How Can Predictive Analytics Be Used In Business?

Predictive Analytics and its Uses

Predictive analytics has been around since the 1940s after computer usage grew among government agencies. Yet, it's only now that analytics tools are well-known and leveraged throughout the restaurant industry. Forecasting the future may seem like a pipe dream but it's possible due to technological advancements. With machine learning, artificial intelligence, and neural networks, analysts can predict future outcomes. As people can guess, organizations across the country are ecstatic at the many opportunities this provides.

Predictive modeling utilizes historical and real-time information to identify patterns in data sets. Analysts use these patterns to predict patterns of future events and then outline specific recommendations. With a predictive model, restaurants can forecast customer preferences, identify root causes of problems, and assess potential market trends. Managers can take these insights to optimize decision-making across the supply chain. This provides a competitive edge, which is critical when other restaurants also have access to analytics software.

Types of Predictive Analytics

To better understand how predictive models work, it helps to know the various types. These include -

• Forecast Models - Calculates the values of new information based on findings from historical information. For example, a restaurant can calculate how much inventory to order for a peak season.
• Classification Models - Categorizes data based on historical information. Industries such as financial services tend to use classification models to generate a broad analysis for a particular business question.
• Outliers Models - Utilizes any data that deviates from the norm to help predict fraud and save money. Banks use outliers' models to mitigate credit card theft for their customers.
• Time Series Models - Utilizes data from previous years to identify trends within a specific time period. Restaurants may use time series models to predict sales during the summer months (using data from the previous summer).

Most Popular Predictive Models: Decision trees, regression models, and neural networks are the most commonly used precictive analytics models. Decision trees enable users to make a decision about some type of process. Regression models analyze the relationship between an independent and dependent variable. Neural networks mimic how the human brain works. They are trained by a data set to assess time-series predictions, outliers, and natural language understanding.

Common Uses of Predictive Analytics

All types of industries want to use predictive modeling because it's effective. With best practices and good data mining techniques, predictive tools have several use cases. Not only can restaurants optimize each internal business process, but they can also maximize external relationships. These insights may enable managers to improve social media marketing campaigns, increase profits, and optimize inventory management. Read ahead to learn more about how predictive analysis is used in the real world.

1. Predictive Analytics Detects Fraud

Cybersecurity threats and fraud are on the rise. Customer data is valuable to hackers, regardless of which industry it exists in. With effective analytics models, restaurants can prevent costly and damaging theft or fraud.

Predictive analytics tools analyze human behavior and assess various actions on a network. They can pinpoint any abnormalities that may result in theft or fraud, and then alert an authorized individual. This can help prevent a ruined reputation, bankruptcy, and litigation.

2. Predictive Analytics Improves Marketing Campaigns

A predictive model uses historical data to forecast long-term customer behavior and purchasing patterns. It can also use big data to identify opportunities to cross-sell items or promote a service. This ensures restaurants can offer the right items at the right times to the right customers.

Furthermore, restaurants can use data science to analyze promotions and social media campaigns to see what worked or didn't. This helps to customize campaigns to align with consumer needs and increase sales. It also improves customer satisfaction and encourages patrons to return.

How to Use PA to Improve Marketing:

• Analyze and forecast seasonal customer behavior; particularly for online sales
  
• Target the most profitable products to customers most likely to purchase them
  
• Conduct 'what if' scenarios for customer purchasing patterns in the event Plan A doesn't work out
  
• Create more effective marketing and advertising strategies
  
• Learn and utilize the best repeat strategies for increasing business
  
• Never forget to prioritize customers

3. Predictive Analytics Streamlines Operations

With good business intelligence, analysts can assess how effective certain areas of the supply chain are. This includes everything from inventory management to cash flow to employee performance. Managers can use these insights to allocate resources properly and save time/money.

For example, a restaurant may use predictive analytics to forecast how many customers will come in on New Year's Day. Managers can then optimize scheduling and reorder supplies to align with customer needs on that day. Using predictive analytics to plan will help restaurants stay organized and operate effectively.

4. Predictive Analytics Minimizes Risk

Predictive analytics can help businesses assess and mitigate both good and bad risks. For example, banks use credit scores to assess how likely it is that a customer will default on payment. Restaurants can use business analytics to determine whether it's worth it to open a new store location.

In short, managers can make sure they capitalize on safe opportunities that result in higher profits. At the same time, they can avoid those risks that lead to wasted resources, time, and effort. This helps improve a restaurant's reputation, increase brand loyalty, and save time and money.

7 Types of Risks That Businesses Monitor: Critical business risks include economic risk, compliance risk, security and fraud risk, financial risk, reputation risk, operational risk, and competition risk. Many of these risks affect one another. For example, a security breach can create a reputation risk and a competition risk. Restaurants need a qualified analyst to ensure all risks and risk overlaps are monitored.

Key Takeaways of How Are Predictive Analytics Used in Business

In conclusion, here is how using predictive tools can streamline internal business processes and increase profit -

• An analytics platform uses historical and real-time data to make predictions. These insights enable owners to optimize decision-making and streamline operations.
• There are 4 types of predictive analytics models. These include forecast models, classification models, outliers models, and time series models.
• Predictive analytics uses are numerous. Some use cases include fraud detection, marketing campaign optimization, a more streamlined operation, and risk minimization.