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What Does Neuromining Mean?

Neuromining is the process of applying various behavioral intelligence and machine learning (ML) techniques in order to analyze human behavior. The goal is to understand human behavior in depth to manipulate and influence it at scale.


Instead of limiting the sources of data to online user activity and survey participants, neuromining takes advantag e of neurological mapping to create synthetic data that's independent of direct user interactions.

While traditional data mining and analytics techniques focus on deriving information and insights directly from data, neuromining skips over the hypothesis stage and presents a thorough understanding of behavioral patterns in test subjects — oftentimes customers and users.

Neuromining is an advanced application of behavioral analysis and intelligence that aims to better customer relationships with brands. It allows services to provide a customizable experience for every user, not only based on their interest and previous activities but based on how they react on a deeper level.

Combining artificial intelligence (AI) — and its subset ML — with behavioral sciences enables neurominers to describe and understand predominantly human systems in a diverse pool of users. At its core, neuromining is an upgrade to the already known technique of behavioral engineering.

Neuoromining can be used to better predict which sales messages and ads would sell best to which individual person. Not to mention, the insights garnered by neuromining can provide personalized recommendations that go beyond an individual or group’s outward behavior.

Techopedia Explains Neuromining

The practice of neuromining is a data collection upgrade in response to to the devaluation of personal data that’s collected the traditional way. According to behavior tracking measures, 40% of consumers will invalidate personal data and information (giving false information or clicking on ads they aren't interested in to thwart algorithms, for instance) to make it harder to monetize by 2024.

While there’s a substantial increase in the number of internet users and the volume of data generated, privacy awareness and data protection laws are stronger than ever. Neuromining is a way to collect data and generate insights useful for product growth and marketing without committing privacy violations against their customers and clients.

The technology utilizes AI and ML to generate data synthetically, instead of organically. Done correctly using well-trained AI and ML software, synthetic data can be just as valuable and insightful as its organic counterpart but without the risk of damage in case of a data breach or leak.

Behavioral Intelligence on Synthetic Data

Synthetic data comes in all shapes and sizes, from audio and text files to media. Neuromining, however, focuses on a specific type of artificially generated data with the goal of further understanding and manipulating user behavior. Additionally, neuromining is a more instantaneous method of acquiring data that isn’t limited by an organization or sample size. It also allows neurominers to dive deeper into territories previously unexplored.

Furthermore, neuromining has the ability and potential to become a self-feeding loop, where neurominers extract data that be used to train AI and ML systems which, in turn, allow for better neuromining. Synthetic data is already being used to train ML applications that work in the real world and well outside a restricted ecosystem.

The Challenges of Neuromining

Neuromining for data is easier said than done. Basing data and insights on previous knowledge is enough to analyze past events and occurrences, but it doesn’t make for reliable insights and predictability.

While AI systems can help a system of neurominers grow and evolve well into the future of user behavior and predictions, the process requires massive amounts of computational power and honest collaboration between third-party service providers to make it happen.


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Margaret Rouse

Margaret jest nagradzaną technical writerką, nauczycielką i wykładowczynią. Jest znana z tego, że potrafi w prostych słowach pzybliżyć złożone pojęcia techniczne słuchaczom ze świata biznesu. Od dwudziestu lat jej definicje pojęć z dziedziny IT są publikowane przez Que w encyklopedii terminów technologicznych, a także cytowane w artykułach ukazujących się w New York Times, w magazynie Time, USA Today, ZDNet, a także w magazynach PC i Discovery. Margaret dołączyła do zespołu Techopedii w roku 2011. Margaret lubi pomagać znaleźć wspólny język specjalistom ze świata biznesu i IT. W swojej pracy, jak sama mówi, buduje mosty między tymi dwiema domenami, w ten…