Predictive analytics is a branch of the larger field of data analytics that deals with making predictions about future events or behaviors. Many diverse industries utilize predictive analytics, including financial services, health care, retail, and manufacturing.
You can use techniques from statistics, data mining, modeling, artificial intelligence, and machine learning to do predictive analytics of current data and predict what will happen in the future. You can use predictive analytics for diverse business applications, from detecting fraud to predicting customer behavior and forecasting demand. You can also use this data science technique in other areas, such as medicine, to predict disease outbreaks and the weather.
Predictive analytics is a powerful tool for improving decision-making in business, government, and other organizations. Here is more detail about how you can use predictive analytics to improve business outcomes.
Predictive analytics has applications in customer service. You’ll be able to identify customers at risk of leaving or those likely to need more support. With a clearer understanding of the data, you can proactively prioritize and contact customers and offer them more personalized service.
Predictive analytics provides you with insights for forecasting in business, such as in sales and demand forecasting. These predictions can guide your decisions about inventory levels, production levels, and marketing campaigns.
You can use predictive analytics in credit scoring processes to identify customers likely to default on their loans or those likely to make late payments. This information helps you to set suitable credit limits and interest rates.
Predictive analytics is instrumental in marketing. You can use it to gain insights to help you better target customers and prospects. By analyzing customer data, you can identify patterns and trends indicating a need or desire for a particular product or service. By understanding the characteristics of potential buyers and how they’re likely to behave, you can segment markets and create targeted marketing campaigns more likely to result in conversions.
Read more: What Does a Marketing Manager Do? A Guide
The process of underwriting insurance policies routinely uses predictive analysis. By analyzing data on past claims, insurers identify patterns that may indicate a higher risk of future claims. Armed with probabilities and predictions, you can adjust premiums for individual policies or groups of policies or even deny coverage altogether.
Predictive analytics can help you to make better decisions. By analyzing past data, predictive analytics can identify patterns and trends that enable you to make a probabilistic determination about events in the future.
You can then use this information to make decisions about:
• How to allocate resources
• How to respond to potential risks
• How to take advantage of opportunities
Predictive analytics can even help you to improve customer service by identifying potential issues before they occur and providing recommendations for how to resolve them.
You can find powerful predictive analytics examples and use cases in diverse industries. Many sectors have come to rely on data science to remain competitive.
Retail: Predictive analytics is essential for retailers who want to understand customer behaviors and preferences better. Additional predictive power means you can make more informed decisions about product assortment, pricing, promotions, and other aspects of your business.
For example, you might use predictive analytics to determine which products are most likely to be purchased together and then offer discounts on those items combined. You might also use predictive analytics to identify customers at risk of leaving for a competitor and take steps to keep them loyal.
Banking: Banks use predictive analytics to make more informed decisions about credit and investment products and even trade currency. As a banking predictive analytics specialist, you might use large data sets to form patterns that identify customers at risk of defaulting on a loan. You might also use predictive analytics to identify customers likely to be interested in investing in a new financial product so that you can target them with impactful marketing campaigns.
Sales: Sales organizations have long used predictive analytics to understand better customers’ wants and needs. By analyzing past customer behaviors, you can more accurately predict which products or services a customer is likely to purchase. This allows you to focus on selling the most appealing items to your prospects and ultimately increase your sales revenue.
Insurance: Insurance companies use predictive analytics to determine the likelihood that a particular customer will make a policy claim. By analyzing claims history, demographics, and lifestyle choices, insurers can develop models that help them predict which customers are most likely to file a claim. This information allows you to adjust premiums and identify and target higher-risk customers with specific policies.
Social Media: Social media companies use predictive analytics to understand user behavior and trends. By analyzing the vast amount of data generated by users on social media platforms, you can gain insights into the things that people care about, what they are talking about, and how they interact with each other. This information improves the user experience on social media platforms and enables you to target advertising more effectively.
Health: Predictive analytics in health care can identify patients at risk of developing certain diseases or conditions. By analyzing demographic data, health records, and genetic information, doctors and researchers can develop models that help them create health policies and interventions. You can use predictive analytics to create targeted prevention and treatment programs for those patients at the highest risk.
To succeed in a career in predictive analytics, you’ll need to be comfortable working with large datasets and have strong analytical and statistical skills. You should also be able to communicate your findings clearly to non-technical audiences. You can learn these skills through formal education, courses, and professional experience.
A data scientist typically has a strong background in mathematics and computer science, and many also have a master’s degree or PhD. In specific educational paths, you'll find a few different options that can lead you to a career in data science.
One option is to study statistics, information technology, or another relevant field at the undergraduate or graduate level. Another option is to pursue a more general computer science degree and then specialize in data science through online courses, certifications, or by gaining professional experience.
Some data scientists have taught themselves the necessary data science skills through online resources and personal projects. However, employers typically look for at least a bachelor's degree in a relevant subject.
In addition to formal education, gaining professional experience is essential for becoming a data scientist. You can gain experience in predictive analytics through internships, working with datasets in freelancing projects, and working in junior or entry-level roles.
Many employers place great value on relevant work experience, so previous experience working with data and analytics tools can be helpful. Many data scientists start their careers as data engineers or database administrators before transitioning into data science roles.
You'll want to build your skill set and experience to work in predictive analytics. Your resume may look more robust if you have demonstrable experience in the following:
Support vector machines
Several certifications are available for predictive analytics professionals, such as the Certified Analytics Professional (CAP) certification offered by INFORMS. Certificates are not always required for employment, but they can boost your resume.
Here are some more certifications that you might like to consider.
Microsoft Certified: Data Analyst Associate
Associate Certified Analytics Professional
IIBA Certification in Business Data Analytics (CBDA)
Job outlook and average salary
The job outlook for data scientists is extremely positive, with demand for data professionals expected to grow significantly in the coming years. Data science is number three in Glassdoor’s ‘50 Best Jobs in America for 2022’ . The average annual salary for a predictive analytics data scientist is $85,556 .
A professional certificate in predictive analytics will allow you to hone your skills in this growing field. You can learn how to use data to make predictions about future trends and behaviors and how to communicate your findings to decision-makers.
You might like to consider the Introduction to Predictive Modeling course offered by the University of Minnesota. By taking this course and getting exposure to this type of data science, you can better understand whether this is a career route you’d like to take. You’ll also enhance your resume, putting yourself in a better position to take advantage of the many emerging job opportunities in this exciting field.
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course ...
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Predictive Analytics, Data Preparation, Time Series Forecasting, Linear Regression
Glassdoor. “50 Best Jobs in America for 2022, https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm.” Accessed November 9, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.