The Benefits of Predictive Analytics in Modern Business

In today’s business world, it’s vital to be strategic and forward-thinking. Predictive analytics can help businesses determine potential outcomes and avoid unnecessary losses. With mountains of data at their fingertips, business leaders can transform how their organizations operate using the knowledge gleaned from the numbers.  

Those with the skills to interpret data and develop plans using analytical findings will see lots of career opportunities. Demand for data scientists is expected to grow 36% between 2023 and 2033, resulting in more than 73,000 new jobs.¹ These data scientists will have the opportunity to use predictive analytics techniques to grow businesses and impact their success.

What is Predictive Analytics?

According to Business News Daily, “Predictive analytics is an advanced analytics category that helps companies make sense of potential outcomes or a decision’s repercussions. By leveraging mined data, historical figures and statistics, predictive analytics uses raw, up-to-date data to peer into a future scenario.”²

Professionals use statistical modeling, along with machine learning, artificial intelligence, and other technologies, to create predictions. Predictive data analytics is especially useful when identifying risks and opportunities and developing strategic plans.  

Leaders across organizations can use the information provided by predictive analytics. For example, human resources professionals can use it to hire and retain employees, while operations leaders might use the insights to manage inventory. With the correct data, leaders can plan with more accuracy when making business decisions, no matter their focus.

Predictive data analytics isn’t a singular technique. According to the visual analytics platform Tableau, some common predictive analytics tools include:³

• Regression models: Regression models analyze the relationship between variables and determine how one action impacts others to predict future outcomes. For example, using data, an organization could determine what colors influence purchases when packaging a product and design its brand accordingly.
• Classification models: Classification models label data and determine where to categorize it, observing correlations between existing and new data. Banks might use this technique to determine what transactions may be fraudulent based on red flag characteristics.  
• Clustering models: This technique places data into groups based on its attributes to identify patterns. For example, clustering is useful when grouping customers together and developing marketing messages that resonate with specific target audiences.  
• Time-series models: This model links data points to time. Time-series modeling takes data collected over a specific period to determine how future events may play out. This is often used by businesses to predict how products and services sell throughout the fiscal year to forecast numbers and stock and hire accordingly.

 Choosing the right model for your business will help you see the numerous benefits of predictive analytics. It is possible that more than one predictive data analytics technique could benefit your organization.

Predictive Analytics vs. Prescriptive Analytics 

Predictive analytics differs from prescriptive analytics, although the two share qualities and can provide similar benefits. Business analytics consists of three primary components: descriptive, predictive, and prescriptive.  

Descriptive is straightforward. It summarizes data, like sales numbers or customer statistics, to highlight what has happened. Predictive analytics focuses on forecasting what might happen by identifying patterns and trends in historical data. Prescriptive analytics goes a step further by recommending the best course of action to achieve the desired result.

All three components of business analytics can work together to help leaders make the right business decisions for their organization.

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Benefits of Predictive Analytics in Business

It’s exciting for business leaders to be able to predict future outcomes based on the data they’re collecting. There are many benefits to employing predictive data analytics in modern business. Some positive effects include:

• Risk reduction: Predictive analytics can help modern businesses reduce risks to their businesses and their customers using data to determine what risk looks like. Organizations can quickly identify data points that are out of the ordinary to detect things like fraud.  
• Efficiency: Predictive analytics can help foresee demand so that companies can plan resources and staffing accordingly. This ensures that companies don’t waste money on unnecessary products or hiring. The other side is also true – organizations can identify when they need more stock and employees to meet demand.  
• Better decision-making: Predictive analytics takes the guesswork out of planning for the future. Modern organizations can create business plans that are more likely to be successful based on specific data insights.  
• Competitive advantage: Predictive analytics can spot vital customer insights, determine competitive pricing, and create marketing strategies that work. In addition, the analytics technique could be used to attract and retain talented employees. This can help businesses get ahead of the competition in a crowded marketplace.
• Enhanced customer service: Predicting customer behavior and determining the attributes of at-risk customers can help organizations provide a level of care and assistance that is tailored to audiences instead of taking a one-size-fits-all approach.

These benefits don’t come without challenging work. Implementing predictive analytics in your business won’t happen overnight, but when you make choices deliberately, you’ll be able to take your organization in strategic directions.

Implementing Predictive Analytics in Business 

The specific steps you’ll need to take to use a predictive analytics model may vary based on your industry and the data you collect. Here are some key actions you must take when implementing this analytics model in your organization:

• Define your objectives. Before you can move forward with a plan, you must determine what you want out of the process. Knowing your goals will help you define the data you collect, how you process it, and what insights you will focus on. You could use predictive analytics for marketing campaign personalization, for example, or you might want to use it to help detect fraud.  
• Evaluate your data. Acquiring and organizing data is vital. Depending on where your company is in its lifecycle, you may have years of data. Before setting up a predictive analytics model, data must be organized and identified properly. You also must process the data by removing anomalies and filling in gaps.
• Integrate with existing systems and workflows. You will need to make sure your predictive analytics model is aligned with every segment of your business and their systems so that you can collect and store the right data and implement insights effectively.  
• Communicate your findings with the right stakeholders. Building the correct team to use the findings and ensure that they make sense to the appropriate parties is key. Analysts and data scientists who speak the language of data and can make compelling visuals to go alongside it will help you ensure that the predictions you gain can be translated into plans.

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Regardless of your previous academic or professional background, the technical competency you gain in this program will ensure you've mastered current analytics tools and help you adapt to emerging analytics technology in the future. You’ll get hands-on training with industry tools and live engagement opportunities that will ensure you’re prepared for real-world business challenges.  

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Related Resources

• Essentials for Implementing Data-Driven Decision Making
• Why is Data Analytics Important for Business?

Sources

1. Bureau of Labor Statistics, U.S. Department of Labor. Occupational Outlook Handbook. Data Scientists. Retrieved September 10, 2024, from https://www.bls.gov/ooh/math/data-scientists.htm.
2. Business News Daily. Predictive or Prescriptive Analytics? Your Business Needs Both. Published October 24, 2023. Retrieved September 10, 2024, from https://www.businessnewsdaily.com/8655-predictive-vs-prescriptive-analytics.html.
3. Tableau from Salesforce. A Guide to Predictive Analytics: Definition, Importance, and Common Techniques. Retrieved September 10, 2024, from https://www.tableau.com/learn/articles/what-is-predictive-analytics.

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