Artificial Neural Networks For Marketing
Artificial Neural Networks are progressive learning systems modeled after the human brain that continuously improve their function over time. Artificial neural networks can be effective in gathering and extracting the relevant information from big data, identify valuable trends, relationships and connections between the data, and then rely on the past outcomes and behaviors to help identify and implement the best marketing tactics and strategies.
The human brain consists of 100 billion cells called neurons. The neurons are connected together by synapses. If sufficient synaptic inputs to a neuron fire, that neuron will also fire. This process is called “thinking”.
Artificial neural networks are a form of computer program modeled after the way the human brain and nervous system works. It is not necessary to model the biological complexity of the human brain at a molecular level, just its higher level rules.
Common practical uses of Neural Networks are for character recognition, image classification, speech recognition, facial recognition, etc. They are also used for Predictive Analytics. Read our article on HOW LEADING TECHNOLOGY COMPANIES ARE USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING.
Artificial Neural Networks(ANN) are a series of interconnected artificial processing neurons functioning in unison to achieve desirable outcomes.
Artificial neurons are elementary units in an artificial neural network. Each artificial neuron receives one or more inputs and sums them to produce an output. Think of artificial neurons as simple storage containers.
Artificial Neural Networks are comprised of three main parts: the input layer, the hidden layer, and the output layer. Note that you can have n hidden layers. The term “deep” learning implies multiple hidden layers. Each layer is a one dimensional array.
During processing each input is separately weighted, and the sum is passed through a non-linear function known as an activation function or transfer function to the next set of artificial neurons.
Using trial and error learning methods neural networks detect patterns existing within a data set ignoring data that is not significant, while emphasizing the data which is most influential.
From a marketing perspective, neural networks are a form of software tool used to assist in decision making. Neural networks are effective in gathering and extracting information from large data sources and identify the cause and effect within data. These neural nets through the process of learning, identify relationships and connections between databases. Once knowledge has been accumulated, neural networks can be relied on to provide generalizations and can apply past knowledge and learning to a variety of situations.
Neural networks help fulfill the role of marketing companies through effectively aiding in market segmentation and measurement of performance while reducing costs and improving accuracy. Due to their learning ability, flexibility, adaption and knowledge discovery, neural networks offer many advantages over traditional models. Neural networks can be used to assist in pattern classification, forecasting and marketing analysis.
Keras is an open source neural network library written in Python. Keras was conceived to be an interface rather than a standalone machine-learning framework. It offers a higher-level, more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. It is capable of running on top of 
TensorFlow was originally developed by the Google Brain Team within Google’s Machine Intelligence research organization for machine learning and deep neural networks research.
Artificial Intelligence and Machine Learning are shaping many of the products and services you interact with every day. In future blog posts I will be discussing how Artificial Intelligence, Machine Learning, Neural Networks, and Predictive Analytics are being used by Marketers to achieve competitive advantage.
According to Pew Research Facebook users continue to reduce the amount of time they are spending on the platform. Just over half of Facebook users ages 18 and older (54%) say they have adjusted their privacy settings in the past 12 months, according to a new Pew Research Center survey. Around four-in-ten (42%) say they have taken a break from checking the platform for a period of several weeks or more, while around a quarter (26%) say they have deleted the Facebook app from their cellphone. All told, some 74% of Facebook users say they have taken at least one of these three actions in the past year.
There are, however, age differences in the share of Facebook users who have recently taken some of these actions. Most notably, 44% of younger users (those ages 18 to 29) say they have deleted the Facebook app from their phone in the past year, nearly four times the share of users ages 65 and older (12%) who have done so. Similarly, older users are much less likely to say they have adjusted their Facebook privacy settings in the past 12 months: Only a third of Facebook users 65 and older have done this, compared with 64% of younger users. In earlier research, Pew Research Center has found that a larger share of younger than older adults use Facebook. Still, similar shares of older and younger users have taken a break from Facebook for a period of several weeks or more.
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