A.I. + M.L.

Artificial intelligence is apparently intelligent behaviour by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

Machine Learning (ML) is the subfield of computer science that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence” and it “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.”

As per Wikipedia, ML can be broken down into the following three categories:

  1. Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
  2. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  3. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

Machine Learning

Unsupervised learning breaks down into clustering and dimensionality reduction, while clustering allows for customer segmentation, which, with AI and ML, can be done almost in real-time as models on feeds from multiple data sources can be created on-the-fly and one can almost have a ‘living segment’ that instantly evolves as customer interactions and customer behavior on social media are included as part of the customer account profile. This can lead to better target marketing as well as help build better recommendation systems.

Unsupervised learning also allows a business to look at its entire customer database and find specific personas that put people into highly specific buckets so that not just the marketing message but also the communication methods and timing can be set for maximum effect.

AI is also going to help with online advertising, taking it from a programmatic method to a cognitive one. In the past, as most internet browsers will content, it always appeared as though advertisements were following you around as you went from webpage to webpage. What happens with cognitive advertising is the system is intelligent enough to understand the reason why you left a site, how you left, whether you bought or not; basically, the AI-powered system makes an informed decision on whether ads should be served up to you or not and, if not, why not. AI will bring a whole new level of intelligence to advertising that will be noticed by the person who sees the advertisement.

Unsupervised learning can also help with fraud prevention.

On the supervised learning front, you can build problem gambling models on in-house data. Looking at customers who have opted-out because of problem with their gambling habits, AI can spot patterns that might go undetected to a human in terms of his or her gambling behavior or his or her demographics and potential psychological history. Supervised learning can also help with customer retention, identifying key patterns in purchasing, as well as marketing forecasting and uncovering customer worth.

With reinforcement learning, AI and ML can also be used to capture real-time cheating, as well as help with skills acquisition and learning tasks. 

‘Personalization’ is a word being bandied about in marketing circles these days and AI can help enormously with personalization marketing. The entire marketing process was radically changed with the introduction of marketing automation over the past few decades, but now we’re moving into AI marketing that is going to increase personalization because it can utilize granular customer details. The AI-powered marketing way is to let the system decide what is the most optimized method to reach a customer so that the marketing message gets through the enormous clutter of today's advertising world. We’re ging from sending out one-size-fits-all marketing messages, filled with one set of images for everyone to a more personalized message, i.e., an email or push notification filled with images that reflect the marketed-person’s desires and tastes, to an AI-powered marketing system that keeps up with that individual’s behaviors and social media posts, so that it can market messages at the time when the marketed individual is most receptive to them. AI is given the goal of ‘When is the most likely time that an individual will open a marketing offer?’ and it utilizes as much information as possible about an individual, and then sends out the offer when it considers it most appropriate and most likely to be opened. 

History of data collection and analytics

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