In this special guest feature, Ty Tucker, CEO of REV, discusses predictive and prescriptive analytics, and how they can be used in tandem to achieve the best results for a business. Ty provides unmatched business services to a wide variety of clients. With experience in growing organizations from two employees to 340,000, he believes that blending active management, business intelligence and advanced analytics is the key to increased productivity.
It’s been noted that if you can’t measure it, you can’t improve it. This is why businesses have begun to utilize analytics to inform strategies based on various collected data. While many businesses can get by with employing either prescriptive or predictive analytics alone, it’s important to realize that you need both to help turn metrics into insights and decisions.
When used in conjunction, prescriptive and predictive analytics can help you create an effective plan to tackle your current business strategy. Here’s a short breakdown of each type of analytics and what they can do you for your company:
Predictive vs. prescriptive analytics—what’s the difference?
Predictive analytics answer the question of what could happen. It utilizes a variation of statistical modeling, data mining and machine learning techniques to study recent and historical data, which in turn allows analysts to make predictions about the future. The purpose is not to tell you what will happen in the future, but instead wants the user to focus on the fact that it can only predict what might happen.
Prescriptive analytics is essentially a type of predictive analytic that asks the question of what will happen. It goes beyond the norm by recommending one or more courses of action for the user. It also shows a likely outcome of each decision. This type of analytics prescribes a possible solution to a problem you may be having. It doesn’t predict one possible future, but gives multiple futures based on the decision-maker’s actions.
Why is it important to utilize both in day-to-day decision making?
While predictive analytics can help businesses forecast what will happen in the future, it is not enough in today’s competitive industry. Prescriptive analytics take data to provide recommendations for the best next steps for any process to land desired outcomes or accelerate results. Together, these analytics inform your business strategies based on data that has already been collected.
For example, a predictive model might suggest that people are looking to buy a coat as the weather cools down across the country, and based on this insight, it might be wise to ramp up on inventory of warm clothes as the season approaches. However, the sales won’t increase at every store across the country, especially in warmer states. Even though predictive analytics show that sales will be up, ramping up on inventory for these items in all stores would be a huge mistake. With prescriptive analytics, businesses can pull in third-party resources such as climate data to get a better course of action for the coming months.
In addition to providing insight into sales trends, prescriptive and predictive analytics can help teams soar to success internally. While no one can predict the future, it’s becoming increasingly easier (with technology like performance management platforms) to show what the outcome will be if one was to take a specific action – especially when it comes to managing teams. Such tools are able to provide organizations with innovative performance optimization to execute key strategic initiatives. They prove that success can be both measured and predictable by providing visibility into user performance, thus giving teams, managers and executives insight into key performance areas so that any course correction can be managed before it’s too late. In addition, they can also highlight key areas where improvement can ultimately impact the end result.
By using predictive and prescriptive analytics, companies will be able to take the right course of action when the time comes, as well as manage teams to keep them on the right track. It’s important to remember that these predictions are only as good as the data that feeds them, so be sure to keep up with your company’s solid data foundation regularly.
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