What is Predictive Analytics?

Predictive analytics is a form of advanced analytics in which predictions are made about the future. There are a lot of techniques that are used in predictive analytics. These techniques include statistics, modeling, historical data and artificial intelligence. The future predictions are made by bringing together the information technology, management, and modeling business process. With the help of predictive analytics, it becomes easy to identify the risk and future opportunities.

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Examples of Predictive Analytics

Business Marketing – You most likely have noticed a lot of time when you open any shopping website to buy anything. But, in the end, you close the website without buying anything and after that when your browse anything else on the internet there is ads that are similar to the products that you were trying to buy. All this take place for the reason that the algorithm is based on predictive analytics.

Amazon Recommendations – You must have seen that whenever you search for a product on Amazon they continuously show you the related products on your return visit. It happens because Amazon uses machine learning. And with this, they track your previous interactions and learn what you will like the most. So according to this Amazon recommend you the product similar to your choice.

Predictive Analytics Tools

Radius – With this tool, one can get many data analytics services. This tool helps you to match the profile of your company with other companies that have audiences similar to yours. Radius is a cloud-based structure.

Board – This tool is very useful as its predictive modeling is built around a live dashboard and responsive interface. So it helps you to analyze the possible outcomes multiple times without creating a new model. There are so many pre-built connectors. For insurance and banking sectors, this tool is best.

Halo – This tool is designed for the supply chain management. For better optimization of the supply chain, its predictive features use comparative analysis of the current data. Halo is a cloud-based and user-friendly platform and with the help of this tool, one can keep an eye on the supply chain in real time.

Predictive Analytics Techniques

Linear Regression – This technique is used to analyze the relationship between the dependent and predictor variables and is stated as an equation that computes the response variables as a linear function of the parameters.

Logistic Regression – This technique is also known as logit regression. This technique is used to predict the probability of occurrence of an event. It is done by fitting data to a logit function.

Stepwise Regression – When there are a lot of independent variables, then this technique is used. An automatic process is used to select the independent variables and there is no involvement of human in this technique.

Ridge Regression – If the data suffers from the multicollinearity, then this technique is used. The least squares predictions are balanced in multicollinearity.

Predictive Analytics Algorithms

Supervised Learning – This type of algorithm consists of a dependent variable that is to be predicted. It is predicted from a given set of independent variables. By using these independent variables, one can generate a function. This function map inputs to desired outputs.

Unsupervised Training – In this type of algorithm there is no need of any variable to predict. Mainly it is used for gathering the population in different groups. Most of the times it is used for segmenting consumers in dissimilar groups.

Reinforcement Learning – With the help of this algorithm a machine can make the decisions. The machine trains itself by continuously using the trial and error. It takes a lesson from the past experience and tries to make accurate decisions by capturing the best possible knowledge.

Predictive Analytics Using R

Data Gathering – Data cleansing operation is performed after reading the data from various sources. Noisy data is identified as well as outliers are removed. With this prediction become more accurate. To handle impure value and data that is missing, apply R packages.

Analysis of Data – For well-organized processing data should be transformed before model building. The processing is done by normalizing the data. With the help of correlation analysis, attributes that are irrelevant can be removed. These irrelevant attributes play a least important role in defining the outcomes.

Building a Predictive Model – The Predictive model can be built by applying linear or logistic regression. In this, a classification algorithm is chosen, test data is identified and classification rules are generated.

Inferences – Cluster analysis is performed to segregate data groups. To make inferences, these meaningful subsets are used.

Predictive Analytics in Marketing

Analyze Customer Behavior – Predicting and analyzing the behaviour of the consumer is the hallmark of the businesses like Amazon. They highlight the products that consumers are going to buy in given time.

Improved Conversions – With the help of predictive analytics you can target only those customers which you are sure that they will buy the product. This was not possible with the traditional marketing techniques. So by doing this, you can increase your conversion rate.

Time-SavingPredictive analytics is really a time-saving technique as you can target only the interested customers. You don’t need to waste your time on chasing every lead instead you can utilize your energy to target the information that has shown the most curiosity.

Less Costly – Compared to the other techniques to retain the old customers, predictive analytics is less costly.

Optimize your Marketing Campaign – With the help of predictive analytics your sales team will be able to find your target audience and they can analyze what your customers buy and how much they spend on which service or product. So with all these information your sale team can make the best marketing campaigns that will help you to get the best possible conversion ratio.

Behavioral Analysis – Like all other tools that are used to analyze the predictive tool also uses demographic data. The actual results come from the behavioral data that is recorded by a predictive analytics tool. It will map historical data and the relationship between these data points to make patterns that will lead to the final sale.

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