Business intelligence software are getting smarter day by day, with all the advancement in technology. Even predictions are being generated of what our future holds in terms of possible growths. And potential risks coming our way, using algorithms, machine learning and artificial intelligence. However, if that software is predicating right then the market for predictive analysis tools and techniques is set to grow to 9.2 billion$ by next year. Whether we take this meta-prediction serious or not, that is nearly four times the market value it has right now. If anything is going to take over the technology in 2020, we all better start taking that thing serious now.

What is Predictive Analysis?

Predictive analysis is a very self-explanatory term. it describes the branch of statistics which deals with the prediction of future trends and behavior patterns by extracting and analyzing the present data available. It calculates the probabilities of future events online which may include positive or negative outcomes. Predictive analysis techniques include machine learning, data modeling, deep learning algorithms, data mining, and Al.

Knowing the future is a craze for ever human. On which even scientists are working for decades now. Therefore, they are working to develop a time machine which will help not only in revisiting their past but also see what is coming towards them. In the same manner the predictive analytics can be applied for any type of unknown reality. It could be of past, present or future, the core of predictive analysis is depended on the Explanatory variables. Which can be linked with past occurrences and then by exploiting them, the prediction can be made on unknown outcomes. Even though, it is very clear that the accuracy and usability of results will be depended on the level of data analysis happened. And the quality of assumptions being made by software.

It is not fair to intermingle phenomena of forecasting with predictive analysis. In case of predictive analysis the results are at more detail level of granularity. i.e., generating predictive scores (probabilities) for every element. Moreover, it is a technology being made to learn from the current experiences of individuals and to predict the future behavior with the intention of driving better decisions. Also, It is being introduced to achieve near-zero breakdown. In addition to prevent potential issues and prescriptive analysis for decision optimization in industrial system. And using the converted data for closed-loop product life cycle improvement is also a priority of predictive analysis.

Predictive Analysis Process:

  • Defining Project:

Identifying the data set which is going to be a part of the procedure, defining business objectives,  we define the possible scope of outcome along with the possible project outcome before starting the work.

  • Data collection:

To get the complete view of customer interaction, we collect from different sources before sending for analysis.

  • Data Analysis:

It is the process of inspecting, cleaning and remodeling the data. The main objective behind is to discover the useful information which will help in conclusion at the end.

  • Statistics:

We use standard statistical models in order to validate the assumptions and hypothesis, generated after careful analysis and after that those models test them to get the results in variables.

  • Modeling:

The ability to automatically creating accurate predictive models about the future comes in the category of modeling. There is also option available to choose the best out of the rest solution with multi-modal evaluation.

  • Deployment:

In the everyday decision-making process, predictive model deployment provides the opportunity to deploy the analytical results. which helps to get the results, reports, and outputs by automating the decisions based on the modeling.

  • Model Monitoring:

To review and keep a check that the model performance is up to the mark and it is providing the expected results, we monitor the whole set up of models really close.

  • Use of predictive analysis in real world:

Predictive analytics software uses obtainable data to identify the present trends and best practices for any company out there. Marketing departments of all organizations can use this software to identify the needs of the customers. By looking into the promising customer interaction. Manufacturing firms can take advantage of this technology and can predict the fluctuation in demand. Anything which is going to cause a change in their supply chain and the whole production details can be change to maximize the profit.

Types of predictive analysis

These disciplines involve scrupulous data analysis. We extensively use them in business for segmentation and decision-making process. But having the difference in each statistical technique make them stand differently.

  • Predictive model:

The analyzing of correlation between specific performances of a unit in a sample and one (or more known) attribute of unit comes in predictive modelling which uses predictive models. The main concern of model is to find the similarity in the different sample’s unit. Which will interlink with the unit under study of first sample, to check if that unit will exhibit any kind of specific performance. The main area covered by predictive models is marketing. In which it will help in answering questions related to the customer performance. By analyzing subtle data patterns available. Live transaction are being monitored to look for fraud detection. With progression in computing speed, individual agent modeling systems have become capable of simulating human behavior or reactions with provided stimuli.

In a predictive model, we divide samples under study into two main segments. One is with known attributes and known performance and it is called “training sessions”. While the other with known attributes but unknown performance can refer to it as “out of sample”. Explaining it in a live situation, like training session is a blood sample from the crime scene. But, now you need to predict the similarity of attributes you have in “out of sample” session.

If you have one book set of one writer, whose name and writing style is clear in front of you will count as the “training session”. Whereas, the stack of books with the unknown writer will fall under the category of “out of sample” session. And this is the stack predictive analysis will help in finding the writer off by looking at the writing style and comparing it with all the other writers.

  • Descriptive model:

We use descriptive models to classify customers or prospects into a group by quantifying relationship in data. Contrasting to the predictive model, the descriptive models focus on many customers and products at a single time. And manage to judge the relationship between them. Customers are not ranked by their likelihood of doing one particular thing as it is done in a predictive model. Product preferences and life stages can play the role of category formation for the division.

Predictive analysis tools and techniques

Predictive analysis in fact works on consumer behaviors and previous trends to access marketing and make future business strategies. Timely plans can be made based on these predictions. But in order to increase the rate of the success and profitability, new technologies are being introduced which can help brands to adapt to change according to the customer needs and their demands. Here are some new tools and techniques:

  • Sales PRISM:

It helps the organizations to collect all the data about sales and predict the potential sales lead for future. It also generates leads for sales team along with proper guidance on success rates and customers related past records to use it for making decision to bring improvement in sales. Every brand has a lot of data when it comes to the customers. We can obtain very promising outcomes By using it in right manner. Involving factors like CRM data, traffic on website, sale history of every individual product and many similar factors. It also helps to analyses external data like LinkedIn activity of brand.

  • Medalogix:

With a sudden boom in the medical field, it has become even more crucial for medical institutions to utilize all the data of patients in an intelligent manner. While keeping in view the importance of resources in a systematic manner.

Medalogix helps the institutions in making the right decisions regarding costs in a proper manner. As it gets difficult for the hospital to decide the readmission of the patient. But now it is the job of medalogix to help the hospital by carefully examining the patient records along with the details of treatment at other healthcare facilities to make the right decision.

This is a great help for healthcare professionals in planning and developing strategies. So everything from readmission to post-treatment of every patient is clear to them beforehand because of predictive analysis tools and techniques.

  • Terracotta In-genius:

Terracotta In-genius is a great analytical tool in the IT industry. It has Big memory 4.0 platform as they require a lot of speed to work with efficiency. This predictive analytical tool helps a brand by transferring the data into the RAM for speeding up the procedure of analysis.

Terracotta In-genius makes the process of data management faster, simpler and way easier. By transferring the whole data for high transaction application into a stream of distributed RAM. As it was divergent to work out of slower data storage devices.

This predictive analysis tool comes handy in cases of fraud, it helps in prediction before it happens. By looking closely into the pattern of working and it can suggest help in effective manners.

  • Viral Heat:

The roar of social media is louder than any other platform nowadays. With social media channels like Facebook, Twitter, and Instagram it is impossible for any organization to have boast without involving themselves in it. Social media now have a lot of impact on marketing strategies and the reach of the brand.

A solid engaging social media strategy will provide a limitless exposure to the brand and its reach will enhance drastically. You can examine the social behavior of the targeted audience in a matter of seconds with the help of viral heat. This predictive analysis technique will help in converting social media researches and interaction into hard data. In this manner, it will become clear for brands, how to engage with their online customers. Also, it will clear the needs of the customers. It will help them grow and empower their brand’s ineffectual fashion.

With ever-growing competition, it is very important for every brand to keep its goals and objectives crystal clear. Use the right tools to achieve those heights. Although, viral heat is not a hundred percent guaranteed formula to success. Therefore, it will show you future customer trends which clear facts and data in front of you and that will act as a great help in the decision-making process.

  • SAS text miner:

In the world of the explosion of data, it is almost impossible to get into every detail. But, SAS text miner helps you to understand and categorize your data in a presentable, simpler and easier manner. So, you can focus on just using your available data to get ahead of your competitors.

From correcting spellings and grammar to listing terms that require more focus this predictive analysis technique examines large documents for you and then categorize them according to your requirement. It is also very effective in approaching new customers, predicting and solving future issues which might happen in your organization.

  • The real-time operations analytical result:

In the world of technology where smartphones and tablets are everywhere, personal or professional life, this security predictive analysis tool is a life saver. Even offices use big data system for their data and they also open themselves to a lot of security issues. This predictive analytical technique can track down unethical practices. It can help the organizations to search specific smartphones, tablets, and desktops for unauthorized access. Also the real-time operations analytical result tool can predict the after effects of these things on organization or on working of an employee.

  • Media platform:

In the world of the internet, how to do business without engaging the online customer? E-commerce is a big part of multiple brands and organizations now and it means they have to examine the behavior of customers. In order to improve according to their demands. For big names like Amazon or eBay, it is relatively really hard to understand the habits and needs of the customers.

As Google analytics provides a good review that can help the organization to understand the pattern of their visitors but Media Platform is a step ahead. It targets the issues which make the customers to leave the website or to engage with other e-stores.

The purpose of this tool is to show the real image to brands of the factor which are making the customers leave.  And those which are encouraging them to engage themselves with the store. It will help the brand to increase or introduce new products according to the needs of the users. When a website will be created while keeping all of these main points in mind, it will help the brand to work in a productive and enhanced manner.

  • My City Way:

It is the predictive analysis technique which every brand must consider in this era of mobile phones. My City Way helps local and global organizations to create and launch their own mobile applications. These apps will not only help them generate their own referrals and build brand loyalty but will also help the brand to target and segment their customer base in an efficient fashion. Through effective combining user intent, location, relevant timing, profiling, and social graph, My City Way will help the brand in mobile marketing. As it is an integrated marketing strategy of any organization nowadays.

  • Lattice:

After analyzing customer behaviors and contact profile information, along with buying pattern, Lattice can easily find effective tools to convert your leads into sales. This predictive analysis tool can provide immense insight into sales so as to help the brand to market their products in much better ways. The pricing for the services varies on the number of sales users a number of modeling users and data volumes.

  • TIBCO software:

For the success of any business, the most common priority is to satisfy the customer. By understanding their behavior and by making changes according to that. The predictive analytical tool in TIBCO can help the organizations in understanding data and enabling them to make smarter decisions.

Brands can comprehend customer’s trends and create campaign according to what they need. Which will give brands surety of high success rate. It will involve targeting the right customer, custom messages and promotions. The sale team can use this technique for better customer management.

  • Toovio:

In order to understand the success and failure rate of every individual campaign, Toovio relies on prediction algorithm. To create custom reports based on client data and does not have to involve marketer. This predictive analysis tool helps the sales team to understand the shopping pattern of customers which will lead to improvement in digital promotions. Toovio also offers Custom segmentation based on customer usage and spending patterns. Its pricing depends on a number of profiles used and average decisions. Therefore, organizations can customize their budget according to their use.

Predictive analysis tools and techniques are the best way through which any organization can reach out to their targeted customers. Using predictive analysis tools and techniques in the right manner in the era of data explosion will prove a very successful investment. With the maximum outcome in sense of systematic and apposite help in decision making and strategies development.