with “buy now!” perform better than ads with “call now!” in the tagline. Or maybe ads with puppy pictures outperform ads with kittens (hard to believe, I know).

## Real-life case study example

One of our clients is an RTB platform. We were asked to design and build a Reinforcement Learning Model, which will be used as a bidding advisor. The main problem with developing an RL model is the fact that it learns "on the fly." It means that typical solutions are an initialization of a random policy, implementation into the environment and letting the model take actions and learn from them. This raises an issue, because redirecting part of traffic to the model, which at first bids randomly and it is difficult to estimate how long it will take the model to learn to produce sensible bids.

With that in mind, we decided to create a model that makes use of historical data to produce a bidding strategy. The method we came up with, enables us to deploy a model with already learnt strategy, but it is never possible to fully evaluate your model without deploying it. This is because of the nature of RL problems. For example, using historical winning bids, we know that higher bids would have also won, but we don't know whether a lower bid would have won or lost.

In a nutshell, our proposed algorithm assumes that there exists a random variable which represents a minimum winning bid and that it depends on the particular website and user. Using historical data, we were able to build a function that approximates the conditional distribution of that random variable. Using that approximation, we then determine expected profit as a function of a bid and directly optimize it. The bid that maximizes the expected profit is a bid returned by the model.

Once implemented, our solution managed to increase the company's average profit by 70%.

### Bottom Line

For modern marketers, leveraging mountains of customer data in a real-time environment requires intelligent software backed by some serious computer power. Right now, Google and Facebook are devouring ad dollars because they have both. But the tools of AI-powered advertising aren’t out of reach to other large and mid-size companies.

Sigmoidal’s team of data scientists, developers and AI experts have built AI adtech solutions that have saved their customers thousands. They can do the same for you.