Marketing Machines — Possible or Pipedream?
We call these “requirements,” because Amazon’s tools, and every tool like it (Google has a similar tool-set for the Google Cloud Platform) requires significant programming to use. They also have a learning curve for inexperienced developers and organizations that haven’t developed competencies in structuring and transforming their data to a treatment that is readily ingested and workable with these tools.
What AWS Tools Do
AWS offers a “Machine Learning” and “Prediction” tool-set. These are two related components. Machine Learning is used to ingest large amounts of data and identify patterns in that data. A typical example is extracting promotional history and responses, and utilizing it to identify what customers are most likely to respond to a marketing promotion or offer.
When Should You Use Machine Learning and Prediction?
Generally speaking, machine learning works best when a simple “logic-based” algorithm doesn’t work, or doesn’t work consistently. Simple (or even complex) logic defines a set of rules or requirements for a decision the algorithm makes to be determined. This is also called a deterministic or rule-based approach.
If there are a lot of variables, say hundreds or more — you can’t realistically develop "brute force" rules that cover every scenario that you’d need to create value. You may determine a favorite color of a buyer with a simple rule that says if the majority of their purchases are in red, then they like red. But each purchase is influenced by more than just color… there is style, season, price and category of product, material, size and discount, to name a few. As the permutations of these combinations of variables grow more complex, a simple deterministic rule-based approach can break down, and make a prediction that doesn’t work more and more of the time.
If and when business rules begin to collide with one another and discrepancies require more rules to manage these logical collisions, Machine Learning can help sort through your data in ways rule-based algorithms cannot.
“In short, you can’t realistically create or code all the permutations and business logic cost-effectively.”
If your data set is very large and the diversity of variables you have is high, any “brute force” approach is destined to fail. Running through a set of rules on a sample of a few thousand cases may still work. Now what if you have millions of raw records? This can be possible even without a multi-million record customer file, given we may be looking at the colors and other attributes of items purchased during a period of years. Machine Learning can help make the task scaleable, and when you’re using Amazon’s computing power to do it, scale becomes the easy part.