The term predictive analytics is used to describe business applications that use data mining, machine learning, or statistical techniques to determine what factors will result in in an increase (or decrease) of some outcome.

Predictive analytics can help predict things such as: when a customer will drop off loyalty rewards programs, whether an employee should be given a raise, or if a product line needs to be discontinued.

By incorporating predictive analytics into your company’s operations, you can save money by preventing poor performance before it happens! And with all the data available through technology these days, performing predictive analyses has never been easier.

There are many types of predictive analytics, but one of the most common uses of this technique is called “machine learning.” This is because the algorithm learns from *past examples like experiments* and findings.

With **big companies using predictive analysis tools every day**, there are now even software packages designed to make it easy for anyone to perform advanced analytics. Some of the more well-known ones include: SAS, R, Stata, SPSS, QTP, and Excel.

Here at DataScienceDome, we believe in educating others about predictive analytics so they can apply these concepts in their own lives and careers! That’s why we created our free course — Beginner’s Guide To Predictive Analytics Using Tableau. In this course, you will learn how to perform basic predictive modeling with Tableau.

## Why is predictive analytics important?

The *term predictive analytics actually comes* from statistics, but it has become increasingly popular in business. Predictive analysis looks at past data to determine what factors will lead to what outcomes. For example, if you have seen that people who work more are paid better, then using this information you can predict how much money someone will make by looking at their paychecks.

Predictive analytics applies this concept to future situations, which is why it’s considered powerful. Businesses use predictive analytics to identify potential problems before they occur and take action to prevent or mitigate them.

It *also helps find new ways* to improve services and products for customers. By analyzing trends and patterns, companies can figure out what works and what doesn’t, paving the way for changes and innovations.

Businesses that apply predictive analytics to monitor and manage their performance are referred to as predictive organizations. Some of the most well-known brands in the world — including Amazon, Google, and Walmart — utilize some form of predictive analytics.

There are many types of predictive analyses, with the two major categories being descriptive and prescriptive. Descriptive analyses look into past behavior to describe what has happened and why (descriptive analytics) while prescriptive analyzes try to determine what actions need to be taken to achieve a specific outcome (predictable analytics).

Descriptive and predictive analytics both play an integral role in predictive organization strategies.

## What are the different types of predictive analytics?

There are three main categories of predictive analytics in business- you have regression, classification, and clustering. Regression analysis predicts whether or not an independent variable will lead to a dependent outcome, while classification analyzes if there is a pattern between two variables. Clustering groups similar items together to identify characteristics or patterns.

Other **less common forms include dimensionality reduction** which looks at only a subset of dimensions of data to create new ones, and outlier detection which studies outliers (or unusual values) in your dataset to determine what may be happening around these numbers.

Using all three methods can *help predict future outcomes*. By predicting trends earlier than normal, you give yourself time to take action! Systems that use predictive analytics do so using statistical algorithms and models designed for specific purposes.

There are many software packages that offer predictive analytics features, some more advanced than others. Some examples of vendors with predictive analytics tools include IBM, SAS, and Google. You should do some research and see what each one has to offer before choosing one over the other.

## What software is used for predictive analytics?

There are several great tools that can be used to perform predictive analytics in business. Some of the most well-known include SAS, Microsoft Excel, Google Spreadsheets, and IBM SPSS.

All four of these applications have advanced features that allow you to perform more powerful analyses. For example, with SAS you can create models using their Statistical Programming Language (SPL). You get all of the *statistics functions built right* into the tool which makes it easy to run basic statistical tests as well as creating regression and classification algorithms.

With Excel you can use many of its functions such as VBA (Visual Basic for Applications) or VSTO (Office Extension Tools) to write your own formulas and add them onto existing ones. This gives you much greater control over the data and how you process it.

Google spreadsheets offer similar functionality to excel by *offering either prebuilt functions* or you can upload your own! One nice feature about this one is the ability to **collaborate easily since everything** is online.

And lastly, SPSS has an intuitive interface like some other software mentioned here but also offers users the option to make graphs and charts directly within the program instead of having to export anything first.

What is important to note about all of these programs is that they are not free. They cost money to develop and license. However, they do offer trial versions so you can check out whether or not they work for you before buying a subscription.

## How can I start performing predictive analytics?

Starting with predictive analytics is easy! There are many ways to get into the swing of it, from doing simple regressions to using more advanced techniques. One very popular way to **perform predictive analytics** is via Statistical Learning or Machine Learning.

By breaking down predictive modeling into its component parts, you can *use different strategies* for each part to create your model. This is what experts refer to as “divide-and-conquer” approach to learning.

Here we will take a look at one such strategy: Linear Regression.

## What are the different stages of predictive analytics?

Once you have gathered your data, you can do several things with it. The first is to perform descriptive analysis, which just looks at numbers without any context. For example, you could look at how many people in your audience does something such as subscribe to a magazine or go to a movie theater.

You could also do correlation analysis, which studies whether there is an association between two variables. For instance, if there were no correlations found between monthly income and happiness, then we could conclude that having more **money cannot make** you happy.

Then, you can do regression analysis, where you determine what affects another variable. For instance, if students’ grade point average was shown to affect their income later in life, then you could conclude that improving one’s education makes you happier.

Next, you can **use statistical models** in predictive analytics. These **models apply mathematics** to predict outcomes. For instance, you could create a model to determine if someone will fail a test by looking at past tests they took and failures they got.

## What are the different metrics that are used in predictive analytics?

The most widely known metric for predictive analytics is accuracy or AUROC, but there are several other performance metrics. These include precision, recall, F1 score, specificity, and loss (or error) function.

Precision refers to how well an *algorithm predicts positive cases*. Recall means how well it predicts positives relative to all possible scenarios. An easy way to think of this is knowing whether you gave a test result as true or false and then calculating the ratio of **true results versus total tests**.

The F1-score is a combination of both precision and recall which weights them equally compared to each other. This is typically not included in predictive algorithms because it is considered too complicated to calculate.

Specificity and loss refer to what the algorithm does when it makes a prediction. If a *model always guesses something* is negative even if it’s uncertain, then it has low sensitivity which is the opposite of high specificity. Conversely, if it **never predicts anything except** for sure negatives, then it will have very poor accuracy since it cannot make any predictions at all!

A loss function determines how much difference there is between the predicted value and the actual value. For example, if your model predicts that something is likely to happen, it can be measured against whether it actually happened or was confirmed to occur. Therefore, its loss would be zero, but unfortunately it isn’t really helpful yet.

## What challenges are associated with predictive analytics?

A significant challenge of using predictive models in business is choosing which model to use and how to implement it. There are many different types of predictive modeling that can be done, and each one has its strengths and weaknesses depending on what you’re trying to predict and who will see the results.

The best way to determine which type of prediction makes the most sense for your situation is by thinking about your goals and then determining which predictive algorithms make the most intuitive sense.

For example, if you wanted to know whether or not someone will fulfill their job responsibilities, then we could look at past behavior as a predictor. If they have been consistently good before, there is a better chance they will continue being consistent now than if they had never done anything before.

Another common algorithm used in predictive analysis is regression. This predicts a dependent variable (in this case, whether or not someone will perform their job duties) based on an independent variable(s). For instance, if someone was given a responsibility last month, they might get credit for just doing their normal job or getting extra busy so that they deserve a promotion.

There are **also classification models** where **something either meets** or does not meet a criteria-used for things like whether or not someone will receive a pay rise or bonus. And finally, **time series forecasting models** such as trendline predictions, which try to determine when an event will happen.

## What should I look for in a predictive analytics company?

Choosing a tool that **offers good quality predictions** is only half of the equation, you also need to check out their customer service. It’s very important that you feel comfortable letting them handle your data, and that there are clear processes in place for doing so.

Tools with *poor customer service may offer fancy features* but they won’t let you give them access to sensitive information unless they have proof that you are who you say you are. This could be through having you upload documents or *using security codes* to verify accounts, but even just requesting their terms and conditions can confirm this.

There are many free tools that can do some predictive analysis, but it’s best to weigh up how reliable they are before paying for a paid-for package. Some *people make overblown claims* about what their software can do, which isn’t necessarily true.