AI and Marketing

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Artificial Intelligence and Marketing Series:

Artificial Intelligence and Marketing Artificial intelligence (AI) is a growing field in both academia and business. Artificial Intelligence is the process of attempting to construct computer systems that can perform tasks that normally require human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. Artificial intelligence offers many benefits for marketing applications such as customer service chatbots and automated content creation systems. While artificial intelligence also poses its own set of challenges for marketers this article will walk through the various types of artificial intelligence, their applications for marketing use cases, and discuss some of the challenges with adopting AI for marketing purposes.

Artificial Intelligence: Artificial intelligence has been around since the 1950s but has only recently become mature enough to see wide adoption from businesses across industry and sectors.

Types of Artificial Intelligence

Artificial intelligence is a very broad term that usually encompasses many types of related machine learning techniques. Broadly there are two major categories of Artificial Intelligence:

Artificial narrow intelligence (ANI): Artificial narrow intelligence describes the development and implementation of systems capable of performing one task, but not generally applicable across multiple tasks. This type of Artificial Intelligence includes solutions such as content curation tools for business social media accounts.

Artificial general intelligence (AGI): Artificial General Intelligence refers to the development and implementation of technology that can perform any intellectual task that a human is capable of performing. At this point in time artificial general intelligence does not exist in practice and it remains a theoretical concept in computer science.

Types of Artificial Intelligence Algorithms

Artificial intelligence applications are built around Artificial Intelligence algorithms that attempt to replicate human-level intelligence across several different parameters including visual perception, speech recognition, decision making, and translation among many others. There are three main categories of Artificial Intelligence algorithms:

Supervised learning: In supervised learning, an algorithm is given a set of training examples, and each example is paired with its desired output value. The algorithm then learns by trying to predict the output values for the remaining examples using the outputs of previous examples as feedback.

Supervised learning: Supervised learning is used to describe machine learning applications where the system attempts to find correlations within historical datasets so as to identify patterns or make predictions about future events based on those identified patterns. For example if an e-commerce merchant sells both turboprop online training devices and ultrasonic fencing systems then a supervised Artificial Intelligence algorithm could recommend products that have been purchased together in the past as a means of maximizing or optimizing product upsell opportunities. Supervised Artificial Intelligence finds applications across many different sectors including marketing, finance, manufacturing, and others.

Unsupervised learning: Unsupervised learning algorithms are given input data without any desired output values. The algorithms then have to find structure in the data and group it accordingly.

Unsupervised learning: In contrast to supervised learning systems unsupervised Artificial Intelligence algorithms attempt to identify patterns within datasets without any historical information for context around those identified patterns. This type of Artificial Intelligence finds applications in cases where there are large datasets that would be impossible to manually review by humans due to either time constraints or data volume. For example if an e-commerce merchant has dozens or even hundreds of thousands of customer orders over the course of several years along with the corresponding customer details (name, address, product purchased, etc) then a company could use unsupervised Artificial Intelligence to cluster together customers who have similar purchasing or browsing histories so that marketing initiatives can be more precisely targeted. Unsupervised Artificial Intelligence also finds direct applications for marketers in e-commerce though most of these systems are still in the early stages of development.

Reinforcement learning: In reinforcement learning, an algorithm is given a set of goals, and it has to find a sequence of actions (or policy) that leads to the goal.

Reinforcement learning: Artificial intelligence algorithms that fall under the category of reinforcement learning are designed to mimic biological neural networks and inform decision making processes by attempting to maximize or optimize some form of desired outcome over many iterations. In practice reinforcement Artificial Intelligence is often used when there is positive feedback on actions it can take such as “if this happens increase x by y%” or “if this doesn’t happen decrease x by y%”. This type of Artificial Intelligence is used extensively in gaming applications where the agent (ie the computer player) is trying to maximize its score. However, there are many potential applications for reinforcement of Artificial Intelligence in other sectors including marketing. One such application would be to create a system that could learn which marketing campaigns are most effective over time and then make decisions accordingly about where to allocate future marketing budget resources.

While Artificial Intelligence has been around for many years, it is only recently that the term “machine learning” has started to be used in conjunction with Artificial Intelligence. Machine learning can be defined as a subset of Artificial Intelligence where the system is provided with data and then left to learn on its own from that data in order to improve its performance. This is in contrast to traditional Artificial Intelligence systems which require hand-tuning by humans in order for them to be effective. The goal of machine learning is to create systems that can learn from experience and hence become better at completing certain tasks over time without any additional human input.

Machine learning tasks are typically classified into three broad categories

The 3 main Artificial Intelligence algorithms relevant to Marketing are Artificial Neural Networks, Support Vector Machines, and Deep Learning Algorithms. These three Artificial Intelligence algorithms differ from one another in terms of their capabilities so it’s important for marketers to understand each algorithm’s strengths and weaknesses when planning how best to apply them in any given context.

Artificial Neural Networks

Artificial neural networks (ANN) is the most well-known Artificial Intelligence Algorithm. ANNs are computing systems that mimic the way the human brain works. Artificial neural networks are composed of nodes called neurons which are connected to one another via connections called synapses. Artificial neuron receives input signals from other neurons and based upon these inputs fires an output signal. Artificial neuron weights the strength of each input signal before firing their output signal. Artificial Neural Networks can be either supervised or unsupervised depending on what they’re being used for: – Classification : Artificial Neural Networks used for classification tasks such as predicting a customer’s purchase intent, propensity to engage with ads etc require that data be plotted in a multi-dimensional space where individual coordinates represent different features such as age, income level, household size, spending habits etc. Artificial Neural Networks can then process this data by assigning certain coordinates high weights and other coordinates low weights. The end result is an Artificial Neural Network that can learn how to distinguish between different types of customers. – Regression: Artificial Neural Networks used for regression tasks (such as predicting a customer’s future purchase value) also require data to be plotted in a multi-dimensional space. However, in contrast to Artificial Neural Networks used for classification, the input data points here represent real-world values rather than feature values. ANNs are then able to learn the relationships between input data and output values.

ANNs have the ability to process large amounts of data quickly and are able to identify patterns that humans would not be able to see. They are also very effective at handling noisy data. However, ANNs are not very good at handling large dimensional spaces and any Artificial Neural Networks used for regression require a lot of manual hyper-parameter tuning.

Support Vector Machines

Support Vector Machines (SVMs) are a type of machine learning algorithm used for classification and regression tasks. SVMs work by finding a so-called decision boundary between two classes of data. This decision boundary is found by maximizing the margin between the two classes of data. The margin is found by constructing a so-called support vector machine. A support vector machine is a mathematical function that maps a set of points in space into another space. In the context of SVM, the points in space represent training data while the output space represents the class to which each point belongs.

SVMs are able to handle high dimensional data spaces very well and do not require as much manual hyper-parameter tuning as ANNs. However, SVMs are not as good at dealing with noisy data as ANNs.

Deep Learning Algorithms

Deep learning algorithms are a type of machine learning algorithm that are a further development of ANNs. Deep learning algorithms are able to learn multiple layers of representations of data which allows them to identify more complex patterns than ANNs. Deep learning algorithms are also able to learn how to represent data in different ways which gives them the ability to generalize better from training data. This means that deep learning algorithms are better at handling noisy data than ANNs.

However, because deep learning algorithms require a lot of training data, they can be quite slow to train. Additionally, the number of layers a deep learning algorithm can learn is limited by the amount of training data available. Finally, deep learning algorithms typically require a lot of manual hyper-parameter tuning.

The Useful applications of AI for marketing.

Given the wide variety of Artificial Intelligence algorithms available it’s no surprise that their use is being seen more and more in marketing contexts. The four main areas where Artificial Intelligence is being applied to marketing are:

Personalization: By far the most common application of Artificial Intelligence in marketing is for personalizing content and ads based on the data collected from a user’s browsing history. Artificial Intelligence applications can be used to identify patterns in customer behaviour so as to deliver relevant product recommendations, special offers, coupons, etc to each individual consumer. As Artificial Intelligence continues to become more robust these algorithms will become more effective at learning about customers over time so that marketers will better target their outreach efforts.

Artificial Intelligence can also be used to personalize the user experience on websites and apps. This can be done by using information about a customer such as their location, purchase history etc. to present them with content that is most relevant to them.

Predictive Analytics: Artificial intelligence is finding uses across many sectors including Marketing where it’s being applied for predictive analytics purposes. Artificial intelligence systems can use historical data to try and predict what might happen in the future with respect to changes in market conditions or trends in customer buying patterns. Artificial intelligence systems are still relatively immature however they hold promise for helping marketers make decisions in real-time to optimize customer experiences. Artificial intelligence can also be applied for predictive analytics purposes when it is used to predict the likelihood of a customer making a future purchase or converting on specific marketing campaigns. Artificial Intelligence systems are able to do this by observing customers browsing activity over time and then assigning probabilities for each customer that they will make a purchase at some point in the future.

Artificial Intelligence can be used to build predictive models of customer behaviour. These models can then be used to determine things such as a customer’s propensity to purchase a product, their likelihood to spend on a product etc. This information can then be used to target customers more effectively and increase the chances of them making a purchase.

Automation: Artificial intelligence can be used to automate various marketing tasks. This can include automating the process of selecting target customers, gathering and analyzing data, creating marketing materials, and even measuring the results of marketing campaigns. The use of Artificial Intelligence for automation can help reduce the workload for marketers and allow them to focus on higher level strategic tasks. Artificial Intelligence can also be used to automate the process of optimizing marketing campaigns over time. Artificial intelligence systems are able to analyze vast amounts of experimental data and then recommend optimal settings for various input variables in order to optimize marketing outcomes such as conversions or engagement.

Sentiment analysis: Artificial Intelligence can be used to analyse customer sentiment. This can be done by identifying positive or negative keywords in customer reviews and then determining the overall sentiment of the review. This information can then be used to improve customer service and products.

Real world AI Marketing Applications

Artificial Intelligence has been successfully used in marketing for many years now. Some of the ways in which Artificial Intelligence can be used in marketing include:

Chatbots

A chatbot is a computer program designed to simulate human conversation. Chatbots are commonly used for customer service interactions where the chatbot can handle simple customer service tasks such as providing information about products or services, handling complaints, or answering questions. Digital Assistants: A digital assistant is a computer program that helps you with tasks that require more cognitive processing than simple commands such as scheduling appointments or sending messages. Digital assistants usually rely on natural language processing (NLP) to understand the user’s intent and perform the task.

Article creation

Automated content creation is a computer program that can create pieces of content such as articles, blog posts, or even social media posts without any human input. This is often done through the use of machine learning algorithms that “learn” how to write by analyzing large data sets of previously written content.

Lead Scoring

Lead scoring is the process of assigning a value to each potential customer based on their likelihood of converting into a customer. This value is often based on a customer’s behavior, such as the number of site visits or pages viewed in a given time period.

Market segmentation

Clustering is a technique used to group objects together based on their similarities. In the context of marketing, clustering can be used to segment buyers together based on their demographics (location, age, gender, etc.), interests, or buying habits. This allows businesses to better target their advertising and marketing efforts towards specific groups of people potentially attracting new customers.

Predictive Analytics

Predictive analytics is the practice of using data to predict future outcomes. Predictive analytics can be used in marketing for a number of purposes, such as predicting customer churn or customer lifetime value.

Recommendation Engines

Recommendations engines are computer programs that generate personalized product recommendations for customers based on their purchase history and/or demographics. Recommendation engines are commonly used on e-commerce websites to increase sales by making it easier for users to find items they are interested in buying.

Audience Identification

Marketing Artificial Intelligence can be used to identify customers that are most likely to purchase an offered product or service.  This is typically done through the use of machine learning and predictive analysis techniques. Artificial Intelligence can identify customers that tend to be more profitable and worth spending advertising budgets on than others.

Sales forecasting

Predictive analytics can be used in sales forecasting by analyzing past customer behavior to identify patterns that can be used to predict future customer behavior. This information can then be used to estimate how much revenue a company can expect to generate from future sales.

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