By Joshua Snape | 11/27/2017 7:20 AM
What Is Predictive Analytics?
Predictive analytics is integral to many different disciplines and sectors, such as in medical research, computer network security, national security and marketing.
In the context of marketing, the basic premise of predictive customer analytics is that intent is determined by behavior. If you analyze the behavior of your customers then you will discover their intent, or what influences their buying decisions. The ultimate goal is to increase customer loyalty and sales.
How does it Work?
There are three distinct but interrelated parts, Capture, Predict, and Act.
Capture what? Data. This data can be information stored in spreadsheets or other software designed for data analysis. The definition of “big data” is – extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations – especially relating to human behavior and interactions. This data can come from many sources, such as past customer transactions. It should be noted that a large amount of data is not essential to the process. In fact, often only a relatively small data sample is used in creating prediction models.
Data mining is about finding new information in existing data. Depending on the size of the data involved computers will use “algorithms,” – (a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer). These algorithms are created to provide the statistical likelihood of an outcome, such as whether a market segment will likely buy a certain product or not.
A marketing strategy can be created and implemented in order to act on the insight gained from the predicting stage.
Clever Algorithms are no Substitute for Human Intelligence
While the above is only a very basic overview, the intent of this article is to minimize the ambiguity behind what is done on the non-inspirational side of the predictive customer analytics equation. By far the most interesting part of the process is the human analysis which requires the qualities of insight, intuition, and inspiration in order to take this data and see the opportunity behind it.
Even though much of the process can be automated or delegated to number crunchers, the vital work of the human analyst is far from obsolete.
As Leo Breiman and Adele Cutler (creators of the well-known ‘random forests’ algorithm) state, “the cleverest algorithms are no substitute for human intelligence and knowledge of the data in the problem.”
The point is this; don’t allow the ambiguity and jargon to prevent you from getting involved with the predictive customer analytics process. Strategis Consulting can help you make sense of it all. Call us (1-877-420-4120) or connect with us here.
Joshua Snape is an accomplished marketing consultant in both Britain and Canada. The range of his marketing consulting includes blogging for B2B and B2C audiences. Joshua currently provides freelance blogging for Strategis Consulting. Connect with Joshua at email@example.com