The field of business analytics is rapidly expanding. Organizations are finding ways to capture large volumes of data and leverage advanced tools to help them turn these insights into action.
Without the right approach or the right technology, it can be difficult to glean much value from data. This is where advanced analytics comes in.
Today, we’re discussing the differences between predictive vs prescriptive analytics, so you can determine if you should include either or both capabilities in your next IT implementation.
The Overarching Field of Advanced Analytics
Before we dive into the granular differences between prescriptive and predictive analytics, let’s look at the umbrella they both fall under: advanced analytics.
This is a broad field that includes diagnostic and descriptive analytics, as well.
It’s easy to confuse advanced analytics with business intelligence (BI). The key differentiator is the direction of the focus. All types of advanced analytics ask two general questions:
- What will happen?
- What might change in the future if we take X action today?
Conversely, BI mostly looks backward. It references historical data to understand what just occurred and why it occurred.
Take a marketing campaign, for instance. When it’s complete, teams can apply BI to understand the elements that worked and the ones that didn’t. The software will look at key inputs, such as the number of people who visited a landing page or purchased a product. Then, it will use that information to gauge where the campaign was most effective.
On the other hand, advanced analytics will use this information to forecast how the company can improve on the next campaign. How are customer preferences trending? Where are there new opportunities for upselling or cross-selling? Where should we put our focus going forward?
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What is Predictive Analytics?
Predictive analytics is a subset of advanced analytics that asks the question: “What is likely to happen in the future at our organization?”
These tools leverage historical and real-time data by accessing enterprise software solutions, such as:
- Enterprise resource planning (ERP) software
- Customer relationship management (CRM) software
- Point-of-sale (POS) software
- Human resource information systems (HRIS)
Predictive analytics software mines the data sets to identify common patterns. Then, it uses advanced algorithms and statistical models to make correlations between them.
Real-World Applications
Predictive analytics tools are useful across a variety of industries.
For example, retail leaders often use predictive analytics to understand how future changes in customer behavior might affect their bottom line. By analyzing current data and identifying trends, they can understand how one specific change (e.g., seasonal shopping fluctuations) could affect their bottom line down the road.
Likewise, manufacturers can use predictive analytics to understand the current and projected performance of their parts and machines. Using aggregate data from real-time sensors, this software can project:
- When components will require replacing
- The likelihood that a component will fail
- The probable causes of component failure
No type of analytics can tell you exactly what will happen in the future. Instead, these sophisticated models are using machine learning to analyze different variables and surmise likely probabilities.
What is Prescriptive Analytics?
While predictive analytics asks, “What might happen in the future?” predictive analytics takes a more actionable approach. These tools ask, “What do we need to do to make that outcome happen (or avoid that outcome)?”
By leveraging this software, you can prescribe specific actions for your teams to take so you can capitalize on future growth or sidestep an impending problem.
Prescriptive analytics applies a variety of methods to evaluate data gleaned from your ERP, CRM, POS, or HRIS system. These include:
- Machine learning
- Computerized algorithms
- Computational modeling
As you might expect, this involves a significant degree of hypothetical thinking, as well as trial and error. It’s not perfect, but it allows you to more easily determine your best steps forward.
Real-World Applications
The algorithms in prescriptive analytics often use “if” and “then” statements to make valid recommendations based on combinations of requirements. This is the natural next step to analyzing the insights that predictive analytics provides.
For instance, one data set may reveal that 50% of retail shoppers are “unsatisfied” with a brand’s customer service. Predictive analytics can help identify what may happen in the future if the company doesn’t improve the customer experience. What percent of shoppers are likely to abandon the brand if these negative interactions persist?
From there, prescriptive analytics algorithms may recommend specific steps to minimize risks. These steps might include additional training for the support team or a more user-friendly eCommerce experience.
Predictive vs Prescriptive Analytics: Leveraging These Insights
Your business captures volumes of valuable data every day. What you do with it is up to you.
When it comes to predictive vs prescriptive analytics, there isn’t one approach that’s better than the other. It’s typically best to leverage both.
When you can more clearly understand what may happen (predictive analytics), you can take the right steps to ensure the desired outcomes (prescriptive analytics).
Our ERP consulting team can help you select and implement an ERP system with advanced analytics capabilities (or at least the ability to integrate with such capabilities). Contact us below for a free consultation.
