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15 Artificial Intelligence Terms Every Marketer Should Know

AI is scientific. It can be tricky to comprehend when encountered for the first time. But we are here to help. We interviewed data scientists at Genus AI and consolidated a list of AI-related terms that are most commonly used in the data scientists’ lingo.

If you consulted Top Marketing Predictions for 2020 published by Gartner, this year is all about finding ways to understand your customers: getting customer insights, balancing between data-driven personalization and customer trust, and leveraging behavioral science and emotional intelligence when building one-to-one relationships. 

In fact, none of these are possible without deep customer data analysis. We are talking about hundreds of hours spent on your customer historical and marketing campaign data by your in-house senior data scientists. Or your marketing team can optimize their resources by employing Artificial Intelligence (AI) and custom data modeling to get even more understanding about your customers in minutes. 

Artificial Intelligence is scientific. It can be tricky to comprehend when encountered for the first time. But we are here to help. We interviewed data scientists at Genus AI and consolidated a list of AI-related terms that are most commonly used in the data scientists’ lingo – we hope these definitions will make it a little easier for you to get excited the next time you will be talking to an AI expert.

  1. Algorithm
    SEO pros are in constant struggle against search engine algorithms to get their content ranking on the first page of search results, while social media marketers try to make sure they are in line with Facebook, Twitter and Instagram algorithms that determine their posts visibility in a user’s feed. 

    In the world of Artificial Intelligence, an algorithm is a set of specific mathematical steps to solve a problem or accomplish a task. They play the central role in AI, transforming and analyzing data. They are used to make predictions from the data sets they analyze, classify customers, or finding relationships between hundreds of data points. 

    For instance, algorithms can be used to analyze an email campaign’s performance, to understand and predict the content that generates highest conversions for a set target segment. 
     
  2. Artificial Intelligence (AI)
    A branch of computer science that focuses on creating intelligent systems that can perform and react like humans. AI systems are developed to perform tasks such as decision-making, learning, seeing, talking, reasoning, or problem solving. 

    Marketers leverage AI for deep understanding of their customer behavior, data-driven campaign optimization, as well as various channel automations such as chatbots and smart campaign flows.

  3. Chatbots (bots)
    As the consumer demand to have a 24/7 digital experience is growing, AI-driven chatbots are now popping up on an increasing number of e-commerce, insurance and direct-to-consumer (D2C) websites to offer assistance in finding relevant information or providing customer support. 

    The computer programs interact directly with customers via natural language processing and usually have a narrow use case as they are programmed for a specific goal, for instance, to capture new lead data prior providing further content guidance. 

  4. Cognitive science
    Artificial Intelligence is part of cognitive science, a broader discipline that also covers psychology, philosophy, neuroscience, anthropology and linguistics. Cognitive science learns how the mind functions and, when applied to AI, how machines can simulate human behavior and language.

  5. Communication Archetypes
    Communication Archetypes cut across the boundaries of traditional segmentation based on race, gender or age, because they combine knowledge from neuroscience, psychology of language and machine learning research. 

    In practical terms, Communication Archetypes benefit marketers from a few different perspectives. Firstly, they provide enhanced understanding of people and how to communicate with them. Secondly, this knowledge makes it easier for marketers to perform to their full potential, allowing them to conduct innovative and more effective customer outreach. 

Did you know that Genus AI is the only Customer Modeling Platform that is based on an extensive use of Communication Archetypes? 
Learn more here


  1. Corpus
    Consider it the source of knowledge for neural networks. A corpus is the body of text or images that are used to train your AI algorithms. For a marketer, this usually means historical customer data, email or social post copy and ad visuals. 

  2. Customer Modeling Platform (CMP)
    CMP combines the usual features of a Customer Data Platform (CDP) and a Data Management Platform (DMP) and enhances them with the AI-enabled abilities to predict customers’ future behavior and to provide recommendations for marketing campaigns’ optimization. 

    As for 2020, Genus AI is the only true CMP provider globally. 

  3. Data efficiency
    Everything that is done to support the storage of huge amounts of data is usually referred to as data efficiency. This is extremely important for marketers as this set of techniques covers data flow management and automation, CRM integration and analytics. 

  4. Data Enrichment
    When your first party data is combined with second or third party data – that is data enrichment. The method is usually used to get a better understanding of a customer database.

    For machine learning purposes, data enrichment usually allows gathering hundreds if not thousands of data points, which is necessary for a better understanding of consumer behavior and a more precise predictions of their future actions.

  5. Data mining
    When it comes to large data sets and hundreds or even thousands of data points, it is usually extremely difficult and time consuming to analyze them manually. Data mining, a computer process of discovering patterns, is then invoked. 

    In e-commerce, data mining is quite often behind upselling and new product recommendations (remember all those “you might also be interested in..” suggestions online?)

  6. Deep learning
    Often referred to as “neural networks” by data scientists, deep learning is a method of processing vast amounts of data. It is based on the way the human brain interprets information, and consists of layers of nodes which receive data, extract the most relevant information and send the data along to the next node. 

  7. Machine Learning
    You must have heard about computers being able to learn over time with experience rather than additional programming. Such AI-driven programs automatically look for trends in vast data sets, but they often focus only on those patterns that serve the predefined goal.

    In practical terms, machine learning (ML) can be used for any marketing purpose: optimizing click through rates (CTR), boosting conversions, reengaging idle customers, managing churn, optimizing content, sharpening target messaging and visuals, and many more.

Did you know that Genus AI runs the world’s first, AutoML-enabled Customer Modeling Platform? Learn more about it here.


  1. Model
    In simple terms, a model is a mathematical representation of relationships in a data set that enables making predictions. The more complex is your data, the more complicated the predictive model is going to be. 

    Predictive models are integral part of marketing enabled by AI and ML. They provide the link between the historical data and future predictions by layering a pre-defined goal over them. 

  2. Supervised vs. unsupervised learning
    These are two approaches Machine Learning can take. 

    In supervised learning, humans provide specific data sets and a clear outcome to work towards and supervise the whole process.

    In unsupervised learning, machine learning is left to find patterns and draw conclusions on its own. 

    Quite often, supervised learning is invoked in the development of a ML platform and later, when it goes through multiple quality checks or starts requiring tremendous scale, can be replaced by unsupervised learning.

  3. Training data
    ML uses training data as the initially data given to the program to learn and identify patterns. Once done, additional data sets may be given to the program to check the accuracy of the patterns. 

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