Analytics is an important part of managing a business today. There are many types of analytics techniques, with predictive and prescriptive analytics being two key ones. But what is predictive analytics, and what is it used for?
Predictive Analytics Today describes predictive analytics as “The branch of advanced analytics which is used to make predictions about unknown future events.” It “uses many techniques from data mining, statistics, modeling, machine learning and artificial intelligence to analyze current data to make predictions about the future.”
Using patterns found in historical data, it’s possible to identify risks and opportunities for the future. Organizations that use this form of data analysis can anticipate future outcomes, identify risk and are better positioned to make decisions.
Analyzing Structured and Unstructured Data
This type of analytics can be performed on both structured data and unstructured data. Structured data, things like age, gender and sales figures, can be used to track customer trends.
Unstructured data, such as notes from call center agents, or customer feedback can be used to identify customer sentiment and build predictive models.
Predictive vs Prescriptive Analytics
Predictive analytics is not to be confused with prescriptive analytics. Nicole Fallon of Business News Daily describes prescriptive analytics as taking “A more technological approach,” and offering a “Deeper look into the ‘what’ and ‘why’ of a potential future outcome.”
Prescriptive analytics takes past data and uses it for projections, but it allows analysts to tweak variables to determine what changes they’d need to make to achieve a specific outcome.
Predictive and prescriptive analytics are often used in tandem. Fallon explains, “Predictive analytics helps find potential outcomes, while prescriptive analytics looks at those outcomes and finds even more paths of options to consider.”
What Are Predictive Analytics Used For?
Predictive analytics can be used to tweak and test processes across a variety of industries. Earl Sires, a digital content marketer at Rapid Insight explains, “Many industries use predictive analytics as a core part of their strategy.”
Some common use cases include:
- Fundraising
- Health care
- Marketing
- Insurance
- Supply chain management
- Risk Management
- Predictive Analytics in Fundraising
Fundraising requires building connections and relationships with potential donors. Sires explains, “Predictive modeling allows you to plan your fundraising calendar strategically.” This means fundraisers can get the right communications out to prospective donors at a time when they’re likely to respond positively.
Predictive Analytics in Health Care
Health care workers can use data in a variety of ways to improve the efficiency of the service they provide. Effective use of patient data can also help improve patient outcomes.
Sires says, “Outside factors, known as Social Determinants, can play a greater role in your patient’s health than anything that happens within the hospital doors.”
Predictive Analytics in Marketing
Marketing is an industry that relies heavily on metrics. Marketers track clicks, engagement, views and other behaviors. Predictive analytics can take that huge database of information and score prospects based on how likely they are to buy a product. This gives brands an idea of where they should prioritize their outreach to get the biggest return on investment.
Predictive Analytics in Insurance
The insurance industry, like the marketing industry, is driven by statistics. Accident reports and historic data are used to judge the risk factors for individual clients. Predictive analytics can help with processing claims and also preventing fraud.
Jason Rodriguez of Instant Insight notes predictive analytics could reduce the need for professional oversight in areas, such as loss handling and initial triage. Fraud is a huge issue for insurance companies, and models that highlight suspected fraud could save time and money for underwriters.
Predictive Analytics in Supply Chain Management
The issue of supply chain management has become increasingly important since the start of the pandemic. Sires explains that predictive analytics can be used to “Model different risk factors to see how they impact your supply chain and incorporate information from disparate sites or sources into one model to get the most accurate, relevant picture of your operation.”
The information from a predictive model can then be used to prioritize shipments or guide the creation of prescriptive models. Supply chains can be complex and have many points of failure. By examining each of these points in turn, organizations can make their supply chains more robust and be better positioned to weather any disruptions that do occur.
Predictive Analytics in Fund Management
Deloitte provides a case study of a major financial organization that transformed from being a risk-averse pension fund into a risk management organization.
The company’s old systems weren’t capable of the new, more complex models required for the new investments. Changing over an entire company’s computer systems was a large undertaking. The systems had been in place for 18 years, and the whole operation depended on them. Using predictive project analytics (PPA), the company was able to run models to determine whether the safest course of action was to update the systems all at once or step-by-step.
By following the suggestions given by the PPA model, the company was able to complete its update project ahead of schedule and under budget. The transition to new systems was a success.
What Sort of Problems Are Predictive Analytics Best Suited for?
Predictive and prescriptive analytics work best when used together. It’s important to use the right tool for each problem. River Logic explains that predictive analytics is generally used to identify short- or medium-term trends.
Some examples of when to apply predictive analytics include:
- Sales trends
- Short-term risk analysis
- Inventory control
- Cash flow
- Maintenance requirements
- Customer churn
Prescriptive analytics models are more complex to build, but they allow an organization to explore multiple what-if scenarios. Meanwhile, predictive analytics models focus on a more narrow set of parameters. This means these models are easier to build and can provide a quick overview of a situation.
Predictive analytics helps bring clarity and objectivity to decision-making. It can inform major spending or policy decisions in situations where managers may otherwise be prone to wishful thinking. Models cannot predict the future with 100% accuracy, but they can assist with making educated guesses.
Artificial Intelligence and Predictive Analytics
In IBM’s report, “A Business Guide to Modern Prescriptive Analytics,” the tech company emphasizes the importance of artificial intelligence for informing business decisions.
The report states, “The scope of predictive analytics has broadened. Breakthroughs in machine learning and deep learning have opened up opportunities to use predictive models in areas that have been impractical for most business investments.”
IBM explains that today, technology is no longer an obstacle to adopting AI for analytics techniques. The availability of better tools means the barrier to entry is much lower than it was a few years ago. Data scientists need to define the use cases where modern predictive analytics will bring the best value to the organization.
The report gives the example of using predictive models for product recommendations and “next best action” models for sales and marketing teams. It also suggests the use of predictive technology for contact center automation.
Using artificial intelligence to improve these models means they can be deployed quickly and effectively and made a part of an organization’s day-to-day operations.
How Predictive Analytics Work
Predictive models can have varying degrees of complexity, but the principle remains the same. They use known results to develop models (or train AI) to predict future values.
SAS gives the following explanation of how models work: “The model gives predictions that represent the probability of a target variable, based on the estimated significance from a set of input variables.”
There are many modeling techniques, but three of the most common are decision trees, regression and neural networks.
Decision Trees
Decision trees partition data into subsets. Each subset is based on categories of input variables. A decision tree has branches representing different choices and leaves representing classifications or decisions.
Decision trees are a popular choice for modeling because they’re easy to understand and provide a visual representation of the information. In addition, they handle missing values quite well, so they can be useful for building quick, simple models when you don’t have all the information required for a more complex model.
Regression
The two types of regression models used in predictive analytics are linear regression and logistic regression. These types of analysis are useful for estimating the relationships among variables. Regression models are intended for use on continuous data, especially if that data can be estimated to follow a normal distribution.
One common application of regression models is to predict how the price of a product might affect sales. More sophisticated models, such as multiple regression, can model the outcome of situations that have multiple variables.
Neural Networks
Models that are incredibly complex and have multiple variables lend themselves to the application of neural networks. These models can handle nonlinear relationships in data. Neural networks use pattern recognition and may also apply some AI to model the parameters. For a neural network to be effective, it will most likely require a significant amount of training data.
Other Models for Predictive Analysis
The above are just a few of the most significant models and techniques used in predictive analysis. SAS lists several other techniques, including Bayesian methods, gradient boosting and ensemble models.
While statisticians use a variety of techniques when processing data, the three techniques listed above cover the bulk of use cases in the world of business.
The Pros and Cons of Predictive Analytics
Predictive analytics is useful for scenarios where a business needs to make predictions about outcomes but does not have a lot of information available.
Investopedia cites cost reduction as a major benefit to the use of predictive analytics, saying, “There is a significant impact to cost reduction when models are used.” By applying analytics techniques, businesses can “Determine the likelihood of success or failure of a product before it launches.”
Predictive analytics can also be used for budgeting. For example, a company can set aside capital for production improvements before the manufacturing process begins. There are some scenarios where the use of predictive analytics is either discouraged or even legally restricted. This is particularly true in the world of finance and insurance. In some cases, predictive models result in statistical discrimination against protected groups. For example, a model may result in one gender paying more for car insurance than another or denying a loan to a person based on their ethnicity alone.
These issues are not new. Khristopher J Brooks writes about the history of redlining in finance for CBS News. Some political figures attempted to blame the 2008 housing crash in part on the practice of limiting loans to people (often people of color) based on the areas in which they lived.
The practice has been going on since the 1930s, and while home lending discrimination is now illegal, similar applications of statistical models persisted for a long time in other areas, such as insurance.
Ethical Use of Data in Modeling
One challenge that data analysts face is creating accurate models without using discriminatory practices. In many parts of the world, the law now prohibits companies from discriminating against customers based on protected characteristics.
Many data analysts simply remove information, such as gender and race, from their data. George Cevora, PhD., from the University of Cambridge, writes in Towards Data Science that it can be more challenging than first expected to create accurate, discrimination-free models. He states that this simplistic approach “Also happens to be the worst at actually addressing the bias.”
Cevora explains, “The information about any characteristic of an individual is to some degree contained in other characteristics about the individual.” He gives the example of a model that attempts to predict absenteeism based on the age of an employee. Discriminating based on age would be illegal. However, given a large enough dataset, an AI model could predict the age range of an employee based on their name, lifestyle habits, BMI and even height. These variables are known as proxy variables because it’s possible to infer other characteristics from them.
Some analysts attempt to look for signs of discrimination in their models and correct it once the model has been run. Another option is to identify and alter proxy variables to make discrimination less likely.
The Use of Predictive Analytics Is Set to Grow
Despite the ethical challenges associated with putting decision-making and predictions into the hands of computer models, the practice is likely to continue.
IBM’s report stresses the value of predictive analytics for helping organizations scale, grow and make accurate decisions. The companies that embrace the opportunities offered by predictive analytics will be best positioned to gain a competitive advantage in the coming years.
Jobs relating to predictive analytics are growing at a rapid rate. According to the Bureau of Labor Statistics, demand for Operations Research Analysts is set to increase by 25% between 2020 and 2030. The data science profession as a whole is also set to grow both in terms of demand and wages. The field is no longer reserved for pure science and mathematicians. It has applicability in almost every area of business.
Those looking to enter the field today have the chance to explore new areas of machine learning, big data and artificial intelligence. Data analysis is a field offering lifelong learning opportunities and new challenges.
Future Trends for Data Analysis
The increasing availability of powerful and flexible toolkits for predictive analytics means even those who are not highly trained statisticians can take advantage of predictive models.
Data is also more readily available than ever before. Businesses don’t have to limit themselves to the data they collect themselves. Bernhard Schroeder writes about the rise of the data marketplace in Forbes. He explains, “These platforms typically focus on the transactional aspect of buying and selling data.” Organizations can work with data marketplaces either for a one-way exchange of data or for joint value propositions.
Companies with access to significant consumer data are now faced with an interesting question, “Could they exchange that data with another company that is not a competitor so that both could benefit?”
Thanks to the power of predictive and prescriptive analytics, such sharing of data is now an appealing option. We’re in a Wild West era when it comes to data processing. There are many questions about the ethics, privacy and the responsibility of data scientists and organizations to consider. It’s clear, however, that the potential benefits of predictive analytics should not be ignored.
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