Predictive analytics is an area of your business that’s ripe for potential.
Consider that organizations were looking to invest $57 billion in hardware, software, and professional services for big data in 2017. That number was expected to grow to $76 billion by the end of 2020.
Why should you be considering predictive analytics for your organization?
According to HBR, predictive analytics should be viewed as more than just a money drain. “The value derived … can greatly exceed the cost of the infrastructure,” they report.
Here are three examples of how companies have used predictive analytics to improve the supply chain, predict customer demand, and improve pricing models.
How Predictive Maintenance Improves Supply Chains
A disrupted supply chain is not a revenue-producing supply chain.
When supply chains are down, organizations spend money fixing it with new equipment, transportation, and labor fees.
Predictive maintenance is “the servicing of equipment when it is estimated that service is required, within a certain tolerance.” They are designed to ensure that maintenance doesn’t happen too soon, so that money isn’t wasted for unnecessary work, and also doesn’t happen too late.
For example, airlines can use big data to predict mechanical failures in order to mitigate flight delays or cancellations.
With more than 20 million technicians in field service, “even minor adjustments to variables such as time taken to carry out repairs can have huge consequences.” This is aided by IoT technology, such as sensors and real-time monitoring. The benefits here are such that the medical equipment manufacturer Medivators… “has seen a 78% increase in the number of service events which can be diagnosed and corrected remotely, with no need to dispatch a field technician.”
In short, predictive analytics can strengthen supply chains.
Predicting Demand With Big Data
Many factors contribute to demand, including the weather, according to a case study published by GIS firm Esri.
They worked with a Fortune 200 food and beverage chain to correlate weather patterns to its customers’ buying behavior. Now, they use that information to anticipate needs and boost sales.
Esri combined weather data, company data, and their geographic information system to analyze relationships. They found how to read weather to better predict which products to stock and where, and which ones to promote through different marketing campaigns. “Instead of reacting to customer demand,” Esri said, “the company uses the weather to get ahead of it.”
Improve Pricing With Predictive Analytics
A 2017 case study from Deloitte showed how the firm revitalized the pricing model of a multinational food and beverage company.
Spurred by “fierce competition, evolving consumer preferences, and an evolving retail/channel landscape,” the company sought to overhaul its revenue management and pricing capability.
Deloitte found through their analysis that the company was, like many, siloed. As a result, it was leaving millions of dollars on the table. By enabling proactive pricing decisions and breaking down these silos, Deloitte could help this company reach its untapped value.
As a result, Deloitte conducted a pilot that, in one grocery category, improved profitability and increased overall sales volume by 9 percent.
All of these examples show how predictive analytics software provides your business with better information. You can use this data to inform key insights about your business so that you can make the best decisions.