Predictive analytics explained in simple terms
No one has the ability to capture and analyze data from the future. However, there is a way to predict the future using data from the past. It’s called predictive analytics, and organizations do it every day.
In this blog post, I’ll be taking a look at predictive analytics, starting with what it is and how it can help a company function more efficiently.
What is predictive analytics?
Predictive analytics is used to make predictions about unknown future events. It uses many techniques, such as statistical algorithms, data mining, statistics, modeling, machine learning and artificial intelligence, to analyze current data and make predictions about the future. It aims to identify the likelihood of future outcomes based on the available historical data. The goal is therefore to go beyond what has happened to provide the best assessment of what will happen.
More and more companies are beginning to use predictive analytics to gain an advantage over their competitors. As economic conditions worsen, it provides a way of getting an edge. Predictive analysis has become more accessible today for smaller companies, healthcare facilities, or smaller, low-budget organizations. This is because the volume of easily available data has grown hugely, computing has become more powerful and more affordable, and the software has become simpler to use. Therefore, one doesn’t need to be a mathematician to be able to take advantage of the available technologies.
Predictive analysis is extremely useful for a number of reasons: Firstly, it can help predict fraud and other criminal activity in places from businesses to government departments. Secondly, it can help companies optimize marketing by monitoring the responses and buying trends of customers. Thirdly, it can help businesses and organizations improve their way of managing resources by predicting busy times and ensuring that stock and staff are available during those times. For instance, hospitals can predict when their busy times of the year are likely to be, and ensure there will be enough doctors and medicines available over that time.
Thus, overall efficiency can be increased for whatever organization utilizes the data effectively.
Different Kinds of Predictive Analytics
Predictive analytics can also be called predictive modeling. Basically, it is a way of matching data with predictive models and defining a likely outcome. Let’s examine three models of predictive analytics:
Predictive models are representations of the relationship between how a member of a sample performs and some of the known characteristics of the sample. The aim is to assess how likely a similar member from another sample is to behave in the same manner.
This model is used a lot in marketing. It helps identify implied patterns which indicate customers’ preferences. This model can even perform calculations at the exact time that a customer performs a transaction.
The model can be used at crime scenes to predict who the suspects may be, based on data collected at the scene. Data can even be collected without investigators having to tamper with the crime scene, by use of, for example, 3D laser scanners, which make crime scene reconstruction easier and faster. The investigators don’t even have to be at the actual scene, but can examine it from their office or home. Nothing at the actual crime scene is disturbed, and all the images and other data are stored for future reference. The scene can later be reconstructed in a courtroom as evidence.
Descriptive modeling describes events and the relationship between the factors that have caused them. It’s used by organizations to help them target their marketing and advertising attempts. In descriptive modeling, customers are grouped according to several factors, for example, their purchasing behaviour. Statistics then show where the groups are similar or different. Attention is then focused on the most active customers. Customers are actually given a “value”, based on how much they use the products or services and on their buying patterns. Descriptive modelling finds ways to take advantage of factors that drive customers to purchase.
It’s worth bearing in mind that while descriptive modelling can help a business to understand its customers, it still needs predictive modelling to help bring desired results.
The rapidly growing popularity of decision models has enjoyed much attention recently. Modeling combines huge quantities of data and complex algorithms to improve corporate performance. Decision models can be extremely useful, helping managers to make accurate predictions and guiding them through difficult decisions, unhindered by bias and human judgement alone. By using models, data can be used more objectively.
Managers need to be able to use data to make decisions. A decision-centered approach is quickly becoming the central analytics focus for most businesses. The ability to model and find solutions for complex issues allows for better decision making.
Decision models can help with such problems as to how to optimize online advertising on websites, how to build a portfolio of stocks to get maximum profit with minimum risk, or how online retailers can deliver products to customers more cheaply and quickly. It can also help product developers decide which new products to develop in the light of market trends.
When at the decision-making stage, it’s best to focus on action-oriented decisions that are repeatable. Decisions should be based solidly on the available data and be non-trivial, and should also have a measurable business impact.
A good model will have well-defined questions and possible answers. It will be able to be easily shared among team members.
While managers should use models to the maximum, they should also bear their limitations in mind. For instance, models can predict how many days of sunshine and rain a farming operation in a certain area may receive that year, but they cannot actually influence the weather. It may predict the likely amount that customers may spend on a product in a given year, but it will never be able to directly control their spending habits.
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