Prescriptive Vs Predictive Analytics: Differences, Benefits, And Why Your Business Needs Both
Many businesses are embracing the rise of big data, with studies showing that some organizations plan to double investments in their data storage, data mining, and infrastructure year-over-year. However, information from said data is only a powerful business tool if it’s combined with actionable and progressive insights. Enter—predictive and prescriptive analytics.
Together, predictive and prescriptive analytics can support optimal business growth. In this article, we’ll look at the differences between prescriptive vs predictive analytics, the benefits and challenges of both, and how they can inform your business’s decisions and support data-driven strategies.
What Is Predictive Analytics?
Predictive analytics refers to statistics and modeling techniques that use current and historical data sets to make forecasts—or ‘predictions’—about future business trends, business outcomes, and marketing performance. This can include short-term predictions such as staffing needs or long-term trends that show business cash flow or revenue.
Using machine learning, algorithms, and artificial intelligence, predictive analytics can provide business leaders with critical information that helps inform risk or gain potential for numerous scenarios. For example, predictive analytics can help a retailer understand trends about when their highest-volume sales occurred previously so they can develop sales forecasts and inform decision-making for factors such as staffing and inventory.
What Is Prescriptive Analytics?
Often regarded as the future of business data analytics, prescriptive analytics reaches past predictive data by employing artificial intelligence, complex mathematical algorithms, and machine learning. Instead of simply predicting options, it aims to eliminate the gut-feel approach and, instead, recommend optimal future strategies to meet organizational goals and describe the potential outcomes of each action.
Prescriptive analytics works by accounting for all variables and factors that affect business operations. As wider data sets become available, prescriptive analytics can constantly update models and strategies to tackle many organizational aspects, including risk management and business optimization.
Prescriptive Vs. Predictive Analytics
With rapid developments in technology and artificial intelligence and increasingly competitive markets, studies suggest the predictive analytics market is set to reach $23.4 billion by 2030. Both prescriptive and predictive analytics are important business data tools, and although their functions sometimes overlap, they maintain certain key differences and purposes.
Predictive and prescriptive analytics both aim to provide insights that inform your business strategies. While predictive analytics forecasts potential future outcomes based on historical data, prescriptive analytics uses data to develop specific actions based on various possible results. In the simplest terms, predictive analytics considers what might happen in the future, and prescriptive analytics considers what your business should do next.
Predictive analytics draws insights from structured data and variables, such as customer or transactional data, to define the value of an unknown variable. In comparison, prescriptive analytics is less bound by data constraints and considers a variety of datasets, inputs, and other variables and how they interact with each other. As a result, prescriptive analytics can develop models with quantified trade-offs that aim to optimize business performance.
In many cases, predictive analytics isn’t enough to keep your business competitive. When paired with prescriptive analytics, however, these tools can inform each other and provide optimal next steps for your business. For instance, an organization using both tools can use predictive analytics to develop revenue forecasts for the following year. Then, by implementing prescriptive analytics, the organization can model several approaches to optimize revenue growth strategies.
Use Cases Of Prescriptive And Predictive Analytics
Almost any business can benefit from implementing predictive and prescriptive analytics. Consider the following examples:
- Staffing needs: Determine coverage and hiring needs based on various factors such as seasons, time of day, and other details to optimize efficiency and customer experience.
- Targeted marketing: By leveraging past consumer behavior data, you can forecast consumer trends and plan marketing campaigns accordingly.
- Financial models: Using historical data and data analytics, you can forecast financial factors such as sales, expenses, and cash flow to make data-driven decisions.
- Reducing equipment malfunction: You can use algorithms and artificial intelligence to predict or prevent technology malfunctions, mitigating problems and saving costs.
- Content strategy and success: Figure out if your marketing content is doing its job. Is it costing more than you gain from it? Are customers engaging with it positively? Something like a company blog or multiple social media accounts can be a big drain if it's not bringing in leads, for example.
Other Types Of Analytics For Business
Predictive and prescriptive analytics aren’t the only tools for interpreting business data. When combined with tools such as descriptive analytics and diagnostic analytics, these methods help you gain a holistic view of your business and future scenarios and strategies.
Descriptive Analytics
In simplified terms, descriptive analytics answers the question: “What happened?”
By aggregating and interpreting historical data, descriptive analytics develops accessible insights that describe, show, and summarize data points about various aspects of your business. On its own, descriptive analytics can describe data sets such as user or customer data, revenue growth, and price changes.
When used in conjunction with other data analytics tools, descriptive analytics can help you recognize various strengths and opportunities in your business and inform subsequent business strategies.
Diagnostic Analytics
Instead of analyzing and describing what happened, diagnostic analytics considers why certain changes or events occurred. Often implemented as a logical next step following descriptive analytics, diagnostic analysis uses historical data to suggest or identify causal and correlational relationships between variables.
Diagnostic analytics can help you understand aspects of your business like:
- Customer or user behavior
- Technology issues
- Employee satisfaction
- Organizational culture
- Branding and marketing strategy effectiveness
Tips for Using Analytics To Inform Business Decisions
Predictive, prescriptive, and other types of data analytics are powerful tools for informing business strategies and determining your business’s best course of action. Below, find a few tips to help you get the most out of your business analytics.
1. Start with simple data analytics
You can implement countless data sets and rules, but too much data can be overwhelming and distract from meaningful insights. To keep your data focused and relevant, start small with simple analytics. Once you establish those that work for your business, you can consider adding more complex data analytics that can refine and enhance your strategies.
2. Create rich data sets
Since predictive analytics often serves as the foundation for prescriptive analytics and business models or strategies, it’s important that it captures all the pertinent data. By broadening your predictive analytics data and accounting for variable relevant factors, for example, video game user age or location demographics or supply chain availability, you can gain richer insights and better results from your prescriptive analytics suggestions.
3. Keep systems up to date
Whether you’re using prescriptive, predictive, or other data analytics tools, it’s important to remember that your charts and graphs are only as effective as their data inputs. In other words, ensure that your predictive and prescriptive analytics are backed up with accurately sourced and maintained data in order to conserve the validity of your analytics results. Optimize your information and insights by continuously maintaining your business’s data hygiene and updating its algorithmic and artificial business intelligence tools.
Why Your Business Needs Both
If you want to take your business models and strategies to the next level, consider leveraging both predictive and prescriptive analytics. By developing realistic outcome predictions and actionable and quantifiable blueprints for what your business should do next, you can optimize your business’s operations and growth.
Ready to learn more about maximizing your business strategies? Check out our breakdowns of the 10 Best Marketing Analytics Software and the 10 Best Social Media Analytics Software. Make sure to share your favorite articles with your peers and colleagues, and don’t forget to subscribe to our newsletter for updates about emerging business trends and tools.