Artificial Intelligence (AI) and Machine Learning (ML) have begun appearing on the cover and trending in almost all magazines, blogs and technological articles, taking off as the fashionable concept in the digital world where technological evolution is exponential. More specifically, these new concepts represent a revolution in the present and near future in advertising and marketing. AI is allowing machines to deal with tasks of a high procedural and computational load so that people can be in charge of providing the maximum added value in areas such as creativity or strategic management.
AI and ML are mainly tools that allow us to build expert systems that provide value in different facets of the digital programmatic advertising environment, from advanced audience profiling based on the analysis of physical or digital behavior, certification and classification of inventory sources in order to guarantee quality and fight against fraud, optimizing results and ROI according to systems that analyze past data and past scenarios, predicting the best possible result.
In an advertising ecosystem where audience focus predominates, and where personalization in communication begins to be key to optimizing the relevance and usefulness perceived by users, these techniques allow us to more accurately know the context and mindset of users in order to maximize the results and effectiveness of communication strategies. We’ve evolved towards an expert system that analyzes, in real time, the probability of a user being interested, buying or interacting with a brand or an advertisement based on a multitude of different data sources, among which is the location, context, demographic profile or digital and behavioral interests.
The new trends place the user as the axis of their actions, posing a new paradigm in the advertising market. More specifically, we see the great value provided by digital strategies, especially those that are user-centric in order to interact with users in all phases of the purchase funnel and via any medium.
In Sonata (DSP & DMP), thanks to our own technology, we’ve evolved our processes of analysis and data processing, in order to build audience profiles and pre-bid systems with a greater degree of specialization depending on the specific moment of the purchase process each user is in. Therefore, Sonata manages to establish cross-media and cross-device interactions, which allow us to move from a less effective mass-media communication (1 to N), to a more personalized one that impacts the user at the most appropriate time (1 to 1), achieving a more effective and relevant interaction.
A key variable of immense value, both in carrying out audience segmentation, activation and attribution at digital and offline levels, and in implementing mobile-centric strategies, is the location. To do this, Sonata has an audit and classification system for the location signals available in the advertising ecosystem called LQI (Location Quality Index). Thanks to artificial intelligence and the daily analysis of billions of pieces of data on a global scale, the expert system is able to discard all data of fraudulent origin or of low quality, taking into account not only the user's location, but also dozens of additional variables, such as advertising interactions (clicks, videos...), viewability, ad placement, origin and quality of the medium, etc.
This has allowed us to continue innovating and developing different systems in the field of Geospatial Intelligence. This new evolution towards knowledge and the development of expert systems that make intelligent decisions based on the study of different data variables in the hyperlocal environment allows us to help brands, and especially retailers, make the most optimal decisions in order to increase the ROI of their advertising or marketing investments. In real time you can know which zones or areas are the most compatible for carrying out an activation or communication depending on audience, traffic, competition analysis or the arrangement of offline media such as OOH.
Artificial Intelligence not only helps us in the engineering of intelligent systems focused on making decisions when buying or serving advertising or for the creation of more complex and precise audience profiles, but they’re key in the optimization of results and maximization of the KPIs and ROI of each strategy. Through the execution of Machine Learning models and algorithms that analyze millions of pieces of historical data, our platform evaluates and executes decisions in real time that allow us to optimize different variables at the same time, knowing and acting at each moment based on probabilities and forecasts of success or conversion. All this, in order to provide the best possible results to advertisers and provide the greatest utility and relevance to consumers who receive such content, which is increasingly adapted.
It’s important to know how to incorporate and weigh other sources of external data certified at a high quality and guaranteed level, as opposed to identifying advertising inventory of low quality or of fraudulent origin. This is a key objective in minimizing losses and optimizing results in an environment, where fraud is increasingly complex and difficult to detect. Big Data analysis, AI and process automation allow us to refine and certify the signals and decisions we make with pinpoint accuracy when activating advertising and reaching consumers in the most efficient and relevant way.
Well-made advertising is no longer a game of volume by unit price, as the advertising industry has traditionally operated. It’s an intelligent and directed offer that satisfies a need. It gives the customer the control they’ve been requesting and increases the effectiveness of the ads in a way that traditional advertising has only dreamed of so far.
Álvaro Mayol
Partner & Chief Product and Technology Officer
Source: Marketing Directo
