Foresee The Future With Predictive Analysis And Outsmart The Market Competition.

Foresee The Future With Predictive Analysis And Outsmart The Market Competition.

The unfolding evolution of Predictive Analytics coupled with Big Data is changing the fundamentals of how a business operates. The present digital era is witnessing a huge proliferation of smart sensors, interconnected devices & networks that are generating huge swathes of data for businesses, individuals, and companies that can be mined to gain valuable peek into the future!

The predictive ability technology has grown. It is much easier to use, and the findings can be easily communicated, implemented, and explained. Analysis is no longer confined to wider views about what will happen to the national economy. Skilled entrepreneurs can now use advanced predictive ability every day to find new ideas to boost prediction accuracy. A timely investment in predictive analytics can give your business. An investment in predictive analytics can give a business the much needed competitive advantage it needs.

As the day-to-day transactions happen, the customers are regularly providing data that can aid in predicting the future. Predictive analysis can be the perfect digital marketing tool for your business by analyzing the patterns of consumer transactions to refine promotional campaigns. But, how does a predictive analysis function? Let’s have a look at it!

THE PREDICTIVE MODELS- HOW THEY WORK?

The traditional predictive models are mostly dependent on the availability of historical data that helps them to study present sets of data and come to conclusions based on the previous outcomes. However, with Machine Learning and Artificial Intelligence, things have changed and real-time data can be processed with much more precision than ever before! To give you a clear idea, let’s discuss some traditional models first.

Forecast Model

The Forecast Model is a widely used tool in predictive analysis and it deals in metric value prediction. It mines the historical data and evaluates the numeric data to provide a fair estimation of the newer sets of incoming data. This model can be used where there is an availability of historical numerical records.

This model can be applied wherever historical numerical data is available. Here are a few scenarios where it can be implemented:

  • A call center can estimate the number of support calls they have to attend per hour.
  • A SaaS firm can predict the number of customers it is likely to convert in a specific week.
  • A clothing store can efficiently calculate the inventory they require to meet demand during a particular sales period.

Outliers Model

The Outliers Model is based on abnormal dataset entries. It can identify anomalous statistics by itself or by coupling and tallying with the general record of numbers and categories.

The outlier model can come handy in the retail and finance industry. For instance, the models can not only analyze the amount involved in a transaction but also the location and time of purchasing, purchase history, and the essence of purchase while efficiently detecting fraudulent transactions. Here are a few scenarios where it can be implemented:

  • Detection anomalous dataset within transactions to separate frauds from normal transactions
  • Recording an increase in specific support calls which might indicate a product failure and warrant a recall of a particular product
  • Finding unexpected data in the net operation logs & perceiving the signs of imminent unplanned downtime

New- age Predictive Algorithms

Overall, algorithms of predictive analysis can be divided into two groups:

  • Machine learning: It consists of structural content, which we see in a table. For this, both linear and non-linear variations are used in algorithms. Linear algorithms learn quicker, whereas non-linear ones are better suited to the challenges they might encounter(often non-linear & unpredictable).
  • Deep learning: It can be defined as the most common subset of machine learning that involves audio, video, pictures, and text.

There are many different algorithms that can be implemented with machine learning predictive modeling. Below are the examples of two of the most widely utilized algorithms for predictive analytics.

Generalized Linear Model

The Generalized Linear Model (GLM) involves the study of multiple variables’ effects on continuous variables prior to deciding on the best-fit model from a number of distributions.

For instance, let us consider that a business wants to know the customer purchase behavior in summer for cold drinks. A regular linear model can reveal that for every positive degree increment in temperature, an additional 300 cold drinks are purchased in a locality. It might seem logical to assume that if the temperature rises from 28°C to 35°C, 2100 cold drinks might be sold. However, it is illogical to expect that the sales will witness the same linear growth if the temperature reaches 40°C. 

The Generalized Linear Model narrows down the number of variables and suggests a rise in sales from a certain temperature & a drop or flattening of sales from another specific temperature.

Random Forest

One of the most popular classification algorithms, Random Forest derives its name from the fact that the algorithm is a mixture of various decision trees. Every tree is separately sampled with an equal distribution of random vector values for all forest trees and is cultivated to its maximum degree. A random forest is adept in both classification & regression. It can precisely classify huge volumes of data and is very accurate when running on large databases. Multiple trees minimize the divergence and bias of a smaller set or single tree. Random forest maintains accuracy even if a large proportion of data is missing.

There are several other predictive analysis models, algorithms, and techniques that are employed to help businesses get many different insights from the available data. Thus, predictive analytics is adopted by organizations increasingly in many sectors like retail, energy, finance, manufacturing among many others. Listed below are some proven benefits of predictive analysis.

Enhanced Production Efficiency

In tandem with big data and IoT tools, the manufacturing and production facilities are using predictive analytics to radically improve the manufacturing cycle. To meet the current consumer demand or to anticipate future production delays or heightened demands, valuable insights can make inventory forecasting more efficient.

Precise Risk Detection

In several ways, predictive analytics is becoming more relevant as they can foresee the probability of disasters and other disrupting events that can affect supply-chain, demands, and other possible problems.

Strategic Decision-making

The more data the predictive analytics tool has, the more reliable its predictions and better the decisions that are taken! As predictive analytics can recognize patterns and trends in enormous amounts of structured & unstructured data, it can provide intelligent insights that help in decision-making.

Targeted Marketing Campaigns

Predictive Analytics helps in knowing the customers, their locations, and their buying habits & preferences. Equipped with this knowledge, exclusive products, and offers can be decided along with the promotion of cross-selling opportunities. Predictive analytic algorithms can tailor and customize marketing strategies to attract, maintain, and expand businesses’ most beneficial clients.

EndNote:

There are an increasing amount and variety of data (structured and unstructured). The transformation of data into knowledge and then into practice is more critical than ever before. As the magnitude and the complexity of information increases, companies face a greater burden.  Predictive Analytics can utilize huge amounts of data and turn them into assets for any business/startup. Invest in the best predictive analytics services today!