These days there is a lot of buzz in the marketing community about the use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing.
In this post, I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts.
What is Artificial Intelligence?
There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: “Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.”
So, in a nutshell, AI refers to a machine, which can learn and become intelligent like humans.
AI is an umbrella term that includes algorithms, concepts, tools, technologies, etc. that perform these complex human-like tasks.
One of such and widely used concept in AI is Machine Learning. Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI includes much more than just Machine Learning (ML).
What is Machine Learning (ML)?
Machine learning is the practice of using statistics to parse a large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from Nvidia).
Machine learning builds a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning)
There are three major types of learning used to train these models – Supervised learning, Unsupervised Learning, and Reinforcement Learning.
In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs.
Example: Predict the churn propensity of a customer.
You can provide training data that contains customers’ purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, the model learns what purchase and behavior data will cause all the customers to be labeled as “Churn Risk” or “Not Churn Risk”.
In unsupervised learning, the algorithm builds a mathematical model from a set of data that contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning)
Example: Uncover customer segments.
Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior, etc.. This can then be used to drive better customer engagement and better marketing performance.
In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn the desired behavior that maximizes the total reward.
Example: Provide recommended products to customers.
Reinforcement learning can be used to develop an online product recommendation engine.
Other Terms that you should be aware of
Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data, etc.
Free form data such as word documents, call scripts, pdf, images, etc. Anything that is not structured is classified as Unstructured data.
Marketing Uses of AI
There are several ways AI can be used in Marketing. Here are some examples, this is not a complete list. I will add more articles in the future to cover several use cases.
- Customer Segmentation
- Ad budget allocation across channels or by channel
- Content creation
- Chatbots – Which understands humans’ questions and then responds with an appropriate response.
- Churn Prediction/Customer Retention
- Product recommendation engine
Hopefully, this article provides some clarity to the confusion around AI in Marketing.
Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges? Let’s talk.
Article posted from http://webanalysis.blogspot.com/2019/10/artificial-intelligence-ai-for.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+WebAnalyticsBehavioralTargetingAndOptimizationByAnilBatra+%28Web+Analytics%2C+Behavioral+Targeting+and+Optimization+by+Anil+Batra%29&utm_content=FeedBurner