How Are Predictive Analytics Used in Business?

how are predictive analytics used in business

Most people have wondered what the future held for them and their families. If there was a time machine that could make predictions, many individuals would take whatever actions possible now to improve their future outcomes. Because small choices can prove consequential in the future, better choices today promote a better outcome tomorrow.

Just as one simple and seemingly inconsequential decision can negatively impact a person for the rest of his/her life, innocuous business decisions can also affect a company's future growth potential.

Though a time machine has not been developed, businesses now have the data-driven analytics software and artificial intelligence available to use historical information to predict future events.Organizations use predictive analytics and machine learning to improve current decision-making today, resulting in an improved outlook for tomorrow.

Read ahead for a comprehensive overview of analytics tools and learning techniques and how they help businesses and the supply chain.

Predictive Analytics and its Uses

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Predictive analytics and predictive modeling is a subset of business analytics and analytics models that use historical and real-time data science to pinpoint patterns that forecast future events.

Most organizations prioritize predictive analytics over other forms of business intelligence because it allows involved stakeholders to make the most informed decisions regarding customer behavior, finances, sales strategies, and market trends. If utilized properly, predictive analytics can be the foundation that pinpoints opportunities for growth and standardizes best practices.

Predictive analytics modeling is not contained to one inflexible genre. Different categories of analytics help with different situations, depending on the type of industry and business needs.
These types of PBA and data mining encompass-

  • Forecast Models - Forecast models and data analytics produces numerical values in historical information to pinpoint patterns that are usually unnoticeable. This allows analysts to use big data and neural networks to better estimate future values and patterns.
  • Classification Models - This is a form of predictive analytics help categorize data based on historical information. Analysts employ classification models to answer complex questions with a comprehensive analysis.
  • Outlier Models - An outlier data model drills down into abnormal information within a dataset to pinpoint a problem or risk. Looking for deviations from the norm through proper data management helps analysts forecast fraud or waste that would otherwise be unnoticeable.
  • Time Series Model - The time series model focuses on data sets that involve time. Different data points are utilized from the previous year's information to forecast patterns within a particular time frame.
  • Clustering Model - This predictive analytics software model takes information and classifies it based on commonalities such as demographics, location, etc. It is particularly employed as part of marketing analytics to improve and fine-tune marketing campaigns for targeted segments of customers.
Organizations across the country are using predictive analytics to improve decision-making, increase sales, enhance the customer experience, optimize efficiency, and generate new leads. Real-world use cases of different industries employing analytics include-

1. Predict Purchasing Behavior

Retailers and other types of businesses have used predictive analysis to learn more about their customers. This helps to improve marketing campaigns, optimize customer outreach, and ultimately increase sales.

In 2004, Walmart utilized predictive analytics tools to drill down into customer transactions and learn more about customer purchasing decisions during certain periods.

Many of their findings were surprising and would have been unnoticeable had they not employed PBA. For instance, they found that strawberry pop tarts were one of the highest selling items right before a hurricane hit a certain region of the country.

By utilizing predictive analytics to dig deep into customer purchasing decisions, companies can create personalized recommendations, enhance analytics marketing, cross sell items, curate coupons, and optimized marketing campaigns. As a result of employing this form of advanced analytics, sales and customer loyalty increases.

2. Improve Healthcare Decisions

The healthcare industry employs PBA to improve diagnoses and forecast health-based outcomes for many of their patients.

For example, one hospital used a model to predict negative health conditions in senior citizens, resulting in reduced hospitalizations and ER visits by this segment of individuals.

As technology and modeling capabilities continue to evolve, more healthcare facilities are employing PBA to improve preventative measures, resulting in saved money and optimized patient care.

3. Curate Marketing Content

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Digital entertainment has benefited tremendously from utilizing predictive analytics to improve viewer experiences.

For example, Netflix collects current and historical customer data to suggest shows to viewers and maintain customer satisfaction with their brand. They even were able to create the show House of Cards because of analyzing collected data that showed user preferences.

Other organizations also collect relevant customer data to improve experiences, such as preferred customer preferences, search keywords, preferred purchasing times, and typical dates when an item is purchased.

4. Improve Maintenance

Predictive analytics is often utilized by manufacturers to forecast equipment breakdowns and maintenance needs.

This type of software predictive analytics can prevent an organization from wasting money and resources before a disruption even occurs. For example, analytics may notify a manufacturer that a conveyor belt is due for repair.

In turn, the organization can use preventative maintenance rather than wasting money on a brand-new belt unnecessarily. By utilizing large quantities of data to pinpoint inefficiencies or breakdowns, manufacturers can save money and optimize efficiency.

Key Takeaways

In conclusion, here are the key takeaways to remember about predictive analytics in business-

  • Predictive analytics uses historical and current information to pinpoint patterns and forecast future events. This helps organizations save money, time, resources, and improve marketing campaigns.
  • There are several different categories of a data predictive model, including-forecast models, classification models, outlier models, time series models, and clustering models. Different businesses use different categories of predictive analytics predictive models, depending on the situation and key objectives.
  • Uses of predictive analytics models include predicting customer purchasing behavior, improving healthcare decisions, curating marketing content, and improving maintenance for manufacturing companies.