Predictive Business Analytics | 5 mins read

What Are Predictive Business Analytics?

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Lauren Christiansen

By Lauren Christiansen

The Inside Scoop on Predictive Business Analytics

Wouldn't it be great if business owners could predict the future? They could determine the up and coming market trends and pinpoint exactly where to find customers. They could even invest in the most effective technologies to help streamline operations. 50 years ago, this was a pipe dream. The best restaurant owners could do was use transaction data and instinct to implement best practices. Their only saving grace was that every other restaurant was in the same boat.

Nowadays, restaurant owners have access to a plethora of incredible tools and computer algorithms. With machine learning and a good data analyst, it's now entirely possible to predict future outcomes. Predictive analytics is the most critical type of business analytics. Industries across the United States are using predictive algorithms to optimize internal operations. This enables restaurant owners to build good business intelligence and maintain a competitive edge.

Predictive analytics is the use of big data, analytics software, and machine learning to pinpoint the likelihood of future events. Now, restaurants can hire a data scientist to drill down into historical data relating to everything from the supply chain to market trends. As a result, restaurants can improve existing business processes to align with future needs.

Importance of Predictive Business Analytics

Analytics uses are endless. Of all the types, restaurants aspire to use predictive platforms most often. What better way is there to get ahead of the competition than to know the future? With the right set of analytics models and analysis data, the opportunities are enormous. Read ahead for the top benefits of statistical analysis and predictive models.

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1. Predictive Business Analytics Forecasts Fraud and Theft

Cyber security threats are on the rise, and consumer data is more at risk than ever before. Even the restaurant industry needs to use effective security tactics to maintain POS system data. Furthermore, hackers can access sensitive company information, such as tax documents or credit card information.

Employee theft is also a big problem, and it impacts everything from inventory to cash flow.
Data analysis and artificial intelligence can help all industries mitigate fraud and theft. Effective predictive modeling looks for real-time anomalies which might specify fraud and persistent threats.

While industries relating to financial services tend to use PA more often, restaurants still take advantage of these tools. As a result, they have saved millions of dollars, protected their reputation, and avoided lawsuits.

2. Predictive Business Analytics Improves Marketing Campaigns

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The most common use of predictive analysis is to forecast consumer preferences. How else can a restaurant curate a menu or align with food trends? This doesn't just have to be about what consumers will buy, either. Predictive analytics models can even drill down into social media data to look for patterns in customer comments. This can help train employees more effectively, and even boost efficiency. It will also enhance the quality of customer service and encourage patrons to come back.

Moreover, data predictive analytics helps improve marketing campaigns. Data scientists can drill down into previous campaigns and analyze their performance. Restaurants can then use the ideas that work and scratch the ones that don't. This will help boost sales and reach more customers. For example, grocery chains use predictive analytics to customize coupons and send them to customers. As a result, they have increased sales and customer loyalty.

3. Predictive Analytics Optimizes Internal Operations

If a restaurant wants customers to be happy, they need to first streamline internal operations. This requires pinpointing the inefficiencies and bottlenecks that are causing problems. For example, chronic absenteeism and inefficient inventory management processes create unhappy customers. The quality of customer service diminishes and patrons can't order the foods they love.

With predictive analytics tools, restaurants can identify any problem areas and eliminate them. For example, regular scheduling errors create absenteeism, which creates angry customers. This is a chain effect that ends up hurting both morale and profit. Analytics tools can identify the root cause of scheduling problems and forecast future scheduling bottlenecks. A restaurant owner will then make a series of data-driven decisions. This may include hiring more people, firing an incompetent manager, or investing in a new scheduling tool.

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4. Predictive Analytics Reduces Risk

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Every restaurant faces risk from the moment it opens. Some risks are worth taking, others need to be avoided at all costs. But what if there was a tool that could determine which risks are worth the expense and resources?

Predictive analytics tools can use deep learning and artificial intelligence to look at all types of risks. This can enable restaurant owners to optimize decision-making and allocate resources more effectively. It can also open up new revenue streams that may have been unnoticeable before. Advanced analytics builds confidence in decision-making because there is an entirely accurate risk-reward analysis.

Predictive Business Analytics Examples

Industries across the spectrum use data science and PA tools in numerous ways. From finance to healthcare to hospitality, this technology optimizes decision-making. Some of the top use cases include -

  • Aerospace - Forecast maintenance needs to improve reliability and maximize fuel usage. This saves money and improves the effectiveness of operating machinery.
  • Automotive - Drills down into failure rates and effectiveness of parts to optimize manufacturing. Analyzes driver behavior to create improved assistance systems and even self-driving vehicles.
  • Energy - Predicts demand and supply ratios for the long-term. Discerns how weather impacts equipment and analyzes failure rates. Also drills down into data to improve compliance with regulations.
  • Financial Services - Banks create credit risk, analytics models. Predicts market trends to create new policies. Also drills down to discern how regulations and economic conditions impact the industry.
  • Manufacturing - Utilizes predictive tools to assess equipment failure rates and improve the effectiveness of machinery. Helps to save money and ensure operational effectiveness.
  • Law Enforcement - Utilizes crime rate information to identify areas that may need more attention and intervention. Also helps to provide additional protection during high crime seasons.
  • Retailers - Monitors real-time online customer data to see whether offering another product or discount will improve sales.

Key Takeaways of Predictive Business Analytics

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In conclusion, here is what to know about predictive analytics -

  • A predictive model enables restaurant owners to minimize fraud and theft. They can also use predictive analytics to improve marketing campaigns and reach more customers.
  • Restaurants are using predictive analytics to optimize internal operations and reduce risks.
  • Data management tools are used across all industries. This includes aerospace, automotive, energy, financial services, manufacturing, law enforcement, and retailers.

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