What is Predictive Modelling?
Predictive modelling is the method of making, testing and authenticating a model to best predict the likelihood of a conclusion. Several modelling procedures from artificial intelligence, machine learning and statistics are present in predictive analytics software solutions. Models can utilise single or more classifiers to decide the probability of a set of data related to another set.
The different models available for predictive analytics software enables the system to develop new data information and predictive models. Each model has its own strengths and weakness and is best suited for various types of problems.
Predictive analysis and models are characteristically used to predict future probabilities. Predictive models in a business context are used to analyse historical facts and current data to better comprehend customer habits, partners and products and to classify possible risks and prospects for a company.
It practices many procedures, including statistical modelling; data mining and machine learning to aid analysts make better future business predictions.
The following points describe more about predictive modelling:
- Predictive modelling is at the heart of business decision making
- Building decision models more than science is an art
- Creating an ideal decision model demands:
- Good understanding of functional business areas
- Knowledge of conventional and in-trend business practices and research
- Logical skillset
- It is always recommended to start simple and keep on adding to the models as required.
Logic driven models are created on the basis of inferences and postulations which the sample space and existing conditions provide. Creating logical models require solid understanding of business functional areas, logical skills to evaluate the propositions better and knowledge of business practices and research.
To understand better, let us take an example of a customer who visits a restaurant around six times in a year and spends around ₹5000 per visit. The restaurant gets around 40% margin on per visit billing amount. The annual gross profit on that customer turns out to be 5000 × 6 × 0.40 = ₹12000. 30% of the customers do not return each year, while 70% do return to provide more business to the restaurant.
Assuming the average lifetime of a customer (time for which a consumer remains a customer) W 1/.3 = 3.33 years. So, the average gross profit for a typical customer turns out to be 12000 × 3.33 = ₹39,960.
Armed with all the above details, we can logically arrive at a conclusion and can derive the following model for the above problem statement:
Economic Value of each Customer (V) = (R × F × M)/D
R = Revenue generated per customer
F = Frequency of visits per year
M = Profit margin
D = Defection rate (Non-returning customers each year)
The main aim of data-driven model concept is to find links between the state system variables (input and output) without clear knowledge of the physical attributes and behaviour of the system. The data driven predictive modelling derives the modelling method based on the set of existing data and entails a predictive methodology to forecast the future outcomes.
It is data-driven only when there is no clear knowledge of the relationships among variables/system, though there is lot of data. Here, you are simply predicting the outcomes based on the data. The model is not based on hand-picked variables, but may contain unobserved, hidden combination of variables.