Businesses in all industries are leveraging predictive analytics (PA) to make better decisions. The healthcare industry, in particular, has made great strides thanks to PA. The technology is helping healthcare professionals predict diseases ahead of time while providing doctors with data on the most effective treatment options.
Every business uses data to drive their business decisions. So, why the need for predictive analytics if you are already using data? Comparing data with and without predictive analytics is like comparing a domestic cat to a Bengal Tiger–there is a vast difference.
Sales forecasting is one of the biggest challenges every business leader has to deal with. Month after month, sales teams come up with revenue forecasts that management teams have to accept while knowing there is at the most a 46% accuracy of meeting targets. This is a forecast that is less accurate than flipping a coin. Optimism is great, but for CEOs trying to set up expenses that are dependent on revenue, this is always a cause for concern. More and more businesses are turning toward predictive modeling specifically for revenue forecasting.
The world of retail is changing, with heavyweights such as Macy’s, Sears and Kmart shutting down many stores. So, how can retail businesses adapt in order to stay relevant? One strategy is to leverage predictive analytics (PA).
According to a whitepaper published by IDC and IBM, the return on investment (ROI) of business analytics solutions that incorporate predictive analytics is about 250%. This statistic illustrates why more businesses are turning toward predictive analytics to stay ahead of their competitors. Technology adoption, after all, is everywhere and in every sphere of operation. More information has been generated in the last 10 years than ever before. Brands are in a race to adopt technologies that can retain customers and to find new business opportunities. This is why predictive analytics is now on the map for businesses of all sizes.
Amazon seems to read our minds, often predicting what we want even before we even know it. Predicting customers’ orders is vital to increasing sales while reducing inventory and supply chain costs. Simply put, that is what predictive analysis for e-commerce is all about. Can you risk being wrong-footed when others in the industry are getting it right?
Whether you love or hate spreadsheets, we all need them. Spreadsheets have revolutionized data since the 1980s when they were the “new kid on the block.” Every accountant swears by them and business analysts spend hours trying to make sense of the rows and rows of numbers. But, for the human mind, a picture is still worth a thousand words. These very same business analysts are looking to go beyond the pie charts and graphs of these spreadsheets.
Over the last two decades, the U.S. has been plagued with high rates of prescription drug overdose. About 142 American citizens die every day from drug overdoses, many of which are caused by prescription medications obtained legally. Additionally, the number of opioids prescribed as of 2015 is three times as high as the number of prescriptions given out in 1999. Due to these staggering statistics, doctors and data scientists are working towards using predictive analytics to reduce the number of prescription drug overdoses.
In 2018, the national per capita healthcare expenditure in the U.S. is projected to be about $11,193. This number has been steadily growing over the years. What’s most alarming about this figure is that it’s double what other ‘rich’ countries are spending per person. Surprisingly, this high expenditure does not come with better health outcomes and hospital revenues have plateaued.
With supply chains becoming larger and more complex, it is becoming more difficult to improve service through a reduced order-cycle time while enhancing inventory availability and reducing operating costs at the same time. Big data is revolutionizing the optimization of supply chain and inventory management by improving visibility. It is telling a compelling story that is making leaders listen. Companies that have adopted predictive analysis see up to a 50% reduction in non-performing inventory and a 25% reduction in inventory holding costs, thus freeing up working capital.