Marketing Machines — Possible or Pipedream?
Here’s An Overview of How the 'Prediction Process' Works
So here’s an executive-level overview of how we use Machine Learning, and how it works if you build your solution on top of AWS, or Google’s developer APIs.
1. Problem Definition — Begin with The End in Mind: Here’s the step too many really don’t get right. If you’re going to venture into Machine Learning with AWS, or anywhere else, first you must define the core problems or opportunities you wish to pursue. You’ll have to do so describing that which you can observe (through your data) and an “answer” a model is expected to predict.
2. Data Preparation: Your data is going to go into a “training algorithm” where the tools will identify patterns in the data that will ultimately be used to predict the answers you're looking for on a like dataset. Look at your data before it goes in. Be curious. Do some logical testing on it. If it is not adding up to the common sense “sniff test,” odds are very good it won’t add up later, either.
3. Transformation: Input variables and the answers you seek from models, also called the “target,” are not tidy such that they can be used to train an effective, predictive model. So you have some heavy lifting to do to get the data into new variables, “transforming” it to a more prediction-friendly input. For example, you may have a set of transactions that a customer had with your brand, but you need to summarize that into a count of transactions for that customer, and an average time between purchases. These two new fields will be more predictive and useful. A command of logic and statistics helps make these calls, as does experience.
4. Implement a Learning Algorithm: Your input variables have to be fed into an algorithm that can sort and find patterns in your data — also called a “learning algorithm.” These algorithms are specialized to help establish models (statistical relationships) and evaluate the quality of the models on data that was held out from model building.