Modern businesses have access to datasets that are staggering in the sheer volume of data they contain. Most of the information pulled from this data relates to events that already took place. The total expenses spent on material last year, the number of new customers you acquired, the average sales value per salesperson, and so on.
This historical information can be useful, but wouldn’t it be better if you could have insights into what may happen in the future? This is the promise of predictive analytics. Businesses can now use software that builds models using historical data to predict the potential for future outcomes. When you run current data through the predictive model, the system can then provide insights into what may happen in the future. These systems can even be enhanced and made more accessible using tools like augmented analytics.
But what does it look like in practice? What are some of the ways a business can use predictive analytics to stay ahead of what is coming? In this post, we will cover some of the practical applications for predictive analytics.
Predicting Demand for a Product
One example of useful predictive analytics is the forecasting of demand for a product. Maybe you run a retail business, and you want to make sure you always have enough of a product to meet consumer demand, but you don’t want to risk overstocking because storage and shelf space are valuable, or it might be products that have a limited shelf life.
A predictive analytics system can take data from a variety of sources to help a retail business manage its inventory. Historical sales data would be one obvious source of useful data for this application, but it could also look at the time of year, weather, and consumer sentiment among a range of other signals that might be relevant.
Managing Material Needs
A manufacturing business might need a variety of materials to keep production moving. With that said, the future of demand can be murky. You don’t want a manufacturing facility to run out of key materials, but you also do not want to warehouse vast quantities of materials that might not be needed until a few months into the future. Beyond that, there is also the question of acquiring materials at the best price.
The managers of manufacturing businesses can gain valuable insights into these concerns when they use predictive analytics. On the one end, you can predict the demand for the products the facility makes, but you need a system that can also consider things like supply chain disruptions, weather issues, and changes to the prices for different materials.
Seeing the Future of Real Estate
Predictive analytics has a lot of potential to inform the decisions of real estate businesses. At the agent level, predictive analytics can help them identify market segments that may have an increase in the number of prospective buyers in the near future. On the level of the investor, you could predict the future value of a property or create heat maps that indicate areas ripe for development or other types of investment.
Predictive analytics in real estate could draw on a number of data sources to provide forecasting in several different ways. Obviously, the current and historical value of properties would be valuable. These systems could also draw on data from lenders, occupancy rates, foreclosures, infrastructure development, and more.
A Window to the Energy Markets
The future of energy markets could be of interest to several different types of organizations. Financial institutions could use these predictions to make investments, and energy-intensive businesses could predict future costs or make purchases based on predicted rates. Utility companies could use predictive analytics to forecast rates for customers and to handle load management.
Those interested in energy markets would have several data sources that could help with building predictive models. Histories of supply, demand, and pricing would be valuable. This is especially true when you consider the way pricing can affect demand. Beyond that, it could account for things like regulations, equipment failure, weather events, and geopolitics.
This is just a look at a few ways predictive analytics can help an organization stay one step ahead of changes on the horizon. With that said, different organizations will need to deploy these tools in different ways.
With AI-driven solutions like Crux Intelligence, tools like predictive analytics are more accessible than ever before. Contact our team to set up a demo or learn more about augmented analytics and what they can do for your company.