When your business has a solid foundation of data governance and descriptive data analytics, you can make a move towards diagnostic and later on towards predictive analytics. All of these fit into the data analytics spectrum in different capacities and aid your decision making in one way or the other. Laying down a predictive business analytics model is a difficult and iterative process. You need to be sitting on a lot of clean and organized data to put this in motion. A lot of companies seek help from data analytics facilitators and it works more often than not. Nevertheless, if you can handle this yourself and find and retain the resources to drive the iterations and modifications, that would be fantastic. Now, let us focus on prediction driven decision making.
Demand analysis for better inventory management
You have spent sleepless nights thinking about inventory as a manufacturer, supplier, or retailer. Which products to stock up, which ones to put at the front of the warehouse, when to increase production, all these questions haunt you because of the unpredictability of the market. Predictive analytics can help you kill the anxiety of inventory management by predicting demand for a certain product.
You have thousands of rows of sales data, organized beautifully by region, time, store location, and whatnot. You have the number of increased sales after each marketing drive and each discount offered. All this data can be used to create a predictive model which recognizes patterns in historical data and puts out a near accurate blueprint of the future numbers.
This can be used to identify probable peaks in demand for a certain product well in the future. Businesses can make crucial decisions in terms of inventory management in light of the predictions.
Customer analytics for enhanced marketing decisions
Whether you are pursuing highly potent customers or building a strategy for stone-cold leads, evaluating your customer is key in terms of making the right marketing decisions. This is how predictive analytics helps.
Let us say you spend $50 on a social media ad campaign focusing on middle aged men from a certain region. You will get a detailed description of its performance. You will know what has worked and what has not. Now, if you have had many instances of similar procedures you are bound to have a ton of data. This data can be used to predict the performance of a certain ad campaign for a region and for a demographic segment.
In fact, with the help of predictive business analytics you can extract more value from your customer segmentation efforts.
This helps you channelize your efforts in the best manner and through the best channels and saves you a significant amount of resources.
Be it computer hardware, heavy machinery, or vehicles, maintenance takes up time and bleeds your company. Fortunately enough, most logistic vehicles and machinery are loaded with sensors that feed information to a database. You can design predictive models to analyze this data related to the temperature of a machine, the voltage, vibrations, etc, and predict when a machine would need maintenance.
Through predictive maintenance you would be able to pre-schedule maintenance for machines that are the most likely to break down. You can delay maintenance for machines that are not showing any possibilities of malfunctioning in near future.
This again takes a lot of anxiety off your plate and helps you run your supply chain and logistics smoothly.
Fraud detection and default prevention
This is something banks and financial institutes use on a regular basis. Before sanctioning a loan to a person they put their historical data through predictive analysis to find out how likely it is that the person will default.
This is a matter of survival for banks. They have been the forerunners in terms of using advanced analytics.
Similar predictive models can be used to identify fraudulent transactions. In fact predictive analytics can be used to prevent fraudulent transactions too by identifying certain patterns.
Predictive analytics in healthcare
This could be a whole essay in itself, but we will just look at one use case to form an idea. Patients who are the most at risk of developing chronic conditions can use better care and supervision. Fed with data from lab tests, bio-metric data, health data generated by the patients, predictive models can generate risk scores for various patients and give the healthcare professionals a better opportunity to intervene when needed the most.
Be it an MNC or a hospital, predictive business analytics aids decision making and enhances the processes significantly.
At Algoscale, we are helping businesses translate their data into meaningful and actionable insights. We serve different industry verticals by creating highly customized solutions that meet our client’s needs precisely. At the heart of every solution we provide, lies our team’s utmost dedication to deliver the right solution to our clients.