How enterprises are using Predictive Analytics to transform historical data into future insights

17 min readAug 16, 2019

Over time, the role of data is significantly shifting in the modern-day business. Irrespective of their size and nature, organizations are looking to optimize the way they are gathering and storing huge volumes of data. The more data you have, the more insights you can derive. The more insights you can derive, the more analysis you can do. Analyzing large volumes of information from varied sources can help organizations predict more accurate future and enhance business decision-making skills. And how to analyze such huge varieties of data? By embracing super advanced technologies. Among all those technologies, predictive analytics with the help of Artificial Intelligence (AI) is the most significant one.

What is Predictive Analytics and why it matters?

Generally, the term predictive analytics is used to mean predictive modeling, “scoring” data with predictive models, and forecasting. Predictive analytics utilises historical data to predict future results. In a border sense, historical data is used to build a mathematical model which carries important trends. On top of that a predictive model is being used to predict future trends and suggest actions to take for best possible outcomes. In recent years, predictive analytics has been able to catch a lot of eyeballs due to significant advancements in its supporting technologies such as big data and machine learning. Due to these advancements, predictive analytics is no more limited as a sole domain of mathematics and has been completely wide-spread. Organizations and business analytics are exploring various predictive analytics techniques to gain more insights.

“Predictive analytics is a kind of advanced analytics that leverage historical as well as new data to forecast activity, behaviour and trends. This includes applying statistical analysis techniques, analytical queries and machine learning algorithms to data sets in order to create predictive models that place numerical values or scores.”

In a more comprehensive way, Predictive analytics can be defined as something which is related to performing predictive scores for an organizational element. The technology of predictive analytics is different from forecasting in the perspective of utilizing data. Though predictive analytics is here for quite a long time now, it has only started getting some serious clout in recent times.

If you think why we need predictive analytics, we have actually plenty of reasons. The most important point is, in today’s extremely high competitive business era, businesses can’t afford to rely on intuition or gut feeling to take business decisions. You need data-driven insights to get more accurate results in the field of customer buying trends, market conditions etc. Predictive analytics has been used in a variety of industries for a whole host of use cases.

Where to use Predictive Analytics?

Predictive analytics has a lot of perspectives. Organizations are using it to obtain relevant and important insights to maximize their top line. The process of deriving value from big data, utilising algorithms on large data sets and using various tools can help you get optimized real-time insights. There are plenty of data sources such as the transaction database, log files, images, audios, videos, sensor data etc. that requires to be analyzed and predictive models can be built on it. Many organizations are deploying various machine learning techniques such as linear, logistic and non-linear regressions, neural networks, decision trees etc. to find the right patterns. Predictive analytics can be used in many verticals to explore new opportunities in order to maximize organizational growth and revenues. Some of the areas where predictive analytics are being used are;

Marketing Campaigns: Predictive analytics is being used to drive data-driven customized marketing campaigns, understanding customer behaviour, customer approach, utilising the right strategy to create future marketing campaigns, measuring key performance indicators and maximizing campaign ROI.

Enhancing Operational Efficiency: Many organizations today leveraging predictive analytics to streamline various business operations such as managing the demand-supply, logistics, inventory, resource, cross-selling etc.

Risk Management: Predictive analytics application for identifying more about the customer’s reluctance to purchasing a product, the various factors which prevent a customer from making the purchase decisions and discovering ways for how to reduce the risks involved.

Fraud Detection: Using various analytical tools to find out more about the pattern discovery of the fraud transaction in financial domains, precluding the criminal actions, applying behavioral analytics to preclude fraud, investigating about zero-day vulnerabilities and eliminating the risks of advanced frauds.

Customer Relationship Management (CRM): To retain most customers and get them to purchase more from you, regression analysis and clustering techniques are being used in CRM systems which can allow you create customer groups based on their buying pattern, demographics, gender, age etc. This will help you optimize your customer life cycle, enabling you to launch more targeted & effective marketing efforts.

Building Recommendation Engines: Personalized recommendations are being used by various industries such as e-commerce, food tech, online cab and others to boost their user loyalty and engagement. Collaborative filtering is a predictive analytics technique which uses past behaviour to create recommendations. While content-based filtering recommends new items to customers based on their past purchase history.

Improving Employee Retention: HR departments of several Fortune 500 companies are using predictive analytics to improve their hiring and employee management policies. Data from the HR database can be used to optimize the hiring process and identify the best talent from the industry. Performance data and employee personality profiles can be evaluated to identify when an employee is likely to leave so that proactive efforts can be made to retain best talent pools.

Choosing the right technique

Predictive analytics potentially contains multiple algorithms that employ various analytics methods. These algorithms are dedicated to define dependencies among several variables in the data and to determine where there could be a high probability of reliance in the predictions which can be obtained from the dependencies. The basic idea behind predictive analytics is to “train” your model on historical data and apply this model to future data.

How does predictive analytics work?

Step 1 — Select the target variable. (for example, predicting stock prices.)

Step 2 — Get your historical data set. (Use “collect-everything-you-can” principle. Involves data extraction from multiple sources and transformation.[cleaning,arranging].)

Step 3 — Split your data. (Split the historical data into 2 sets: 1.Training set: the dataset to teach the model. 2.Test set: the dataset to validate the model before using it on real life future data.)

Step 4 — Pick the right prediction model and the right input values, Validate and Implement.

Sub-step 4.1: The historical data (Training set) is fed into various mathematical models that consider key trends and patterns in the data.
Sub-step 4.2: Now use the other data (Test set) to validate these models.
Sub-step 4.3: The best fitting model (avoid overfitting) is then applied to current data to predict what will happen next.

This step is the heart of Predictive Analytics. Creating the right model with the right predictors will take most of your time and energy. It needs as much experience as creativity. And there is never one exact or best solution. It’s an iterative task and you need to optimize your prediction model over and over. Though there are various algorithms that can be used, there are a few smaller set of predictive analytics techniques which are typically being applied. These are;

Regression

Regression technique is devised to identify meaningful relationships between two or more data variables, particularly considering the connections among various dependent data variable and other factors that may or may not affect it. The information allows data analysts to predict future developments related to the dependent data variable based on what results with related elements. This technique is used for finding the causal effect relationship between the variables. For example, relationship between rash driving and number of road accidents by a driver is best studied through regression.

In Regression technique, we fit a curve / line to the data points, in such a manner that the differences between the distances of data points from the curve or line is minimized. Depending on the situation, there are an extensive variety of regression models that can be applied while performing predictive analytics. Some of them are;

Regression is typically being used in price optimization, especially determining the best price for a product based on how other products have sold. It is being used by stock market analysts to determine how factors like the interest rate will affect stock values. Regression models are also used in predicting what will be the demand for specific products in various seasons and how the supply chain can be optimized to match demand.

Before visiting the next technique, we will look at this simple way of using Regression Analysis with Excel Sheet.

Correlation

Correlation analysis is being used to identify relationships and dependencies among different data variables to predict how they are going to affect each other in future. Determining whether there is any correlation or no correlation between a set of data variables can be beneficial to target predictive analytics projects. Correlations can be positive or negative. A positive correlation directs the degree to which data variables increase or decrease in parallel, whereas a negative correlation directs the degree to which one data variable increases as the other decreases.

Correlation coefficients are used in statistics to measure how strong a relationship is between two variables. There are several types of correlation coefficient: Pearson’s correlation (also called Pearson’s R) is a correlation coefficient commonly used in linear regression. For example in predicting returns in the stock market for individual stocks, the correlation coefficient may have value in predicting the extent to which two stocks move in relation to each other. Here the correlation coefficient is a statistical measurement of the relationship between how two stocks move in tandem with each other, as well as of the strength of that relationship and investors often use this correlation coefficient to diversify assets in the construction of portfolios.

In a nutshell, correlation analysis is a statistical model of the degree to which changes in the value of one data variable foretell changes to the value of another data variable. When a fluctuation of one data variable predicts a similar fluctuation in another variable, there is a tendency to consider that means that the change in one results in a change in the other.

Classification

Using classification technique, analysts separate various entities in a data set in related groups by mapping them into predefined categories based on relevant characteristics. The classification model can be used to classify new records as well as to perform predictive analytics against the data for the selected subgroups. Thus, Regression answers “how much/many?” and classification answers “which one?” Some classification techniques are:

Decision trees: A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. A decision tree is the building block of a random forest and is an intuitive model.
Random Forests: Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees’ habit of overfitting to their training set.
Naive Bayes: Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.

Segmentation

Segmentation technique is being used to analyze a huge collection of entity data like a customer database, report etc. and segregate it into smaller groups. All the entities which are collected into the same subgroup are defined to be similar to each other on the designated characteristics, which allows predicting future behaviour.

Clustering

Clustering includes grouping data with similar characteristics into “clusters”. During clustering method, the most relevant factors within a dataset are separated. The process outlines the relationships between data which can be applied to predict the status of the future data. Clustering has the power of allowing data to define the clusters and therefore the defining characteristics of the class rather than using preset classes. It is useful when you don’t have any knowledge about the data in advance. Organizations use a clustering method for customer segmentation. It sees the characteristics which actually separate classes of customers from each other rather than depending on human-generated classes like demographics, age, gender etc.

To get more knowledge about Clustering in Predictive Analysis, I suggest looking at the following blogs: 1. Basics of Data Clusters in Predictive Analysis and 2. How to Use K-means Cluster Algorithms in Predictive Analysis

So far we can clearly see that the Regression model works along with Correlation, Classification, Segmentation and Clustering models for achieving predictive analytics. These models are applied based on the requirements to predict outcomes using Regression and Classification technique as main predictive models.

Time Series Models

Time series models are used to predict and forecast the future behaviour of data variables. These models are responsible for the data points used over time and have an internal structure like auto-correlation, trend or seasonal variation.

Basic regression techniques can’t be used to time series data and this model has been developed to decompose the trend, seasonal and cyclical component of the series. Time series is a collection of data points collected at constant time intervals. These are analyzed to determine the long term trend so as to forecast the future or perform some other form of analysis. But what makes a Time series different from say a regular regression problem? There are two things:

It is time dependent. So the basic assumption of a linear regression model that the observations are independent doesn’t hold in this case.
Along with an increasing or decreasing trend, most Time series have some form of seasonality trends, i.e. variations specific to a particular time frame. For example, if you see the sales of a woolen jacket over time, you will invariably find higher sales in winter seasons.

Dynamic path of a variable can enhance forecasts since the predictable component of the series can be extended into the future. Two generally used forms of time series models are Auto-regressive Models (AR) & Moving-average (MA) Models. Let us hands on with

Machine Learning

We can not do much prediction with basic regression and time series techniques only. Machine learning, which is a part of artificial intelligence, holds a number of high-level statistical methods for regression and classification. It is being applied in various fields such as medical diagnostics, financial fraud detection, speech recognition and stock market prediction etc. In a few cases, it is enough to directly predict the dependent data variable without focusing on the underlying relationships between variables. In other cases, the underlying relationships can be very complicated and the mathematical form of the dependencies are unknown. For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events. There are varied types of machine learning techniques which are being used in predictive analytics. Few of them are Neural networks, Support Vector Regression of SVM , k-nearest neighbours, RNN, LSTM, Multilayer Perceptron (MLP), Radial basis functions , Geospatial predictive modeling etc.

Let us discover how to develop neural network models for time series prediction in Python using the Keras deep learning library before moving to chaos fractal model predictions with neural networks.

Fractal Time Series Models

Mathematician Benoit B. Mandelbrot often is credited with introducing the notion of a fractional, or fractal, dimension in his 1967 paper. Fractals are infinitely complex patterns that are self-similar across different scales. Over the past two decades, machine learning methods for time series have been proposed and developed, which are used for many tasks of time series analysis, including classification. Many complex technical and information systems have a fractal (self-similar) structure, and their dynamics is represented by time series with fractal properties. For such systems,there are problems of recognition and classification of fractal series. Most often, they are solved by evaluation and analysis of self-similar properties. In recent years, machine learning methods became popular for analyzing and classifying such fractal time series.

Fractal time series substantially differs from conventional one in its statistic properties. Many methods exist for quantifying the fractal characteristics of a structure via a fractal dimension. The fractal dimension of a time series shows how turbulent the time series is and also measures the degree t to which the time series is scale-invariant. Fractal analysis is proposed as a concept to establish the degree of persistence and self-similarity within the time series data. Advanced neural techniques are required to model the non-linearity and complex behavior within the time series data to predict the outcome.

Elliott Wave

Elliot wave analytics is another form of predictive analytics techniques which is being widely used by traders to predict the cyclical nature of the trading market (stock market). Elliott wave theory which was introduced by Ralph Elliott proposes that the uncontrolled behaviour of different financial markets isn’t actually disorganized. The market actually runs through predictable & repetitive waves. These waves are a result of various factors such as outside influence on the investors.

Elliot wave predicts the price of a trading currency pair which will develop in waves. Impulsive waves build the trend and corrective waves retrace the trend. In their most basic form, impulses contain 5 lower degree waves and corrections contain 3 lower degree waves. Elliot wave is fractal and the pattern remains consistent. The 5 impulsive and 3 correction waves define a complete cycle. They form several patterns such as ending diagonals, expanded flats, zigzag corrections and triangles. The key to dealing Elliott waves strongly is by counting them accurately for which there are rules and guidelines.

With advancement in machine learning, neural networks can be used to predict Elliot waves segment as cited here.

Sentiment Analysis

Sentiment analysis is one of the most common text classification technique that analyzes an incoming message and reports whether the underlying sentiment is positive, negative or neutral. This helps a business to understand the social sentiment of their brand while monitoring online conversations. However, the analysis of social media conversations is just a basic level of sentiment analysis. Sentiment analysis applications are very powerful. The ability to derive insights from social data is a method that is being widely used by companies across the globe. The Obama Government in the USA used sentiment analysis to measure public opinion to policy announcements and campaign messages ahead of the 2012 presidential election.

Sentiment Analysis is a relatively new predictive model backed by the availability of excessive data points from social media networks, which can be used to predict various outcomes. Cindicator is a fintech company that enables effective asset management through predictive analytics based on Hybrid Intelligence, which is more of sentiment analysis. I love to mention this blog here: A Sentiment Analysis Approach to Predicting Stock Returns for getting more knowledge on the same topic.

We in BangBit work with one of the apps named CoinAnalysis. It is expecting a new release next quarter with the cryptocurrencies price values prediction feature. The predictions are made through with above mentioned predictive analytics techniques (Regression, Elliot Wave, Fractal time series and sentiment analysis — from social media, crypto-signals and hybrid intelligence from the app users) via Neural Networks, for predicting the fractal nature of the crypto market.

Enterprise Predictive Analytics Software

Whether you are a data analyst, an engineer, or an entrepreneur, predictive analytics can play a crucial role in your day-to-day job. It may improve efficiency in the workplace, reduce business risks, detect fraud, and meet consumer expectations, ultimately giving you an edge against competitors. Your industry experience and professional expertise may equip you with the skills needed to manage your business. However, these will not make it any easier for you to ensure that you are making the best possible business decisions. This is why many entrepreneurs invest in predictive analysis software to reinforce their operations. It can reduce the time needed to collect data from multiple sources, filter data according to unique preferences, and analyze information using various methodologies and algorithms. Data may also be formatted in different visualizations to present. Below are some of the best Predictive Analytics software available in market;

Industries using Predictive Analytics

Most of the modern day business industries can obtain maximum advantages from predictive analytics. As the size of data is increasing, which are getting generated from hundreds of sources such as smartphones, connected devices, sensors, emails, logs, campaigns, transactions etc., industries are now determined to leverage their historical data and get the best out of it by applying predictive analytics.

Let’s have a sneak peek of the industries those use predictive analytics.

Aerospace: Modern-day airlines are generating plenty of data. Due to extensive use of sensors, a lot of data is getting generated and airlines are looking out for effective ways to use those data. Predictive analytics is taking huge leaps in the aerospace industry to reduce maintenance cost, improve aircraft up-time, measuring subsystem performance for oil, fuel, liftoff, control etc.

Automotive: Automobile industry is massively competitive and service providers continually looking out for several ways to take driving experience to the next level. They are always keen to deploy cutting-edge technologies, sensors to ensure customer safety and experience. As most of the automobiles are all set to be connected to the internet of things, the role of predictive analytics has its own significance. The new autonomous vehicles and driver assistance technologies are using predictive analytics to analyze sensor data from connected vehicles and develop driver assistance algorithms.

Energy & Utilities: In the energy and utility industry, predictive analytics is being used to predict the demand-supply. Highly sophisticated forecasting apps use predictive models to monitor plant availability, seasonality, and changing weather pattern. Predictive analytics can potentially save huge money and resources in this industry.

Banking and Financial Services: The banking and financial industry is the first industry to start using predictive analytics. Due to the large volume of sensitive data, BFSI service providers use predictive analytics to offer customized offerings. Predictive analytics is also being used to find opportunities for cross-selling and up-selling, find patterns of fraud and malpractices among a host of other things. One of the common use cases of predictive analytics in the banking industry is the use machine learning techniques and quantitative tools to predict credit risk.

Healthcare: Prediction and prevention go hand-in-hand, perhaps nowhere more closely than in the world of population health management. Machine learning strategies are particularly well suited to predicting clinical events in the hospitals. Organizations that can identify individuals with elevated risks of developing chronic conditions as early in the disease’s progression as possible have the best chance of helping patients avoid long-term health problems that are costly and difficult to treat.

Retail: The retail industry is largely using predictive analytical tools and technologies to get customer insights. It also involves managing warehouse by stocking the right products, selling the right products to the right customers, offering the best discounts to influence sales, having the right strategy for marketing campaigns and advertising among other aspects.

Oil & Gas: Oil and gas industry is a big user of Predictive Analytics. This helps to save huge cost through greater predicting equipment failure, forecasting the need for future resources, ensuring safety and reliability measures are sufficed, and so on.

Manufacturing: The manufacturing industry can use predictive analytics in order to streamline various processes, enhance service quality, supply chain management, optimizing distribution and other tasks for improving the overall business revenue.

Conclusion

In this data-driven and intelligence era, predictive analytics leverage various technologies like big data analytics, IoT, Cloud and AI. Machine learning has made predictive analytics highly efficient by analysing large amounts of data. Predictive Analytics is set to grow at an enormous speed as the need for making data-driven decisions are raising. Organizations are now aware of the importance of their data and intend to derive the maximum benefits from it using predictive analytics to achieve a competitive edge as well as business efficiency. Get in touch today!

How enterprises are using Predictive Analytics to transform historical data into future insights

Written by BangBit Technologies