Predictive analytics produces statistics and data modelling leveraged by businesses to make predictions. These predictions are used to make data-driven decisions based on heuristics and patterns from previous events. Predictive analytics can be used to make more effective, informed decisions on future cyber-attacks, incidents, or events.
History of Predictive Analytics
It’s hard to believe that predictive analytics spans back to the late 1600s when Lloyd's of London used data from past events to underwrite insurance for delivery ships. Lloyd's would identify risks from past voyages to determine a shipping company’s premiums. After the success of Lloyd’s, predictive analytics became a standard for every insurance provider.
Fast forward to the mid-1900s, predictive analytics was used in World War II to determine risks for battalions and enemy combat strategies. Arnold Daniels used predictive analytics to limit casualties, and his battalion finished the war with no casualties. Upon his return home, he created the first predictive index, which would later be used in psychometric testing.
In the 1970s, predictive analytics was used to improve business functionality and optimise operations. Businesses were introduced to the first mainframes, which could take large datasets and use them to calculate predictions for marketing, finance, and trading.
Mathematical models were introduced in the 1990s that used scoring methods to weigh decisions for risk analysis. Predictive analytics was introduced to managers to improve operational efficiency. Weighting techniques provided simple values so that anyone could make decisions based on simplified output.
Now, predictive analytics and machine learning are tied together to optimise results. The wide range of programming options, cloud computing, machine learning, and artificial intelligence offer improved accuracy of predictive analytics. It’s used in almost every industry for various applications, including finance, marketing, customer service, cybersecurity, and human resources.
What Industries Use Predictive Analytics?
Most people know that predictive analysis is often used in financial industries but also in operations, cybersecurity and other information technology fields. It helps many organisations increase productivity, reduce costs, and safeguard critical data.
Here's how a few IT industries use predictive analytics:
- Fraud detection: Finance and other cyber-operations use predictive analytics to determine fraudulent activity. Stolen credit cards, malicious bot activity, insider threats, and digital threats create anomalous activity that can be detected using predictive analytics. Detecting abnormalities help protect customer identities and financial data and protects organisations from revenue-impacting malicious activity.
- Marketing optimisation: In competitive markets, predictive analytics give organisations insight into what sells well, what will sell well in the future, and what can be improved to sell even more. It attracts customers, keeps them interested in products, upsells additional products, and helps build long-term customer relationships.
- Improve operations: Determining the accurate inventory affects revenue and profits, and predictive analysis can determine the number of products to purchase and keep stocked for optimal profits. Hotels and airlines use predictive analytics to determine prices based on past public travel habits.
- Reduce operational risks: Predictive analytics lowers the risk of fraud, mainly in the financial industry, but it can also lower risks for mortgage lenders and creditors. It determines the best recipients for loans and helps work with credit scores to assess high-risk recipients so that lenders can avoid defaults on loans.
- Community safety: Governments use predictive analytics to identify future growth to ensure residents' safety. Similarly, utilities use predictive analytics to identify anomalies that could result in long-term outages with machinery and the need to dispatch technicians to fix issues.
Benefits of Predictive Analytics
It might seem like predictive analytics are only useful for large financial institutions, but it can be used in many applications across numerous industries. Predictive analytics offer several benefits that businesses of all sizes can leverage. A few benefits include:
- Scalability: It’s possible to have data science applications across multiple departments. A good predictive analytics platform lets businesses scale their various decision-making tools using new data models that train algorithms with more recent heuristics. New data models mean that an organisation's decision-making is not limited to a single dataset.
- Speed: Creating your own data model is time-consuming and delays the deployment of applications, but a predictive analytics platform gives businesses the speed to deploy pre-built effective data models almost immediately. Speedy deployment reduces the time it takes for businesses to make and execute on data-driven decisions, thus increasing overall profitability and revenue.
- Simplicity: Not every business has data scientists and administrators to create and maintain an on-premises predictive analytics application, so cloud platforms offer technical capabilities under one dashboard. Administrators can deploy pre-built data models with a platform that provides the framework and machine learning out-of-the-box.
- Increase in revenue: Whether it’s improvement in customer retention or optimising sales, predictive analytics has a positive impact on revenue and profitability. It can be a driving force for businesses to propel from startup to competitive midsize to large enterprise.
How It Works
Without predictive analytics, organisations would need to test the market to determine the best outcome for future services and products. Test marketing is costly, typically taking several attempts to land on the right sales promotion. With predictive analytics, organisations can make data-driven decisions based on past sales performance or past patterns to determine future outcomes.
Working with predictive analytics requires organisations to have large datasets to feed artificial intelligence (AI) and machine learning (ML) algorithms. AI and ML technologies use large amounts of data to provide outcomes. The more data offered to an algorithm, the more accurate the outcomes. Data models can be created internally by data scientists, or they can be pre-built and provided by the predictive analytics platform.
Before working with a specific dataset, data scientists might perform data mining to evaluate a dataset and determine if it’s viable for a specific application. Another option is to use analysis tools to work with large datasets and determine if the data is accurate for use with machine learning and predictive analytics.
A few ways predictive analytics works with specific applications:
- Determine future weather patterns and alert communities of upcoming destructive weather events.
- Develop marketing campaigns for predictable sales focused on a specific seasonal product.
- Auto-complete text-to-speech messages on smartphones.
- Produce customer service applications to help users find answers to common questions.
- Create trades for a specific stock based on past performance.
- Identify sales opportunities for seasonal products based on past customer interests.
All these examples use past performance and data to determine future performance. It’s what makes predictive analytics a powerful tool for businesses to optimise sales and revenue.
Predictive Analytics Uses
Every organisation has at least one predictive analytics use case. However, its use is prevalent in specific industries such as marketing, finance, customer service, and operations. Here are a few common use cases:
- Forecasting in the supply chain: Optimisation of stock and determining supply chain issues are two common use cases for predictive analytics. It helps organisations identify any interruptions to pivot to other vendors and avoid disruption of productivity. Predictive analytics reduces the risk of negative revenue impact from unforeseen changes in the supply chain.
- Credit scoring and loan applications: The credit scoring system has been around for decades, but predictive analytics provides a more accurate risk assessment based on a specific individual or business history. It processes an individual's past payment and lending history to determine if they can make payments and should be trusted with a line of credit.
- Insurance underwriting: Past claims patterns and costs can be used in predictive analytics to determine future insurance rates and the risk of covering a specific business or individual.
- Marketing: Economy changes, past buying habits and consumer-interest data feed into predictive analytics decisions for future marketing opportunities. It helps organisations determine promotions on a specific product or potential sales opportunities for products within a particular location.
Predictive Analytics Models and Techniques
Data scientists and the developers behind predictive analytics tools use three main techniques to produce results. The outcome from predictive analytics tools can then be made to make decisions. The three techniques are:
- Decision trees: For simple models where several decision-making options are available, a decision tree displays branches for each possible choice, where each leaf represents a decision. A decision tree helps organisations determine what will happen based on each branch (decision option) and the results from each branch’s decision.
- Regression: Statistical analysis uses regression to input several values into an algorithm and output results based on results. It’s used in pricing and determination of marketing efforts for best results.
- Neural Networks: Just like the name suggests, neural networks are a form of analytics that attempts to mimic how the human brain works. Neural networks take complex data and use artificial intelligence to provide information to the reader. It’s used to find relationships between input and a dataset. Neural networks are better for predicting outcomes based on specific inputs such as products and economies, buying habits, demographics and seasons.
How Predictive Analytics is Used Today
Because computing is much more powerful than just a decade ago, predictive analytics has seen several improvements. These advancements make predictive analytics more affordable to businesses, easier to use for all stakeholders, and offer various usage options. The cloud enables businesses to work with predictive analytics platforms without supporting and building their own environments.
As predictive analytics platforms make it more affordable, they are used in more businesses to give them a competitive advantage. A few ways predictive analytics is used today:
- Banking and finance: With billions of dollars at stake, banks and financial institutions use predictive analytics to reduce fraud, identify high-risk borrowers and maximise customer sales for better profits.
- Retail: Past buying habits can help determine future buying behaviour. Predictive analytics defines buying behaviour so retailers can up-sell additional products, optimise product placement and analyse potential promotions.
- Oil, Gas and other Utilities: Utility companies must mitigate risks of failure to avoid catastrophic incidents for a community. They use predictive analytics to determine when machinery needs maintenance or when sensors detect anomalies that could indicate future downtime.
- Governments: The public sector uses predictive analytics to identify city growth and the need for improvements in roads, housing, businesses and other community necessities to support local populations.
- Health insurance: To avoid fraud and risky claimants, insurance agencies use predictive analytics to warn customers of upcoming events, determine script and medical costs, and identify beneficial treatments and their costs.
- Manufacturing: Quality control is integral to manufacturing to avoid defects. Predictive analytics can help identify anomalies, optimise parts distribution, find supply chain opportunities and detect issues with machinery.
How Your Business Can Use Predictive Analytics
Numerous businesses reap benefits by leveraging predictive analytics platforms. However, many don't realise the full spectrum of opportunities until they buy into a platform offering optimisation and operational improvements. Typically, businesses start with predictive analytics to improve customer service and experience. However, they also offer a much better user experience, increased customer satisfaction, loyalty, and brand reputation.
As executives continue experimenting with predictive analytics, they realise its numerous opportunities and implications. Businesses want to know that predictive analytics can positively impact revenue and profitability and it can optimise promotions, sales, marketing, and operations. With optimised business procedures, businesses can be more productive and build better customer retention.
Predictive analytics is mainly used in marketing and advertising. It can catapult sales and turn a small business into a competitive midsize enterprise. It can mean the difference between growth and stagnant sales because it can predict seasonal purchases so businesses can optimise their promotional strategies for increased revenue. Predictive analytics also saves companies money so that they can optimise their operational procedures, including security and information technology systems.
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