Predicting The Perfect Profile
Updated: Jul 9, 2019
Predicting your next business decision or the method by which you build strategy could be easier than you think. In a world filled with artificial intelligence, machine learning, natural language processing and a plethora of alternatives almost anyone can harness this potential. But how?
Predictive modeling is a tactic used by analysts and other data professionals with a goal of creating a CONCISE representation of the association between input data and target variables.
In laymen’s terms, what is your goal and what data do you have that I can use to help you get there?
In every predictive model we need at least one target variable and at least one input variable. As the scope of the model grows, so does the input requirements, respectively.
In its most basic sense, a predictive model explores the data that you have collected in a way that has been defined by the user and ‘trains’ the data to evaluate and create an understanding of the possibilities.
A general request for a predictive model would be an application to a business, creating an ideal customer profile – who is the most likely to do what? What are the attributes that create our most profitable clients or put us in our most impactful economic environment? Who is the most susceptible to our advertising campaigns? In what area will our promotions be of the most value?
How can we eliminate portfolio risk? How can we decrease workplace injury? How can we retain our employees?
It starts with data collection.
In every interaction we can begin by collecting customer attributes such as gender, age, income, location of primary residence, secondary residence, family size and any other attribute that may influence an end user’s (customer, employee or any external variable) decision to interact favorably toward your target. The more information you collect, the better off you are.
The image above is that of a metadata node from a powerful program known as SAS Enterprise Miner in which we are defining demographics as targets and inputs.
In this specific example this business has collected customer sales data including age, location, gender, amount spent, etc. and this model’s goal is to target the ideal purchaser and the ideal purchase amount. With some intricate modeling tools and a statistical approach to calculation we can estimate with (in most cases) 95% accuracy the probability that a customer will respond or purchase. With a little imagination and enough time spent collecting information about your business and industry, the applications are nearly limitless.
I’m sure that by now you’ve realized the potential applications of this method of analysis.
So, what is the long-term benefit?
In any business situation, given the same customer attributes have been collected and should be applied to our analysis we can simply insert a new data set to our existing model, [fully] automating the decision strategy and simplifying our future analysis’.
To phrase this solution simply, insight, strategy and data driven decision making.
If this sounds like a solution that would fit your business' needs or if you are unsure if this is your solution, you can contact us today using the information below.
Let us make your data Tangible.
Bryan Tamburrino | email@example.com | (203) 954-5121
Tangible Analytics Consulting, LLC