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Many enterprises can benefit from using predictive modeling. As the technology gets more accurate, easier to use and cheaper, the benefits of this type of analytics will only continue to increase.
According to Allied Market Research, the global predictive analytics market size reached $7.3 billion in 2019 and will increase to $35 billion by 2027, a compound annual growth rate of 22%.
With such a strong market, many enterprises are looking at whether predictive analytics use cases fit within their business models.
What is predictive modeling?
Predictive modeling, or predictive analytics, uses standard statistics, machine learning or even artificial intelligence to predict what's going to happen next by looking at previous patterns in data.
One of the best-known and oldest examples of predictive models is weather forecasting. It's useful to know when it's going to rain or be sunny, or if your house is in the path of a hurricane.
Predictive models are also used to create election forecasts, predict the spread of diseases and estimate the effects of climate change.
There are plenty of enterprise applications for predictive analytics. According to a McKinsey report, 35% of what people buy on Amazon and 75% of what people watch on Netflix are based on recommendations from predictive algorithms.
Here are the top five predictive analytics use cases for enterprises.
Predictive modeling is everywhere when it comes to consumer products and services. Every major e-commerce site uses predictive modeling in one form or another, and many off-line retailers use analytics as well in order to set the best possible prices for their products or send custom offers to potential customers.
According to a MicroStrategy report, 46% of enterprises said they've been able to create new products and revenue streams by using analytics.
And it's not just retailers using analytics. Many organizations can benefit from predictive analytics when it comes to marketing their products.
Justin Richie, data science director at Nerdery, recently worked with a pet adoption service that decided to use analytics to predict whether a particular animal was likely to get adopted.
Animals with low scores could get extra resources to help them get placed, Richie said. They began using the system at the start of the year, and not only were they able to improve adoption rates, they also found unexpected insights in the data.
"They didn't realize that animals that were fostered were more likely to be adopted than animals in shelters," he said.
After the system was implemented, some facilities brought the number of animals waiting for adoption down to zero by April. However, some of that could have been due to the pandemic, Richie said.
"I would love to take credit for that, but I can't," he said.
Marketing is one of the top predictive analytics use cases in enterprises right now, according to Dave Kuder, principal of cognitive insights and engagement in the U.S. at Deloitte.
In fact, many marketing tools and systems from third-party vendors already have advanced analytics built in.
"AI and predictive analytics is permeating your organization and you're not even necessarily aware of it," Kuder said.
2. Risk management
Large financial institutions have to deal with massive volumes of information when it comes to loan applications or requests for insurance policies. It takes time for humans to go through all this, especially in cases where industry knowledge and judgment is required, such as small business loans or insurance policies.
"There [are] tremendous increases in complexity," Kuder said.
Personal insurance, such as auto insurance or life insurance, has well-developed actuarial tables and risk formulas, and there are credit ratings for personal loans. But when it comes to enterprise customers, the situation is vastly different.
"There [are] tremendous opportunities with small business and large corporate customers," Kuder said.
For example, consider insuring two restaurants, he said. One has a single fryer, and the other has four. Intuitively, it might make sense for the restaurant with four fryers to be riskier, Kuder said. But in fact, the single-fryer restaurant is riskier, because it's more likely to be a mom-and-pop operation without the kind of risk controls you have with a larger operation.
"Understanding this can help drive significant benefit," Kuder said. "It can help you quantify risk in who should we generally assign lower premiums to and who should we assign higher premiums to, so we can be more competitive in the marketplace. This is a critical piece in the financial services space -- how do I get the right price and do it as quickly as possible."
A human being might know, based on experience, which restaurant is riskier. But today's firms need to make the decision almost instantaneously. Humans can't keep up -- AI can.
3. Fraud detection
Financial services companies, as well as retailers and other firms, also have to deal with deliberate fraud.
The latest global crime survey by PwC (formerly called PricewaterhouseCoopers) showed that fraud rates are at record highs. Fraud cost companies $42 billion and affected 47% of companies over the past 24 months. According to a recent report by LexisNexis, retail fraud was up 7.3% last year, and it's becoming more sophisticated and complex.
The use of analytics in fraud detection can help create a smooth and frictionless experience for retail customers, according to the LexisNexis report. Analytics can help authenticate customers when they first log in and then continue monitoring to spot suspicious behaviors as they happen.
A company will usually have a small team of investigators who can't go through every single potential fraud case, Kuder said.
"Your goal is to give them the most likely claims to investigate for potential fraud," he said. "That's a perfect use case for predictive models. You can scan through hundreds of thousands of claims and refer just the top 100 that are mostly likely to be fraudulent. If you give them the right ones, the team is likely to be very productive. If you give them the wrong ones, they're not going to be productive."
According to Deloitte's 2020 State of AI report, the single biggest user of analytics inside a company is the IT department. Some of that could be the IT department helping other departments set up analytics systems. There's also the use of analytics for IT operations, such as network optimization.
But the second most common department using AI applications is cybersecurity.
Attackers are getting better at evading traditional signature-based defense systems. Attacks evolve quickly and often take advantage of compromised credentials and other normally legitimate access channels.
Analytics can predict whether a new application or website is more likely to be safe or malicious or whether a particular person's behavior is suspicious.
"With employees, you're talking about user behavior analytics," said Josh Axelrod, partner/principal in advisory risk cybersecurity practice at EY. "As you begin to examine those types of behaviors, you can use analytics capabilities and machine learning to understand what is normal and what is not normal."
Analytics can also be used to spot suspicious movement of data, such as breach-related exfiltration of company secrets or sensitive customer information.
Delivery vans, trucks and ships cost money. Sometimes, a lot of money. Optimizing routing is one of the most complex tasks that a company's operations department has to tackle. And it's only part of what supply chain managers have to do. In the wake of the COVID-19 pandemic, this job got even tougher.
Last year, only 30% of supply chain managers were using predictive analytics, according to a survey by MHI, the logistics and supply chain industry association. But that's about to change -- 57% of companies that are not yet using predictive analytics plan to start in the next five years.
But there are other kinds of routes companies have to handle. For example, there's the routing of internal tasks and documents, Kuder said.
"Predictive modeling is used here as well," he said. "You want to route the most complex workflow to the most skilled practitioners."
The easier tasks can go to less experienced staffers, as well as tasks which are the least risky for the company.
Predictive modeling in healthcare
There is one more area where predictive analytics is likely to take off, but the data isn't quite in yet. And that's the use of predictive models in the healthcare sector in the wake of the COVID-19 pandemic.
According to Allied Market Research, areas impacted will include precision medicine and genomics, population health and risk scoring, and disease outbreak prediction.