Predictive Analytics vs Prescriptive Analytics

Predictive Analytics vs Prescriptive Analytics

With so many types of data analytics and analytical methods out there, many people are curious about the relationship between predictive analytics vs prescriptive analytics.

Today, we’ll review all four types of analytics and delve into predictive and prescriptive analytics to help you understand when and how to use both.

What are the 4 Types of Analytics?

• Descriptive – This type of analytics tells you what happened.
• Predictive – This type of analysis explains what might happen in the future.
• Diagnostic – This type of analytics helps you understand why things happen.
• Prescriptive – This type of analysis helps you decide what to do next.

In this piece, we’ll be focusing on predictive and prescriptive analytics.

What is Predictive Analytics?

This future-focused type of analytics is all about deciding what might happen in the future. It looks at historical trends to see what predictions it can make. This can help businesses make plans for how to meet changes in demand. To do this, they might use predictive analytics software or manual analytical methods.

What are the different types of predictive models?

These are the four main types of predictive models:

• Classification – these models group data into categories based on defined criteria
• Clustering – these models group data points by their similarities
• Outlier – these models look for dissimilar data points to ignore
• Time series – these models focus on historical data to give forecasts

What is Prescriptive Analytics?

This kind of analytics does more than tell you what it thinks might happen – it tells you what to do about it. The output is then a series of recommendations for your teams to evaluate and action. According to HBR, “Machine-learning algorithms are often used in prescriptive analytics to parse through large amounts of data faster—and often more efficiently—than humans can. Using “if” and “else” statements, algorithms comb through data and make recommendations based on a specific combination of requirements.” However, you won’t want to let the computer run with these recommendations, in general. It’s a good idea to have them looked over by a human expert within your team to approve or deny the suggestions.

Predictive Analytics vs Prescriptive Analytics – What are the Differences?

For predictive analytics vs prescriptive analytics, they both look at and analyse trends. Prescriptive software goes one step further to recommend or sometimes even take a course of action on the back of the results. You can see both of these technologies at work in predictive sales analytics software that includes lead scoring or product suggestions. When you accept and follow algorithmic recommendations – like watching what’s suggested to you on Netflix – you’re tapping into prescriptive analytics.

Pros and Cons of Prescriptive Analytics

With prescriptive analytics, you can make fast, data-based decisions that are designed to reduce your risk. In turn, this can make your operation more efficient as a lot of the leg work is done for you. The downside, of course, is that the data is only as good as what it’s fed. If your input data is poorly-formatted or wrong; you’ll get more erroneous output. Plus, it’s not very good at making long-term decisions. The bigger your timeline or more critical the business function, the more you’ll want to temper the recommendations with human oversight.

Pros and Cons of Predictive Analytics

Predictive analytics are amazing tools that, according to Georgetown University, “provide managers and executives with decision-making tools to influence upselling, sales and revenue forecasting, manufacturing optimization, and even new product development.”

But, just like with prescriptive analytics, you need great data to start from. There’s a risk that your computer analysis will fail to account for the human factor when accounting for trends and they’ll need to be kept up to date to factor in current economic impacts and consumer confidence. Lastly, you’ll need people to make conclusions based on that data and act on them; so it’s slightly less autonomous than prescriptive. But that means predictive analytics can benefit from the expertise of your team when sense-checking the findings.

Use Case of Prescriptive or Predictive Analytics Software in Sales

Prescriptive or predictive analytics software in sales can offer enhanced ROI versus other optimisation approaches. It does this via use cases like:

• Identifying cross-selling and up-selling opportunities within your current database
• Offering enticing products to your customers during checkout
• Assign priority to high-potential accounts so sales teams can allocate their time
• Flag accounts that are likely to churn for direct handling by AM
• Discover pockets of demand for new products so you can focus R&D efforts
• Determine the price ceiling for products so you can extract the maximum value


Predictive Analytics vs Prescriptive Analytics – Summary

These two key analytical models and the predictive analytics software they inform are core components in your profit maximisation toolkit. From interrogating trends to recommending a course of action, predictive analytics vs prescriptive analytics have evolved through machine learning. And they should be looked at as an essential AI-assisted business tool for all wholesale and manufacturing operations. If you’re ready to explore how our predictive analytics software could help you reduce churn, increase profits and keep more of your customers for longer; reach out now. We can help you estimate your suitability for this amazing application of ML and determine just how much upside it could generate.


Further Read:

Georgetown University: Pros and Cons of Predictive Analysis

Cote, C. (2021): What is Prescriptive Analytics? 6 Examples. Ed.: Harvard Business School

Frankenfield, J. (2023): Predictive Modeling: History, Types, Applications. Ed.: Investopedia

Business News Daily (2023): Predictive or Prescriptive Analytics? Your Business Needs Both