Have you heard about predictive and prescriptive analysis? What about machine learning? What are they and what are their benefits to your business?
So as not to extend too much or create a mess for you who read this post, let’s define what machine learning and predictive analytics is:
Machine Learning
Translating to the letter: machine learning is nothing more than a sub-field of artificial intelligence dedicated to the development of algorithms and techniques that allow the computer to learn, that is, allows the computer to improve its performance in some task. In artificial intelligence there are two types of reasoning – the inductive, which extracts rules and patterns from large data sets, and the deductive. Machine learning only cares about the inductive.
Some parts of machine learning are closely linked to data mining and statistics. Their research focuses on the properties of statistical methods, as well as their computational complexity.
Predictive Analysis
Predictive analytics is the use of statistical data, algorithms, and machine learning techniques to identify the probability of future results based on historical data.
And why should I do predictive analysis with machine learning?
The goal is not just to go beyond descriptive statistics and reports on what happened, but to also provide a better assessment of what might happen in the future of your business.
The end result should serve to expedite decision-making and produce new knowledge that takes the best possible actions.
Applied to business, predictive analysis is used to cross-reference current data with historical facts of the company to better understand its economic scenario, its customers, products, partners, and to even identify possible risks and opportunities. It uses a number of techniques including: data mining, statistical modeling, and machine learning to help analysts have more cohesive predictions.
In marketing, for example, most organisations today use predictive analytics to determine customer responses to purchases, as well as promote cross-selling opportunities appropriate to each purchase profile found.
Predictive analysis helps companies attract, retain, and win more profitable customers, making marketing investments smarter.
Operations: there are also companies that use predictive analysis for inventory forecasting and plant resource management. Airlines, for example, use predictive analytics to decide the best ticket prices for a flight, aiming to attract customers and also profit.
What do I Need to Get Started?
The first thing you need to start using predictive analytics in your company for is to determine a problem to solve. Key questions are important, such as: What do I want to know about the future based on the past? What do I want to predict about the future? You’re also going to want to consider what will be done with these predictions, right? What decisions will be driven by results? What measures will be taken?
Secondly, you will need data, and that means collecting data from different sources.
The lack of good data is the most common barrier in organizations that seek to employ predictive analytics, it is necessary to know how to find the data.
Let’s consider a case: You want to make predictions about what customers will buy in the future. For this you need to have good data about who the customers are, what they are buying, what may require the development of a loyalty program, credit card reviews, what they have bought in the past, and the attributes of these purchased products – (attribute-based predictions are often more accurate because it allows you to understand that “people who buy this type of product, can also buy this other type”). Demographic attributes of the client should also be considered, such as age, gender, residential location, socioeconomic situation, etc.
The wider range of different channels and contact points that you can identify to exist with your customers, the better ‘good’ data will be collected that can then be used and provide amazing results for decision making.
You’ll also need to have a follow-up from a company/expert with experience in data management to help you prepare this collected data for analysis. For the data being used for predictive analytics to prepared it is also required that the individual has clearly understood the problem or problems you want to solve.
How you set your target is essential to be able to interpret the results. Data preparation is considered one of the most time-consuming aspects of the analysis process, it is very important not to rush it.
In the next post, we will talk about prescriptive analysis. Stay tuned!
