How artificial intelligence drives more effective advertising campaigns
Source: Daniel Surmacz

Artificial intelligence (AI) is no longer a futuristic concept, it’s a staple of today. From virtual personal assistants like Siri and Cortana, to image scanners built to identify diseases, to Google’s or Tesla’s self-driving cars, AI is becoming a part of everyday technology.

According to a MarketsandMarkets report, the artificial intelligence industry is estimated to reach USD 5.05bn by 2020, growing at an annual growth rate (CAGR) of 53.65% between 2015 and 2020.

One reason for this significant growth is the increased use of machine learning technology – a subcategory of AI where computers learn from data themselves in the advertising and media industry.

Machine Learning has a huge impact on the advertising ecosystem already. One of the best examples is Real-time Bidding (RTB), where online advertising space can be bought or sold automatically in real time.

Self-learning algorithms, typically used for running online campaigns, provide advertisers with the ability to identify the most valuable e-shoppers, and then deploy personalized ads to each customer and encourage them to take a desired action.

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Not even to mention that robots don’t sleep, which allows them to observe the market 24/7 and adjust activities to every small change out there.
Deep learning is changing the way we calculate customers’ buying potential

This is all well-known to the e-marketer already, but an exciting prospect in the near future of machine learning is that deep learning algorithms (a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers) may eventually be able to craft features that recognize the attitude, intention and the overall state of every user visiting a website, even users who haven’t clicked an ad yet. How does it work?

Conversion prediction is the estimated probability that a user will act in a desired way, and plays a crucial role in many digital advertising activities.

By using this kind of forecasting, algorithms can decide which people who visited a website have the biggest potential to buy. Consequently, it begins to build up momentum and importance for these particular users, multiplying the chance of achieving a better ROI.

It works the same way in the RTB ecosystem, however evaluation has to be incredibly fast (milliseconds) and a vast knowledge about potential customer history is needed. Thanks to the technology which uses mathematical structures inspired by the biological neurons in our brains (recurrent neural networks) it is possible to get more reliable, richer, machine-interpretable user descriptions of customer’s buying potential without any human expertise.

Typically, the history of user activities in a given ecommerce site is described as a fixed number of manually-crafted features which are believed to help predict the profitability of conversion. Such information can be more continuous (e.g. time gap between user’s last visit on the advertiser’s website and publisher’s data about the audience), or more of logical character, like an answer to the question: has the user added any product to the basket recently?

The knowledge about users and their probability to convert is, as expected, critical to planning advertising activities. Unfortunately, manually crafting each one requires substantial amounts of human expert work.

The usability of data may depend on the advertiser’s characteristic and a preset of features will not always be suitable for every retargeting campaign, so to make it work an expert should revise and partially re-explore the information for every new advertiser.

Moreover, features are snapshot at the time of the impression, so typical models ignore the data of users who have never seen any ad. This means information is obfuscated, because the vast majority of users do not convert after clicking an ad. Here is where the deep learning steps in.
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Finding patterns in a user’s decision-making

Every user takes hundreds of small steps when visiting advertiser’s website and algorithms analyze every event originating from the user’s activities.

Thanks to self-learned algorithms, we can identify every one of these footprints and find patterns in a user’s decision-making by seeing a larger pool of data, not only those connected with clicked impressions, but also with browsing particular offers, categories of interest, basket behavior, search tactics, etc.

By using deep learning, we can make a strong attempt to answer the questions: What is the predicted next event? This could be visiting the home page, browsing product listings, viewing product details, or adding product to the basket. What is the time gap to the next conversion or the category of the next product viewed?

Consequently, the consideration of the buying potential for each and every user is based almost entirely on scientific knowledge and proofed calculations rather than human intuition. This forms a significant part of a persisting problem in an approach where typical statistical models or simpler machine learning algorithms are used.
Self-learning algorithms help to analyze ad-resistant behaviours

Knowledge is power, so the saying goes. The information advertisers have is only part of the story without innovative approach – they only know about those who convert. But deep learning allows us to learn not only about buyers, but those who haven’t purchased as well.

How do the algorithms get the relevant information about conversion probability for users who haven’t showed any interest in the ad served to them?

Typical algorithms built according to classical guidelines can learn from limited, specifically prepared data. Those methods of evaluating conversion rate snapshot user-based data at the moment of an impression, but it means that usually when thinking about the Conversion Rate we take into consideration only those users who saw and clicked on the ad.

Comprehensive data analysis, which comes with deep learning, can reveal a much expanded understanding of our website visitors’ intentions, and further our perspective on which groups of people will be best to target in the particular situation. In addition, we’ll know where to find them, what their interests are, and their preferred channels of interaction.

Applying deep learning to conversion prediction used in personalized RTB activities results in more powerful campaigns. By having a more information-abundant, real-time, intelligent context-aware solution, advertisers can allocate resources at peak optimization.