Predictive Analytics
Definition, Importance, and Common Techniques
Analyze data and build analytics models to predict future outcomes
Definition
Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.
Predictive analytics is an advanced form of data analytics that attempts to answer the question, “What might happen next?” As a branch of data science for business, the growth of predictive and augmented analytics coincides with that of big data systems, where larger, broader pools of data enable increased data mining activities to provide predictive insights. Advancements in big data machine learning have also helped expand predictive analytics capabilities.
The growth of predictive and augmented analytics coincides with that of big data systems, where broader pools of data enable increased data mining activities to provide predictive insights. Advancements in big data machine learning have also helped expand predictive analytics capabilities.
Predictive analytics is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior. Organizations can use historic and current data to forecast trends and behaviors seconds, days, or years into the future with a great deal of precision.
Predictive analytics determines the likelihood of future outcomes using techniques like data mining, statistics, data modeling, artificial intelligence, and machine learning. Put simply, predictive analytics interprets an organization’s historical data to make predictions about the future. Today’s predictive analytics techniques can discover patterns in the data to identify upcoming risks and opportunities for an organization.
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science.
Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees. Some of these modeling techniques use initial predictive learning to make additional predictive insights.
Predictive analytics is one of the four key types of data analytics, and typically forecasts what will happen in the future, such as how sales will shift during different seasons or how consumers will respond to a change in price. Businesses often use predictive analytics to make data-driven decisions and optimize outcomes.
Businesses use data to understand what's happening—both now and in the future. Predictive analytics falls under the latter category. It uses historical data to predict potential future events or behaviors so companies can better position themselves in the present.
In order to calculate the future, predictive analytics relies on a number of techniques from statistics, data analytics, artificial intelligence (AI), and machine learning. Some common business applications include detecting fraud, predicting customer behavior, and forecasting demand.
Organizations need to be forward-thinking: anticipating outcomes, capitalizing on opportunities, and preventing losses. With growing volumes of data and easy-to-use software, predictive analytics is more accessible than ever, helping organizations become more proactive and increase their bottom line.
The term predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance. Predictive analytics looks at current and historical data patterns to determine if those patterns are likely to emerge again. This allows businesses and investors to adjust where they use their resources to take advantage of possible future events. Predictive analysis can also be used to improve operational efficiencies and reduce risk.
Key Takeaways
- Predictive analytics uses statistics and modeling techniques to determine future performance.
- Industries and disciplines, such as insurance and marketing, use predictive techniques to make important decisions.
- Predictive models help make weather forecasts, develop video games, translate voice-to-text messages, customer service decisions, and develop investment portfolios.
- People often confuse predictive analytics with machine learning even though the two are different disciplines.
- Types of predictive models include decision trees, regression, and neural networks.
IN THIS ARTICLE ........
Understanding Predictive Analytics
7 areas that use predictive analytics
How to get started in predictive analytics
Predictive Analytics in Today's World
What do you need to get started using predictive analytics?
Prepare for a Career in Predictive Analytics
11 Best Predictive Analytics Tools (Software Compared)
6 Top Predictive Analytics Tools
Predictive Analytics Software FAQs
Understanding Predictive Analytics
Predictive analytics is a form of technology that makes predictions about certain unknowns in the future. It draws on a series of techniques to make these determinations, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics.1 For instance, data mining involves the analysis of large sets of data to detect patterns from it. Text analysis does the same, except for large blocks of text.
Predictive models are used for all kinds of applications, including weather forecasts, creating video games, translating voice to text, customer service, and investment portfolio strategies. All of these applications use descriptive statistical models of existing data to make predictions about future data.
Predictive analytics is also useful for businesses to help them manage inventory, develop marketing strategies, and forecast sales.2 It also helps businesses survive, especially those in highly competitive industries such as health care and retail.3 Investors and financial professionals can draw on this technology to help craft investment portfolios and reduce the potential for risk.4
These models determine relationships, patterns, and structures in data that can be used to draw conclusions about how changes in the underlying processes that generate the data will change the results. Predictive models build on these descriptive models and look at past data to determine the likelihood of certain future outcomes, given current conditions or a set of expected future conditions.
More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. Why now?
- Growing volumes and types of data, and more interest in using data to produce valuable insights.
- Faster, cheaper computers.
- Easier-to-use software.
- Tougher economic conditions and a need for competitive differentiation.
With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.
What are some common predictive analytics techniques?
Potential applications for predictive analytics vary widely, as do the types of models used to power resulting insights. Determining what types of predictive analytics techniques are best for your organization starts with a clearly defined objective.
Predictive analytics models can be roughly grouped into these four types:
Regression models
Regression models estimate the strength of a relationship between variables. The model tracks how actions (independent variables) impact outcomes (dependent variables) and uses that information to predict future impact. These statistical models can be simple, with one independent variable and one dependent variable or a multiple linear regression with two or more independent variables.
A variety of regression techniques exist and can be employed depending on the application and types of variables involved. By defining the relationship between variables, organizations can perform scenario analysis, also colloquially known as ‘what-if’ analysis, to plug in new independent variables and see how they affect the outcome. Organizations might use a regression model to determine how a product’s qualities affect the likelihood of purchase. By analyzing the relationship between the color of the product and the likelihood of purchase, an organization might see a correlation between blue shirts and more sales. Because correlation doesn't equal causation, the organization might explore how other factors affect likelihood to purchase, such as size, seasonality, or product placement. They can use these insights to help with marketing efforts or product development to determine which products might perform well in the future.
Classification models
Classification models place data into categories based on historical knowledge. Classification begins with a training dataset where each piece of data has already been labeled. The classification algorithm learns the correlations between the data and labels and categorizes any new data. Some popular classification model techniques include decision trees, random forests, and text analytics. Because classification models can easily be retrained with new data, they are used in many industries. Banks often use classification models to identify fraudulent transactions. The algorithm can analyze millions of previous transactions to learn what future fraudulent transactions might look like and alert customers when activity on their account looks suspicious.
Clustering models
Clustering models place data into groups based on similar attributes. A clustering model uses a data matrix, which associates each item with relevant features. With this matrix, the algorithm will cluster together items that have the same features, identifying patterns in the data that might previously have been hidden. Organizations can use clustering models to group customers together and create more personalized targeting strategies. For example, a restaurant might cluster their customers based on location and only mail flyers to customers who live within a certain driving distance of their newest location.
Time-series models
Time series models capture data points in relation to time. Because so much of the world’s data can be modeled as a time series, time is one of the most common independent variables used in predictive analytics. A typical model might use the last year of data to analyze a metric and then predict that metric for the upcoming weeks. Tableau’s advanced analytics tools allow organizations to forecast and explore multiple scenarios without wasting time or effort. Because time is a common variable, organizations use time series analyses for a variety of applications. This model can be used for seasonality analysis, which predicts how assets are affected by certain times of the year, or trend analysis, which determines the movement of assets over time. Some practical applications include forecasting sales for the upcoming quarter, predicting the number of visitors to a store, or even determining when people are most likely to get the flu.
Other predictive analytics techniques
Often a combination of these models are used to mine the data for insights and opportunities. For example, neural networks are a set of algorithms designed to mimic the human brain and identify patterns within the data. Neural networks use a combination of regression, classification, clustering, and time series models, so they are capable of handling big data and modeling extremely complex relationships. In fact, neural networks can handle more than just text data. With deep learning techniques, they can also input images, audio, video, and more, and training on labeled datasets allows these networks to improve their accuracy. These deep learning techniques are currently being used for voice and facial recognition software, and networks can analyze facial movements to identify a person’s disposition. With information like this, organizations can potentially predict the emotions customers will feel when using certain products or services.
Why is predictive analytics important?
Predictive analytics allows organizations to be more proactive in the way they do business, detecting trends to guide informed decision-making. Organizations no longer have to rely on educated guesses because forecasts provide additional insight. The benefits of predictive analytics vary by industry, but here are some common reasons for forecasting.
- Improve profit margins. Predictive analytics can be used to forecast inventory, create pricing strategies, predict the number of customers, and even configure store layouts to maximize sales.
- Optimize marketing campaigns. Predictive analytics can unearth new customer insights and predict behaviors based on inputs, allowing organizations to tailor marketing strategies, retain valuable customers, and take advantage of cross-sell opportunities.
- Reduce risk. Predictive analytics can detect activities that are out of the ordinary — such as fraudulent transactions, corporate spying, or cyber attacks — to reduce reaction time and negative consequences.
How do I get started with predictive analytics tools?
With so many types of predictive models and potential applications, it can be difficult to know where to get started.
Four general steps for implementing a predictive analytics practice in your organization:
- Identify the business objective. Before you do anything else, clearly define the question you want predictive analytics to answer. Generate a list of queries and prioritize the questions that mean the most to your organization.
- Determine the datasets. Once you outline a list of clear objectives, determine if you have the data available to answer those queries. Make sure that the datasets are relevant, complete, and large enough for predictive modeling.
- Create processes for sharing and using insights. Any opportunities or threats you uncover will be useless if there’s not a process in place to act on those findings. Ensure proper communication channels are in place so that valuable predictions end up in the right hands.
- Choose the right software solutions. Your organization needs a platform it can depend on and tools that empower people of all skill levels to ask deeper questions of their data.
Tableau’s advanced analytics tools support time-series analysis, allowing you to run predictive analysis like forecasting within a visual analytics interface.
Tableau What-is-predictive-analytics
Predictive analytics models are designed to assess historical data, discover patterns, observe trends, and use that information to predict future trends. Popular predictive analytics models include classification, clustering, and time series models.
Classification models
Classification models fall under the branch of supervised machine learning models. These models categorize data based on historical data, describing relationships within a given dataset. For example, this model can be used to classify customers or prospects into groups for segmentation purposes. Alternatively, it can also be used to answer questions with binary outputs, such answering yes or no or true and false; popular use cases for this are fraud detection and credit risk evaluation.
Types of classification models include logistic regression, decision trees, random forest, neural networks, and Naïve Bayes.
Clustering models
Clustering models fall under unsupervised learning. They group data based on similar attributes. For example, an e-commerce site can use the model to separate customers into similar groups based on common features and develop marketing strategies for each group.
Common clustering algorithms include k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering using Gaussian Mixture Models (GMM), and hierarchical clustering.
Time series models
Time series models use various data inputs at a specific time frequency, such as daily, weekly, monthly, et cetera. It is common to plot the dependent variable over time to assess the data for seasonality, trends, and cyclical behavior, which may indicate the need for specific transformations and model types.
Autoregressive (AR), moving average (MA), ARMA, and ARIMA models are all frequently used time series models. As an example, a call center can use a time series model to forecast how many calls it will receive per hour at different times of day.
Predictive analytics can be deployed in across various industries for different business problems. Below are a few industry use cases to illustrate how predictive analytics can inform decision-making within real-world situations.
- Banking: Financial services use machine learning and quantitative tools to predict credit risk and detect fraud. As an example, BondIT is a company that specializes in fixed-income asset-management services. Predictive analytics allows them to support dynamic market changes in real-time in addition to static market constraints. This use of technology allows it to both customize personal services for clients and to minimize risk.
- Healthcare: Predictive analytics in health care is used to detect and manage the care of chronically ill patients, as well as to track specific infections such as sepsis. Geisinger Health used predictive analytics to mine health records to learn more about how sepsis is diagnosed and treated. Geisinger created a predictive model based on health records for more than 10,000 patients who had been diagnosed with sepsis in the past. The model yielded impressive results, correctly predicting patients with a high rate of survival.
- Human resources (HR): HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement. This combination of quantitative and qualitative data allows businesses to reduce their recruiting costs and increase employee satisfaction, which is particularly useful when labor markets are volatile.
- Marketing and sales: While marketing and sales teams are very familiar with business intelligence reports to understand historical sales performance, predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle. For example, churn predictions can enable sales teams to identify dissatisfied clients sooner, enabling them to initiate conversations to promote retention. Marketing teams can leverage predictive data analysis for cross-sell strategies, and this commonly manifests itself through a recommendation engine on a brand’s website.
- Supply chain: Businesses commonly use predictive analytics to manage product inventory and set pricing strategies. This type of predictive analysis helps companies meet customer demand without overstocking warehouses. It also enables companies to assess the cost and return on their products over time. If one part of a given product becomes more expensive to import, companies can project the long-term impact on revenue if they do or do not pass on additional costs to their customer base.
An organization that knows what to expect based on past patterns has a business advantage in managing inventories, workforce, marketing campaigns, and most other facets of operation.
- Security: Every modern organization must be concerned with keeping data secure. A combination of automation and predictive analytics improves security. Specific patterns associated with suspicious and unusual end user behavior can trigger specific security procedures.
- Risk reduction: In addition to keeping data secure, most businesses are working to reduce their risk profiles. For example, a company that extends credit can use data analytics to better understand if a customer poses a higher-than-average risk of defaulting. Other companies may use predictive analytics to better understand whether their insurance coverage is adequate.
- Operational efficiency: More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it’s broken down on the side of the road means deliveries are made on time, without the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
- Improved decision making: Running any business involves making calculated decisions. Any expansion or addition to a product line or other form of growth requires balancing the inherent risk with the potential outcome. Predictive analytics can provide insight to inform the decision-making process and offer a competitive advantage.
How does predictive analytics work?
Data scientists use predictive models to identify correlations between different elements in selected datasets. Once data collection is complete, a statistical model is formulated, trained, and modified to generate predictions.
The workflow for building predictive analytics frameworks follows five basic steps:
- Define the problem: A prediction starts with a good thesis and set of requirements. For instance, can a predictive analytics model detect fraud? Determine optimal inventory levels for the holiday shopping season? Identify potential flood levels from severe weather? A distinct problem to solve will help determine what method of predictive analytics should be used.
- Acquire and organize data: An organization may have decades of data to draw upon, or a continual flood of data from customer interactions. Before predictive analytics models can be developed, data flows must be identified, and then datasets can be organized in a repository such as a data warehouse like BigQuery.
- Pre-process data: Raw data is only nominally useful by itself. To prepare the data for the predictive analytics models, it should be cleaned to remove anomalies, missing data points, or extreme outliers, any of which might be the result of input or measurement errors.
- Develop predictive models: Data scientists have a variety of tools and techniques to develop predictive models depending on the problem to be solved and nature of the dataset. Machine learning, regression models, and decision trees are some of the most common types of predictive models.
- Validate and deploy results: Check on the accuracy of the model and adjust accordingly. Once acceptable results have been achieved, make them available to stakeholders via an app, website, or data dashboard.
What are predictive analytics techniques?
In general, there are two types of predictive analytics models: classification and regression models. Classification models attempt to put data objects (such as customers or potential outcomes) into one category or another. For instance, if a retailer has a lot of data on different types of customers, they may try to predict what types of customers will be receptive to market emails. Regression models try to predict continuous data, such as how much revenue that customer will generate during their relationship with the company.
Predictive analytics tends to be performed with three main types of techniques:
Regression analysis
Regression is a statistical analysis technique that estimates relationships between variables. Regression is useful to determine patterns in large datasets to determine the correlation between inputs. It is best employed on continuous data that follows a known distribution. Regression is often used to determine how one or more independent variables affects another, such as how a price increase will affect the sale of a product.
Decision trees
Decision trees are classification models that place data into different categories based on distinct variables. The method is best used when trying to understand an individual's decisions. The model looks like a tree, with each branch representing a potential choice, with the leaf of the branch representing the result of the decision. Decision trees are typically easy to understand and work well when a dataset has several missing variables.
Neural networks
Neural networks are machine learning methods that are useful in predictive analytics when modeling very complex relationships. Essentially, they are powerhouse pattern recognition engines. Neural networks are best used to determine nonlinear relationships in datasets, especially when no known mathematical formula exists to analyze the data. Neural networks can be used to validate the results of decision trees and regression models.
Uses and examples of predictive analytics
Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk for almost any business or industry, including banking, retail, utilities, public sector, healthcare, and manufacturing. Sometimes augmented analytics are used, which uses big data machine learning. Here are some more use case examples, including data lake analytics.
Fraud detection
Predictive analytics examines all actions on a company’s network in real time to pinpoint abnormalities that indicate fraud and other vulnerabilities.
Conversion and purchase prediction
Companies can take actions, like retargeting online ads to visitors, with data that predicts a greater likelihood of conversion and purchase intent.
Risk reduction
Credit scores, insurance claims, and debt collections all use predictive analytics to assess and determine the likelihood of future defaults.
Operational improvement
Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently.
Customer segmentation
By dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique audiences.
Maintenance forecasting
Organizations use data to predict when routine equipment maintenance will be required and can then schedule it before a problem or malfunction arises.
Google Cloud what-is-predictive-analytics
Benefits, Examples, and More
Benefits of predictive analytics
Predictive analytics can help businesses make stronger, more informed decisions. It can identify patterns and trends within data that enable different business functions to make a probabilistic determination about future events. Other benefits include:
· Decision making: Improve how a business function makes decisions by relying on data to determine potential outcomes.
· Risk management: Develop risk management strategies for potential risks, and even prioritize the risks that could be most detrimental.
· Customer insights: Better understand potential customers and what they need so that you can develop more specific marketing campaigns to reach them.
· Operational efficiency: By turning to historical data to understand resources and better manage them, predictive analytics can make companies operate more efficiently.
5 Examples
Data analytics—the practice of examining data to answer questions, identify trends, and extract insights—can provide you with the information necessary to strategize and make impactful business decisions.
There are four key types of data analytics:
- Descriptive, which answers the question, “What happened?”
- Diagnostic, which answers the question, “Why did this happen?”
- Prescriptive, which answers the question, “What should we do next?”
- Predictive, which answers the question, “What might happen in the future?”
The ability to predict future events and trends is crucial across industries. Predictive analytics appears more often than you might assume—from your weekly weather forecast to algorithm-enabled medical advancements. Here’s an overview of predictive analytics to get you started on the path to data-informed strategy formulation and decision-making.
What Is Predictive Analytics?
Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.
The predictions could be for the near future—for instance, predicting the malfunction of a piece of machinery later that day—or the more distant future, such as predicting your company’s cash flows for the upcoming year.
Predictive analysis can be conducted manually or using machine-learning algorithms. Either way, historical data is used to make assumptions about the future.
One predictive analytics tool is regression analysis, which can determine the relationship between two variables (single linear regression) or three or more variables (multiple regression). The relationships between variables are written as a mathematical equation that can help predict the outcome should one variable change.
“Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship,” says Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics, one of the three courses that make up the Credential of Readiness (CORe) program. “Such insights can prove extremely valuable for analyzing historical trends and developing forecasts.”
Forecasting can enable you to make better decisions and formulate data-informed strategies. Here are several examples of predictive analytics in action to inspire you to use it at your organization.
5 Examples of Predictive Analytics in Action
1. Finance: Forecasting Future Cash Flow
Every business needs to keep periodic financial records, and predictive analytics can play a big role in forecasting your organization’s future health. Using historical data from previous financial statements, as well as data from the broader industry, you can project sales, revenue, and expenses to craft a picture of the future and make decisions.
HBS Professor V.G. Narayanan mentions the importance of forecasting in the course Financial Accounting, which is also part of CORe.
“Managers need to be looking ahead in order to plan for the future health of their business,” Narayanan says. “No matter the field in which you work, there is always a great amount of uncertainty involved in this process.”
2. Entertainment & Hospitality: Determining Staffing Needs
One example explored in Business Analytics is casino and hotel operator Caesars Entertainment’s use of predictive analytics to determine venue staffing needs at specific times.
In entertainment and hospitality, customer influx and outflux depend on various factors, all of which play into how many staff members a venue or hotel needs at a given time. Overstaffing costs money, and understaffing could result in a bad customer experience, overworked employees, and costly mistakes.
To predict the number of hotel check-ins on a given day, a team developed a multiple regression model that considered several factors. This model enabled Caesars to staff its hotels and casinos and avoid overstaffing to the best of its ability.
3. Marketing: Behavioral Targeting
In marketing, consumer data is abundant and leveraged to create content, advertisements, and strategies to better reach potential customers where they are. By examining historical behavioral data and using it to predict what will happen in the future, you engage in predictive analytics.
Predictive analytics can be applied in marketing to forecast sales trends at various times of the year and plan campaigns accordingly.
Additionally, historical behavioral data can help you predict a lead’s likelihood of moving down the funnel from awareness to purchase. For instance, you could use a single linear regression model to determine that the number of content offerings a lead engages with predicts—with a statistically significant level of certainty—their likelihood of converting to a customer down the line. With this knowledge, you can plan targeted ads at various points in the customer’s lifecycle.
4. Manufacturing: Preventing Malfunction
While the examples above use predictive analytics to take action based on likely scenarios, you can also use predictive analytics to prevent unwanted or harmful situations from occurring. For instance, in the manufacturing field, algorithms can be trained using historical data to accurately predict when a piece of machinery will likely malfunction.
When the criteria for an upcoming malfunction are met, the algorithm is triggered to alert an employee who can stop the machine and potentially save the company thousands, if not millions, of dollars in damaged product and repair costs. This analysis predicts malfunction scenarios in the moment rather than months or years in advance.
Some algorithms even recommend fixes and optimizations to avoid future malfunctions and improve efficiency, saving time, money, and effort. This is an example of prescriptive analytics; more often than not, one or more types of analytics are used in tandem to solve a problem.
5. Health Care: Early Detection of Allergic Reactions
Another example of using algorithms for rapid, predictive analytics for prevention comes from the health care industry. The Wyss Institute at Harvard University partnered with the KeepSmilin4Abbie Foundation to develop a wearable piece of technology that predicts an anaphylactic allergic reaction and automatically administers life-saving epinephrine.
The sensor, called AbbieSense, detects early physiological signs of anaphylaxis as predictors of an ensuing reaction—and it does so far quicker than a human can. When a reaction is predicted to occur, an algorithmic response is triggered. The algorithm can predict the reaction’s severity, alert the individual and caregivers, and automatically inject epinephrine when necessary. The technology’s ability to predict the reaction at a faster speed than manual detection could save lives.
Using Data to Strategize for the Future
No matter your industry, predictive analytics can provide the insights needed to make your next move. Whether you’re driving financial decisions, formulating marketing strategies, changing your course of action, or working to save lives, building a foundation in analytical skills can serve you well.
Catherine Cote HBS Predictive-analytics
7 areas that use predictive analytics
Many industries use predictive analytics, including financial services, health care, retail, and manufacturing, and they each have different use cases. Let's review a few.
1. Retail
Predictive analytics is essential for retailers who want to understand customer behaviors and preferences. With insights from data, you can make more informed decisions about product assortment, pricing, promotions, and other aspects.
For example, retailers might use predictive analytics to determine which products are most likely to be purchased together and then offer discounts on those items combined. They can also identify customers at risk of leaving for a competitor and take steps to keep them.
2. Banking
Banks use predictive analytics to make more informed decisions about credit and investment products and even trade currency. Banking-related data sets form patterns that identify customers at risk of defaulting on a loan.
Banks also use predictive analytics to identify customers likely to be interested in investing in a new financial product so that they can target them with impactful marketing messaging.
3. Sales
Sales teams use predictive analytics to understand better customers’ wants and needs. By analyzing past customer behaviors, they can more accurately predict which products or services a customer is likely to purchase. This allows sales teams to focus on selling the most appealing items to their prospects and ultimately increase their sales revenue.
4. Insurance
Insurance companies use predictive analytics to determine the likelihood that a particular customer will make a policy claim. By analyzing claims history, demographics, and lifestyle choices, insurers can develop models that help them predict which customers are most likely to file a claim. This information allows them to adjust premiums and identify and target higher-risk customers with specific policies.
5. Social media
Social media teams use predictive analytics to understand user behavior and trends. By analyzing the vast amount of data generated by users on social media platforms, they can gain insights into the things that people care about, what they are talking about, and how they interact with each other. This information improves the user experience on social media platforms and enables them to target advertising more effectively.
6. Underwriting
The process of underwriting insurance policies routinely uses predictive analysis. By analyzing data on past claims, insurers identify patterns that may indicate a higher risk of future claims. Armed with probabilities and predictions, they can then adjust premiums for individual policies or groups of policies or even deny coverage altogether.
7. Health
Predictive analytics in health care can identify patients at risk of developing certain diseases or conditions. By analyzing demographic data, health records, and genetic information, doctors and researchers can develop models that help them create health policies and interventions. They can then use predictive analytics to create targeted prevention and treatment programs for those patients at the highest risk.
Predictive analytics: job outlook and salary
Predictive analytics falls within the larger umbrella of data science, which has a positive outlook in the US. Demand for data professionals is expected to grow by 36 percent—much faster than average—over the next decade, according to the US Bureau of Labor Statistics [1].
What's more, data science occupies the third spot on Glassdoor’s "50 Best Jobs in America for 2022" list [2]. Working in data science also tends to pay a higher-than-average salary. According to Glassdoor, the average annual salary for a predictive analyst is $83,948, once base pay and additional compensation are combined [3].
How to get started in predictive analytics
To work in predictive analytics, you’ll need to be comfortable working with large datasets, have a strong grasp of data analytics and statistics, and be able to communicate your findings clearly to non-technical audiences. Here are some ways you can gain the skills needed to become a data professional specializing in predictive analytics:
1. Education
A data scientist typically has a strong background in mathematics and computer science, and holds at least a bachelor's degree with a major in data science or a related subject, like IT, statistics, or business. That being said, many data scientists have taught themselves the necessary skills through online resources and personal projects.
2. Professional experience
In addition to formal education, gaining professional experience is essential for becoming a data scientist. You can gain experience in predictive analytics through internships, working with datasets in freelancing projects, and working in junior or entry-level roles.
Many employers place great value on relevant work experience, so previous experience working with data and analytics tools can be helpful. You'll want to build your skill set and experience to work in predictive analytics. Your resume may look more robust if you have demonstrable experience in the following:
· Predictive modeling
· Regression analysis
· Classification algorithms
· Decision trees
· Neural networks
· Support vector machines
3. Certifications
When you're pivoting into data analytics, earning a professional certificate or certification can be a great way to learn about the subject and gain the skills you need to do the work.
Several certifications are available for predictive analytics professionals, such as the Certified Analytics Professional (CAP) certification offered by INFORMS. Certificates are not always required for employment, but they can strengthen your resume.
Common certifications and certificates include:
· Google Data Analytics Professional Certificate
· Microsoft Certified: Data Analyst Associate
· Associate Certified Analytics Professional
· IIBA Certification in Business Data Analytics (CBDA)
Model Types and Uses
Uses of Predictive Analytics
Predictive analytics is a decision-making tool in a variety of industries.
Forecasting
Forecasting is essential in manufacturing because it ensures the optimal utilization of resources in a supply chain. Critical spokes of the supply chain wheel, whether it is inventory management or the shop floor, require accurate forecasts for functioning.
Predictive modeling is often used to clean and optimize the quality of data used for such forecasts. Modeling ensures that more data can be ingested by the system, including from customer-facing operations, to ensure a more accurate forecast.
Credit
Credit scoring makes extensive use of predictive analytics. When a consumer or business applies for credit, data on the applicant's credit history and the credit record of borrowers with similar characteristics are used to predict the risk that the applicant might fail to perform on any credit extended.
Underwriting
Data and predictive analytics play an important role in underwriting. Insurance companies examine policy applicants to determine the likelihood of having to pay out for a future claim based on the current risk pool of similar policyholders, as well as past events that have resulted in payouts. Predictive models that consider characteristics in comparison to data about past policyholders and claims are routinely used by actuaries.
Marketing
Individuals who work in this field look at how consumers have reacted to the overall economy when planning on a new campaign. They can use these shifts in demographics to determine if the current mix of products will entice consumers to make a purchase.
Active traders, meanwhile, look at a variety of metrics based on past events when deciding whether to buy or sell a security. Moving averages, bands, and breakpoints are based on historical data and are used to forecast future price movements.
Fraud Detection
Financial services can use predictive analytics to examine transactions, trends, and patterns. If any of this activity appears irregular, an institution can investigate it for fraudulent activity. This may be done by analyzing activity between bank accounts or analyzing when certain transactions occur.
Supply Chain
Supply chain analytics is used to predict and manage inventory levels and pricing strategies. Supply chain predictive analytics use historical data and statistical models to forecast future supply chain performance, demand, and potential disruptions. This helps businesses proactively identify and address risks, optimize resources and processes, and improve decision-making. These steps allow companies to forecast what materials will be on hand at any given moment and whether there will be any shortages.
Human Resources
Human resources uses predictive analytics to improve various processes, such as forecasting future workforce needs and skills requirements or analyzing employee data to identify factors that contribute to high turnover rates. Predictive analytics can also analyze an employee's performance, skills, and preferences to predict their career progression and help with career development planning in addition to forecasting diversity or inclusion initiatives.
Predictive Analytics vs. Machine Learning
A common misconception is that predictive analytics and machine learning are the same things. Predictive analytics help us understand possible future occurrences by analyzing the past. At its core, predictive analytics includes a series of statistical techniques (including machine learning, predictive modeling, and data mining) and uses statistics (both historical and current) to estimate, or predict, future outcomes.
Machine learning, on the other hand, is a subfield of computer science that, as per the 1959 definition by Arthur Samuel (an American pioneer in the field of computer gaming and artificial intelligence) means "the programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning."5
The most common predictive models include decision trees, regressions (linear and logistic), and neural networks, which is the emerging field of deep learning methods and technologies.
Types of Predictive Analytical Models
There are three common techniques used in predictive analytics: Decision trees, neural networks, and regression. Read more about each of these below.
Decision Trees
If you want to understand what leads to someone's decisions, then you may find decision trees useful. This type of model places data into different sections based on certain variables, such as price or market capitalization. Just as the name implies, it looks like a tree with individual branches and leaves. Branches indicate the choices available while individual leaves represent a particular decision.
Decision trees are the simplest models because they're easy to understand and dissect. They're also very useful when you need to make a decision in a short period of time.6
Regression
This is the model that is used the most in statistical analysis. Use it when you want to determine patterns in large sets of data and when there's a linear relationship between the inputs. This method works by figuring out a formula, which represents the relationship between all the inputs found in the dataset. For example, you can use regression to figure out how price and other key factors can shape the performance of a security.6
Neural Networks
Neural networks were developed as a form of predictive analytics by imitating the way the human brain works. This model can deal with complex data relationships using artificial intelligence and pattern recognition. Use it if you have several hurdles that you need to overcome like when you have too much data on hand, when you don't have the formula you need to help you find a relationship between the inputs and outputs in your dataset, or when you need to make predictions rather than come up with explanations.6
If you've already used decision trees and regression as models, you can confirm your findings with neural networks.6
Cluster Models
Clustering describes the method of aggregating data that share similar attributes. Consider a large online retailer like Amazon. Amazon can cluster sales based on the quantity purchased or it can cluster sales based on the average account age of its consumer. By separating data into similar groups based on shared features, analysts may be able to identify other characteristics that define future activity.
Time Series Modeling
Sometimes, data relates to time, and specific predictive analytics rely on the relationship between what happens when. These types of models assess inputs at specific frequencies such as daily, weekly, or monthly iterations. Then, analytical models seek seasonality, trends, or behavioral patterns based on timing. This type of predictive model can be useful to predict when peak customer service periods are needed or when specific sales will be made.
How Businesses Can Use Predictive Analytics
As noted above, predictive analysis can be used in a number of different applications. Businesses can capitalize on models to help advance their interests and improve their operations. Predictive models are frequently used by businesses to help improve their customer service and outreach.7
Executives and business owners can take advantage of this kind of statistical analysis to determine customer behavior. For instance, the owner of a business can use predictive techniques to identify and target regular customers who could defect and go to a competitor.7
Predictive analytics plays a key role in advertising and marketing. Companies can use models to determine which customers are likely to respond positively to marketing and sales campaigns. Business owners can save money by targeting customers who will respond positively rather than doing blanket campaigns.7
Benefits of Predictive Analytics
There are numerous benefits to using predictive analysis. As mentioned above, using this type of analysis can help entities when you need to make predictions about outcomes when there are no other (and obvious) answers available.8
Investors, financial professionals, and business leaders are able to use models to help reduce risk. For instance, an investor and their advisor can use certain models to help craft an investment portfolio with minimal risk to the investor by taking certain factors into consideration, such as age, capital, and goals.8
There is a significant impact to cost reduction when models are used. Businesses can determine the likelihood of success or failure of a product before it launches. Or they can set aside capital for production improvements by using predictive techniques before the manufacturing process begins.8
Criticism of Predictive Analytics
The use of predictive analytics has been criticized and, in some cases, legally restricted due to perceived inequities in its outcomes. Most commonly, this involves predictive models that result in statistical discrimination against racial or ethnic groups in areas such as credit scoring, home lending, employment, or risk of criminal behavior.
A famous example of this is the (now illegal) practice of redlining in home lending by banks. Regardless of whether the predictions drawn from the use of such analytics are accurate, their use is generally frowned upon, and data that explicitly include information such as a person's race are now often excluded from predictive analytics.
How Does Netflix Use Predictive Analytics?
Data collection is very important to a company like Netflix. It collects data from its customers based on their behavior and past viewing patterns. It uses that information to make recommendations based on their preferences. This is the basis behind the "Because you watched..." lists you'll find on your subscription.
What Are the 3 Pillars of Data Analytics?
There are three pillars to data analytics. They are the needs of the entity that is using the models, the data and the technology used to study it, and the actions and insights that come as a result of the use of this kind of analysis.
What Is Predictive Analytics Good for?
Predictive analytics is good for forecasting, risk management, customer behavior analytics, fraud detection, and operational optimization. Predictive analytics can help organizations improve decision-making, optimize processes, and increase efficiency and profitability. This branch of analytics is used to leverage data to forecast what may happen in the future.
Clay Halton Erika Rasure Investopedia Predictive-analytics
Predictive Analytics in Today's World
With predictive analytics, you can go beyond learning what happened and why to discovering insights about the future. Learn how predictive analytics shapes the world we live in.
Why is predictive analytics important?
Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Common uses include:
Detecting fraud. Combining multiple analytics methods can improve pattern detection, identify criminal behavior and prevent fraud. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.
Got a predictive analytics skills gap?
Turning raw numbers into valuable insights requires help from professionals skilled in AI, machine learning and data analytics. But talent is in short supply. Discover strategies to address this dilemma.
Put predictive analytics to good use
Wondering what you could learn by exploring trends and making predictions with your organization’s data? Read about seven organizations using analytics to gain customer insights, make better decisions and grow their businesses.
How to predict the uncontrollable
Natural disasters are here to stay. But we can minimize their destruction by predicting and preparing for events like floods. Learn how organizations are using AI and predictive analytics to make the world safer.
Improve uptime with analytics
Laboratories can’t afford downtime when sending results to doctors, clinicians and researchers. See how Siemens Healthineers used SAS to develop a predictive maintenance solution to improve system uptime by 36%.
Predictive Analytics
Data mining from SAS uses proven, cutting-edge algorithms designed to help you solve your biggest analytics challenges.
Who's using it?
Any industry can use predictive analytics to reduce risks, optimize operations and increase revenue. Here are a few examples.
Banking & Financial Services
The financial industry, with huge amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers. Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation.
Retail
Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using predictive analytics for merchandise planning and price optimization, to analyze the effectiveness of promotional events and to determine which offers are most appropriate for consumers. Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137% ROI.
Oil, Gas & Utilities
Whether it is predicting equipment failures and future resource needs, mitigating safety and reliability risks, or improving overall performance, the energy industry has embraced predictive analytics with vigor. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. Analyses of machine sensor data predicts when power-generating turbines need maintenance.
Governments & the Public Sector
Governments have been key players in the advancement of computer technologies. The US Census Bureau has been analyzing data to understand population trends for decades. Governments now use predictive analytics like many other industries – to improve service and performance; detect and prevent fraud; and better understand consumer behavior. They also use predictive analytics to enhance cybersecurity.
Health Care
In addition to detecting claims fraud, the health care industry is taking steps to identify patients most at risk of chronic disease and find what interventions are best. Express Scripts, a large pharmacy benefits company, uses analytics to identify those not adhering to prescribed treatments, resulting in a savings of $1,500 to $9,000 per patient.
Manufacturing
For manufacturers it's very important to identify factors leading to reduced quality and production failures, as well as to optimize parts, service resources and distribution. Lenovo is just one manufacturer that has used predictive analytics to better understand warranty claims – an initiative that led to a 10 to 15 percent reduction in warranty costs.
How It Works
Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. Modeling provides results in the form of predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables.
This is different from descriptive models that help you understand what happened, or diagnostic models that help you understand key relationships and determine why something happened. Entire books are devoted to analytical methods and techniques. Complete college curriculums delve deeply into this subject. But for starters, here are a few basics.
There are two types of predictive models. Classification models predict class membership. For instance, you try to classify whether someone is likely to leave, whether he will respond to a solicitation, whether he’s a good or bad credit risk, etc. Usually, the model results are in the form of 0 or 1, with 1 being the event you are targeting. Regression models predict a number – for example, how much revenue a customer will generate over the next year or the number of months before a component will fail on a machine.
Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks.
Regression (linear and logistic) is one of the most popular method in statistics. Regression analysis estimates relationships among variables. Intended for continuous data that can be assumed to follow a normal distribution, it finds key patterns in large data sets and is often used to determine how much specific factors, such as the price, influence the movement of an asset. With regression analysis, we want to predict a number, called the response or Y variable. With linear regression, one independent variable is used to explain and/or predict the outcome of Y. Multiple regression uses two or more independent variables to predict the outcome. With logistic regression, unknown variables of a discrete variable are predicted based on known value of other variables. The response variable is categorical, meaning it can assume only a limited number of values. With binary logistic regression, a response variable has only two values such as 0 or 1. In multiple logistic regression, a response variable can have several levels, such as low, medium and high, or 1, 2 and 3.
Decision trees are classification models that partition data into subsets based on categories of input variables. This helps you understand someone's path of decisions. A decision tree looks like a tree with each branch representing a choice between a number of alternatives, and each leaf representing a classification or decision. This model looks at the data and tries to find the one variable that splits the data into logical groups that are the most different. Decision trees are popular because they are easy to understand and interpret. They also handle missing values well and are useful for preliminary variable selection. So, if you have a lot of missing values or want a quick and easily interpretable answer, you can start with a tree.
Neural networks are sophisticated techniques capable of modeling extremely complex relationships. They’re popular because they’re powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. They are often used to confirm findings from simple techniques like regression and decision trees. Neural networks are based on pattern recognition and some AI processes that graphically “model” parameters. They work well when no mathematical formula is known that relates inputs to outputs, prediction is more important than explanation or there is a lot of training data. Artificial neural networks were originally developed by researchers who were trying to mimic the neurophysiology of the human brain.
Other Popular Techniques
Bayesian analysis. Bayesian methods treat parameters as random variables and define probability as "degrees of belief" (that is, the probability of an event is the degree to which you believe the event is true). When performing a Bayesian analysis, you begin with a prior belief regarding the probability distribution of an unknown parameter. After learning information from data you have, you change or update your belief about the unknown parameter.
Ensemble models. Ensemble models are produced by training several similar models and combining their results to improve accuracy, reduce bias, reduce variance and identify the best model to use with new data.
Gradient boosting. This is a boosting approach that resamples your data set several times to generate results that form a weighted average of the resampled data set. Like decision trees, boosting makes no assumptions about the distribution of the data. Boosting is less prone to overfitting the data than a single decision tree, and if a decision tree fits the data fairly well, then boosting often improves the fit. (Overfitting data means you are using too many variables and the model is too complex. Underfitting means the opposite – not enough variables and the model is too simple. Both reduce prediction accuracy.)
Incremental response (also called net lift or uplift models). These model the change in probability caused by an action. They are widely used to reduce churn and to discover the effects of different marketing programs.
K-nearest neighbor (KNN). This is a nonparametric method for classification and regression that predicts an object’s values or class memberships based on the k-closest training examples.
Memory-based reasoning. Memory-based reasoning is a k-nearest neighbor technique for categorizing or predicting observations.
Partial least squares. This flexible statistical technique can be applied to data of any shape. It models relationships between inputs and outputs even when the inputs are correlated and noisy, there are multiple outputs or there are more inputs than observations. The method of partial least squares looks for factors that explain both response and predictor variations.
Principal component analysis. The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the original variables as possible.
Support vector machine. This supervised machine learning technique uses associated learning algorithms to analyze data and recognize patterns. It can be used for both classification and regression.
Time series data mining. Time series data is time-stamped and collected over time at a particular interval (sales in a month, calls per day, web visits per hour, etc.). Time series data mining combines traditional data mining and forecasting techniques. Data mining techniques such as sampling, clustering and decision trees are applied to data collected over time with the goal of improving predictions.
What do you need to get started using predictive analytics?
The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by the insights? What actions will be taken?
Second, you’ll need data. In today’s world, that means data from a lot of places. Transactional systems, data collected by sensors, third-party information, call center notes, web logs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and prep the data for analysis. To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.)
After that, the predictive model building begins. Increasingly easy-to-use software means more people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data – and that’s where you get your results.
Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.
Why It’s Important
Predictive analytics uses mathematical modeling tools to generate predictions about an unknown fact, characteristic, or event. “It’s about taking the data that you know exists and building a mathematical model from that data to help you make predictions about somebody [or something] not yet in that data set,” Goulding explains.
An analyst’s role in predictive analysis is to assemble and organize the data, identify which type of mathematical model applies to the case at hand, and then draw the necessary conclusions from the results. They are often also tasked with communicating those conclusions to stakeholders effectively and engagingly.
Types of Predictive Models
While data analysts are required to make decisions regarding which mathematical model to use in a given situation, they are not actually the ones crunching the data. Statisticians and programmers develop computer programs that carry out these processes, each of which operates using a different mathematical model.
“The tools we’re using for predictive analytics now have improved and become much more sophisticated,” Goulding says, explaining that these advanced models have allowed us to “handle massive amounts of data in ways we couldn’t before.”
The advancement of these tools has also resulted in the use of predictive analytics to identify “unknowns” that previously could not be addressed, leading to an overall need for analysts that can succinctly identify which model best aligns with the type of unknown in each scenario.
Below, we explore four common predictive models and the types of questions they can be best used to answer.
1. Linear Regression
Linear regression is one of the most famous and historic modeling tools, according to Goulding. This model considers all the known data points on a graph and creates a straight line that travels through the center of those data points. This line represents the smallest possible distance between all the points on the graph. A linear regression mathematical modeling tool can then base predictions about nonexistent data off of the relationship between this line and the existing data points.
Real-World Example
A linear regression model would be useful when a doctor wants to predict a new patient’s cholesterol based only on their body mass index (BMI). In this example, the analyst would know to put the data the doctor gathered from his 5,000 other patients—including each of their BMIs and cholesterol levels—into the linear regression model. They are hoping to predict an unknown based on a predetermined set of quantifiable data.
The linear regression model would take the data, plot it onto a graph, and establish a line down the center that properly depicts the smallest distance between all plotted data points. In this scenario, when that new patient arrives knowing only that their BMI is 31, a data analyst will be able to predict the patient’s cholesterol by looking at that line and seeing what cholesterol level most closely aligns with other patients who have a BMI of 31.
2. Text Mining
Whereas linear regression uses only numeric data, mathematical models can also be used to make predictions about non-numerical factors. Text mining is a perfect example.
“Text mining is part of predictive analytics in the sense that analytics is all about finding the information I previously knew nothing about,” Goulding says. In this scenario, the tool takes data points in the form of text-based words or phrases and searches a giant database for those specific points.
Sound Familiar? The algorithm used by Google or other search engines to bring up relevant links when you search for a specific keyword is an example of text mining.
Real-World Example
Although tools like search engines—or even the “find” function you may use when searching for a word in a digital body of text—represent some common examples of text mining, there are also industry-specific instances where this type of predictive analytics comes into play.
Goulding describes another medical application of predictive analytics, explaining how doctors rely on text mining when analyzing patient symptoms and trying to determine the root cause. “If I’m a doctor and I have 50 children in front of me with flu symptoms, my brain can figure out that the next patient to walk in the door [with similar symptoms] also has the flu,” he says. “But if I see an unusual set of symptoms from just one patient, I may need the case history of patients from all over the world to make a correct diagnosis. My brain can’t help me do this; analytics, however, can.”
Especially in complex patient cases, an analyst can use text mining modeling tools to comb databases, locate similar symptoms among patients of the past, and generate a prediction as to what this new patient is “most likely” suffering from based on that data.
3. Optimal Estimation
Optimal estimation is a modeling technique that is used to make predictions based on observed factors. This model has been used in analytics for over 50 years and has laid the groundwork for many of the other predictive tools used today. According to Goulding, past applications of this method include determining “how to best recalibrate equipment on a manufacturing floor…[and] estimating where a bullet might go when shot,” as well as in other aspects of the defense industry.
Real-World Example
If two planes were flying toward one another, an analyst might use the optimal estimation model to predict if or when they will collide. To do this, the analyst would put a variety of observed factors into the mathematical modeling tool, including the airplanes’ height, altitude, speed, angle, and more. The mathematical model would then be able to help predict at which point, if any, the planes would meet.
4. Clustering Models
Clustering models are focused on finding different groups with similar qualities or elements within the data. Many mathematical modeling tools fall within this category, including:
- K-Means
- Hierarchical Clustering
- TwoStep
- Density-Based Scan Clustering
- Gaussian Clustering Model
- Kohonen
Real-World Example
If a fast-food restaurant wanted to open a new location in a new city, the corporate team may work with a data analyst to figure out exactly where that new location should go. The analyst would start by gathering an array of specific, relevant data about each location—including factors like demographics, where the high-end houses are, how close the location is to a college, etc.—then input all of that data into a clustering mathematical model. This model would most efficiently analyze this particular type of data and predict where the most strategic location in the city for that restaurant is based on the data alone.
5. Neural Networks
Neural networks are complex algorithms inspired by the structure of the human brain. They process historical and current data and identify complex relationships within the data to predict the future, similar to how the human brain can spot trends and patterns.
A typical neural network is composed of artificial neurons, called units, arranged in different layers. The neural network uses input units to learn about and process data. On the other hand, output units are on the opposite side and outline how the neural network should respond to the input units. Between the two are hidden layers, which are layers of mathematical functions that produce a specific output.
Real-World Example
If an e-commerce retailer wants to accurately predict which products its customers are likely to consider purchasing in the future, a data analyst or data scientist might use neural networks to inform the company’s product recommendation algorithm. The analyst will pull purchase data and feed it to the neural network, giving the network real examples to learn from. This data will travel through the neural network through various mathematical functions until the output is produced and a product recommendation populates.
Other Common Predictive Models
In addition to the mathematical models above, there are additional models that data analysts use to make predictions, including:
- Decision trees
- Random forests
- Logistic regression
- Bayesian methods
Why Is Predictive Analytics Important?
While organizations have recognized the importance of gathering data as a means of looking back on industry trends for years, business teams have only just started scratching the surface of possibility when it comes to predictive analytics.
“Analytics is getting exciting in every industry because we’re [more] equipped than ever to…use the data in the back room that has been gathering dust…to make better business decisions,” Goulding says.
From insurance to retail to healthcare, organizations are starting to adapt to this model of informed decision-making and are using it to their advantage:
- Today, insurance companies can predict if a new client is a risk based on their age, history, health conditions, etc. They can weigh this data and make an informed decision about whether or not they want to cover that individual.
- Retail organizations can predict how new brands or items might sell in their local market based on consumer demographics. They can then make strategic decisions about how much product to stock.
- Doctors can use predictive data to help determine not only what ailment someone’s conditions point to but also their chances of survival, whether or not they need immediate surgery, and their condition’s expected decline over a certain period of time.
No matter the industry, the recent advancements in mathematical modeling and the overall lean into data as a prescriptive form of insight have changed the way businesses operate today. Businesses can make data-driven decisions based on predictive models, allowing them to mitigate potential risks and maximize profits. These changes have created an overall trend in decision-making that is sure to continue developing and expanding for years to come.
Prepare for a Career in Predictive Analytics
Those who aspire to work with predictive analytics should consider a career as a data scientist or data analyst, two roles that play very different parts in the predictive analytics process.
In short, Goulding explains that “data scientists…develop the mathematical models [while] most data analysts, use the tools…that have already been developed.” This difference in roles requires a particular background for professionals who want to achieve success in each field.
Those hoping to work on the development of the mathematical models vital to the predictive analytics process, for example, should focus primarily on honing their computer programming, mathematical, and statistical skills. Data analysts, on the other hand, are tasked with developing a working understanding of these data science tools on top of practical skills in data analysis.
Pursuing a Master’s Degree in Analytics
To gain the full breadth of knowledge and practical abilities required to succeed as a data analyst, Goulding recommends professionals pursue a master’s degree in analytics from a top university like Northeastern.
“In [this type of program], students will master the tools and techniques required for data analytics,” he says. “They will gain functional competency in statistics, programming languages like R and Python, and in visualization tools so that they can learn how to present their results in a visual way.”
Apart from the tactical skills needed to organize, input, and draw conclusions from data, this added layer of presentation skills is what he considers the most important area of study for aspiring analysts. Students “have to learn how to present their findings in a way that executives can use to make decisions,” he says. “They need to be able to think in terms of the business problem they’re trying to solve.”
While the use of visualization tools and the practice of effectively presenting data are covered at length in Northeastern’s analytics curriculum, Goulding also recognizes that students will do most of their learning in this area outside of the classroom.
“[Students] need real-world experiences,” he says. “They need to practice taking real-world data and solving business problems from that data.”
Northeastern offers countless experiential learning opportunities during which students pursuing their master’s in analytics can gain this real-world exposure. From co-ops to XN projects, aspiring analysts are given the chance to apply their skills from the classroom within the various industries and organizations that make up Northeastern’s global partnership network.
“Crunching data with no correlation to a business problem that needs solving is not useful,” Goulding says. “To be effective, a data analyst has to understand how data is driving strategic decisions in the workplace [and how] that data is helping executives make those strategic decisions.”
Northeastern.edu Predictive-analytics
Analytical techniques
The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.
Machine Learning
Main article: Machine Learning
Machine learning can be defined as the ability of a machine to learn and then mimic human behavior that requires intelligence. This is accomplished through artificial intelligence, algorithms, and models.
Autoregressive Integrated Moving Average (ARIMA)
Main article: ARIMA
ARIMA models are a common example of time series models. These models use autoregression, which means the model can be fitted with a regression software that will use machine learning to do most of the regression analysis and smoothing. ARIMA models are known to have no overall trend, but instead have a variation around the average that has a constant amplitude, resulting in statistically similar time patterns. Through this, variables are analyzed and data is filtered in order to better understand and predict future values.
One example of an ARIMA method is exponential smoothing models. Exponential smoothing takes into account the difference in importance between older and newer data sets, as the more recent data is more accurate and valuable in predicting future values. In order to accomplish this, exponents are utilized to give newer data sets a larger weight in the calculations than the older sets.
Time series models
Main article: Time series
Time series models are a subset of machine learning that utilize time series in order to understand and forecast data using past values. A time series is the sequence of a variable's value over equally spaced periods, such as years or quarters in business applications. To accomplish this, the data must be smoothed, or the random variance of the data must be removed in order to reveal trends in the data. There are multiple ways to accomplish this.
Moving average
Main article: Moving average
Single moving average methods utilize smaller and smaller numbered sets of past data to decrease error that is associated with taking a single average, making it a more accurate average than it would be to take the average of the entire data set.
Centered moving average methods utilize the data found in the single moving average methods by taking an average of the median-numbered data set. However, as the median-numbered data set is difficult to calculate with even-numbered data sets, this method works better with odd-numbered data sets than even.
Predictive modeling
Main article: Predictive modeling
Predictive Modeling is a statistical technique used to predict future behavior. It utilizes predictive models to analyze a relationship between a specific unit in a given sample and one or more features of the unit. The objective of these models is to assess the possibility that a unit in another sample will display the same pattern. Predictive model solutions can be considered a type of data mining technology. The models can analyze both historical and current data and generate a model in order to predict potential future outcomes.
Regardless of the methodology used, in general, the process of creating predictive models involves the same steps. First, it is necessary to determine the project objectives and desired outcomes and translate these into predictive analytic objectives and tasks. Then, analyze the source data to determine the most appropriate data and model building approach (models are only as useful as the applicable data used to build them). Select and transform the data in order to create models. Create and test models in order to evaluate if they are valid and will be able to meet project goals and metrics. Apply the model's results to appropriate business processes (identifying patterns in the data doesn't necessarily mean a business will understand how to take advantage or capitalize on it). Afterward, manage and maintain models in order to standardize and improve performance (demand will increase for model management in order to meet new compliance regulations).
Regression analysis
Main article: Regression analysis
Generally, regression analysis uses structural data along with the past values of independent variables and the relationship between them and the dependent variable to form predictions.
Linear regression
Main article: Linear regression
In linear regression, a plot is constructed with the previous values of the dependent variable plotted on the Y-axis and the independent variable that is being analyzed plotted on the X-axis. A regression line is then constructed by a statistical program representing the relationship between the independent and dependent variables which can be used to predict values of the dependent variable based only on the independent variable. With the regression line, the program also shows a slope intercept equation for the line which includes an addition for the error term of the regression, where the higher the value of the error term the less precise the regression model is. In order to decrease the value of the error term, other independent variables are introduced to the model, and similar analyses are performed on these independent variables.
Applications
Analytical Review and Conditional Expectations in Auditing
An important aspect of auditing includes analytical review. In analytical review, the reasonableness of reported account balances being investigated is determined. Auditors accomplish this process through predictive modeling to form predictions called conditional expectations of the balances being audited using autoregressive integrated moving average (ARIMA) methods and general regression analysis methods, specifically through the Statistical Technique for Analytical Review (STAR) methods.
The ARIMA method for analytical review uses time-series analysis on past audited balances in order to create the conditional expectations. These conditional expectations are then compared to the actual balances reported on the audited account in order to determine how close the reported balances are to the expectations. If the reported balances are close to the expectations, the accounts are not audited further. If the reported balances are very different from the expectations, there is a higher possibility of a material accounting error and a further audit is conducted.
Regression analysis methods are deployed in a similar way, except the regression model used assumes the availability of only one independent variable. The materiality of the independent variable contributing to the audited account balances are determined using past account balances along with present structural data. Materiality is the importance of an independent variable in its relationship to the dependent variable. In this case, the dependent variable is the account balance. Through this the most important independent variable is used in order to create the conditional expectation and, similar to the ARIMA method, the conditional expectation is then compared to the account balance reported and a decision is made based on the closeness of the two balances.
The STAR methods operate using regression analysis, and fall into two methods. The first is the STAR monthly balance approach, and the conditional expectations made and regression analysis used are both tied to one month being audited. The other method is the STAR annual balance approach, which happens on a larger scale by basing the conditional expectations and regression analysis on one year being audited. Besides the difference in the time being audited, both methods operate the same, by comparing expected and reported balances to determine which accounts to further investigate.
Business Value
As we move into a world of technological advances where more and more data is created and stored digitally, businesses are looking for ways to take advantage of this opportunity and use this information to help generate profits. Predictive analytics can be used and is capable of providing many benefits to a wide range of businesses, including asset management firms, insurance companies, communication companies, and many other firms. In a study conducted by IDC Analyze the Future, Dan Vasset and Henry D. Morris explain how an asset management firm used predictive analytics to develop a better marketing campaign. They went from a mass marketing approach to a customer-centric approach, where instead of sending the same offer to each customer, they would personalize each offer based on their customer. Predictive analytics was used to predict the likelihood that a possible customer would accept a personalized offer. Due to the marketing campaign and predictive analytics, the firm's acceptance rate skyrocketed, with three times the number of people accepting their personalized offers.
Technological advances in predictive analytics have increased its value to firms. One technological advancement is more powerful computers, and with this predictive analytics has become able to create forecasts on large data sets much faster. With the increased computing power also comes more data and applications, meaning a wider array of inputs to use with predictive analytics. Another technological advance includes a more user-friendly interface, allowing a smaller barrier of entry and less extensive training required for employees to utilize the software and applications effectively. Due to these advancements, many more corporations are adopting predictive analytics and seeing the benefits in employee efficiency and effectiveness, as well as profits.
Cash-flow Prediction
ARIMA univariate and multivariate models can be used in forecasting a company's future cash flows, with its equations and calculations based on the past values of certain factors contributing to cash flows. Using time-series analysis, the values of these factors can be analyzed and extrapolated to predict the future cash flows for a company. For the univariate models, past values of cash flows are the only factor used in the prediction. Meanwhile the multivariate models use multiple factors related to accrual data, such as operating income before depreciation.
Another model used in predicting cash-flows was developed in 1998 and is known as the Dechow, Kothari, and Watts model, or DKW (1998). DKW (1998) uses regression analysis in order to determine the relationship between multiple variables and cash flows. Through this method, the model found that cash-flow changes and accruals are negatively related, specifically through current earnings, and using this relationship predicts the cash flows for the next period. The DKW (1998) model derives this relationship through the relationships of accruals and cash flows to accounts payable and receivable, along with inventory.
Child protection
Some child welfare agencies have started using predictive analytics to flag high risk cases. For example, in Hillsborough County, Florida, the child welfare agency's use of a predictive modeling tool has prevented abuse-related child deaths in the target population.
Predicting outcomes of legal decisions
The predicting of the outcome of juridical decisions can be done by AI programs. These programs can be used as assistive tools for professions in this industry.
Portfolio, product or economy-level prediction
Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example, a retailer might be interested in predicting store-level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques (see below). They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power.
Underwriting
Many businesses have to account for risk exposure due to their different services and determine the costs needed to cover the risk. Predictive analytics can help underwrite these quantities by predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the process of customer acquisition by predicting the future risk behavior of a customer using application level data. Predictive analytics in the form of credit scores have reduced the amount of time it takes for loan approvals, especially in the mortgage market. Proper predictive analytics can lead to proper pricing decisions, which can help mitigate future risk of default. Predictive analytics can be used to mitigate moral hazard and prevent accidents from occurring.
Policing
Police agencies are now utilizing proactive strategies for crime prevention. Predictive analytics, which utilizes statistical tools to forecast crime patterns, provides new ways for police agencies to mobilize resources and reduce levels of crime. With this predictive analytics of crime data, the police can better allocate the limited resources and manpower to prevent more crimes from happening. Directed patrol or problem-solving can be employed to protect crime hot spots, which exhibit crime densities much higher than the average in a city.
Sports
Several firms have emerged specializing in predictive analytics in the field of professional sports for both teams and individuals. While predicting human behavior creates a wide variance due to many factors that can change after predictions are made, including injuries, officiating, coaches decisions, weather, and more, the use of predictive analytics to project long term trends and performance is useful. Much of the field was started by the Moneyball concept of Billy Beane near the turn of the century, and now most professional sports teams employ their own analytics departments.
Wikipedia Predictive Analytics
11 Best Predictive Analytics Tools (Software Compared)
What are predictive analytics tools?
Predictive analytics tools are tools that use data to help you see into the future. But it’s not a crystal ball. Instead it tells you the probabilities of possible outcomes. Knowing these probabilities can help you plan many aspects of your business.
Predictive analysis is part of the wider set of data analysis.
Other aspects of data analytics include descriptive analytics, which helps you understand what your data represents. Diagnostic analytics pinpoint the causes of what’s happened. Prescriptive analytics is closer to predictive analytics. This gives you actionable tips for making better decisions.
In other words, you can say predictive analytics is between data mining, which looks for patterns, and prescriptive analytics, which tells you what you should do with this information.
Predictive analytics tools comparison chart (top 10 highest rated)
Product | Best for | Pricing | Website |
SAP Analytics Cloud | Best predictive analytics solution overall | $22.00 /user / month | Visit |
SAS Advanced Analytics | Best business intelligence tool for enterprise | Contact vendor | Visit |
RapidMiner | Top free predictive analytics software | Studio Professional $7,500 /year | Visit |
Alteryx | Best predictive analytics vendor for team collaboration | $72,000 / year | Visit |
IBM SPSS | Good predictive analytics tools for researchers | SPSS Statistics $99 /user /month | Visit |
TIBCO | Best free predictive analytics software | Premium plans are $25 /month | Visit |
H2O.ai | Good open source predictive analytics tool | Contact vendor | Visit |
Ibi WebFOCUS | Good predictive analytics tool for beginners | Contact vendor | Visit |
Emcien | Top predictive analytics tools for marketing | Contact vendor | Visit |
Sisense | Good business intelligence software for data scientists | Contact vendor | Visit |
Predictive modeling versus predictive analytics
These two terms may get used interchangeably. They are both parts of data science. But there are some differences to keep in mind.
You can say that predictive modeling is the more technical aspect of predictive analytics. Data analysts do modeling with statistics and other historical data. The model then weighs the likeliness of various outcomes. You can also reuse the models on other data stored in your data warehouse.
Predictive analytics tries to help you understand why the models gave different weighted scores. The insights here go beyond data scientists. They help in all sorts of use cases for business managers and other professionals. The best predictive analytics software streamlines the transition between modeling to analytics.
Raw data analytics
Another difference is the ability to do textual analysis. This is when you collect unstructured data. Basic predictive modeling cannot use this information.
For example when interviewing people. Normal human speech doesn’t compute to simple models. But the best predictive analytics products can do sentiment analysis. This means they use AI and machine learning to understand what someone means and transforms that raw sentiment into structured data.
Uses of predictive analytics
There are many great use cases for predictive analytics. Here are a few.
In e-commerce it can help analyze customer data. It can predict the likeliness of certain people to buy. And it can predict the ROI of targeted marketing campaigns. Some SaaS can collect data right from online stores like Amazon Marketplace.
Social media marketing benefits from predictive analytics. It can help you plan what kind of content to post. It also shows you the best times and days to post. It may help you with things like Google Ads too.
It’s used in banking and insurance. It can help figure out credit ratings. You can also use it to identify fraudulent activities.
There are a lot of uses for predictive analytics in healthcare. It can monitor and look for early warning signs of someone’s health problems.
Manufacturing needs predictive analytics to help with many things. These include inventory and supply chains. It also includes hiring of personnel. Planning your shipping and fulfillment is more efficient with predictive analytics.
What are the best predictive analytics tools? Here’s our top 11 list:
You want the best business intelligence software? Are you looking for good free predictive analytics tools?
Let’s break down 11 platforms that’ll give you an edge over future outcomes. We talk about features, niche, scale and price. Hopefully you find the solution best for your business needs.
SAP Analytics Cloud (best predictive analytics solution overall)
SAP is a huge multinational software company. It’s a German firm dating back to the 70s. ERP is their specialty and they have many good data platforms.
SAP Predictive Analytics was their main data analytics platform. Now, that software is being phased into SAP’s larger Cloud Analytics platform. This does more BI than SAP Predictive Analytics did. SAP Analytics Cloud runs on AI for enhanced business planning and forecasting. It works on all devices. This analytics platform easily scales up for businesses of any size. Augmented analytics lets you use NLP to gain insights from big data. Machine learning automates workflows to regularly reveal relationships and patterns. Finally, SAP Analytics Cloud is easy enough for anyone to use.
SAP Analytics Cloud for Business Intelligence starts $22.00 per user per month.
SAP is best for:
· SMBs
· Large business
· Enterprise
· Data mining
· Predictive analytics
· Augmented analytics
· Forecasting
· Business intelligence
Website: SAP
SAS Advanced Analytics (best business intelligence tool for enterprise)
SAS is another multinational software firm with roots in the 70s. The main focus of SAS is analytics software. It’s headquartered in North Carolina.
SAS Advanced Analytics is a total suite of predictive analytics tools. It does data ming, which simplifies the data. This gets it ready for data modeling. There is statistical analysis. This uses algorithms to analyze the past, present and future. Forecasting tools automatically generate models for future probabilities. You get text analytics that analyzes unstructured data. SAS Advanced Analytics lets you run quick simulations. Here you can tweak variables and get new results instantly.
For prices you’ll have to contact SAS.
SAS is best for:
· SMBs
· Large business
· Enterprise
· Text analysis
· Statistical analysis
· Forecasting
· Predictive analytics
Website: SAS
RapidMiner (top free predictive analytics software)
RapidMiner’s origins are in the AI department of the Technical University of Dortmund. It got off the ground in 2006. Today there are two products. RapidMiner Go and RapidMiner Studio.
RapidMiner is an end to end data analysis platform. It makes use of data modeling and machine learning to give you robust predictive analytics. Everything works on a fast drag and drop interface. You get a library of over 1,500 algorithms to apply to your data. There are templates to monitor things like customer churn and predictive maintenance. RapidMiner is a good data visualization tool. It makes seeing future outcomes of business decisions easy to interpret. Automated machine learning gives you stats on potential gains and other ROI data.
There’s a free version of RapidMiner Studio. The Professional version is $7,500 and the Enterprise is $15,000, both per user per year. RapidMiner Go starts at $10 per month.
RapidMiner is best for:
· Free users
· Startups
· Small businesses
· Large businesses
· Data modeling
· Data visualization
· Machine learning algorithms
Website: RapidMiner
Alteryx (best predictive analytics vendor for team collaboration)
Alteryx used to be called SRC LLC. In 2010 the company rebranded as Alteryx. Today it’s a Gartner Magic Quadrant leader in data science and machine learning.
Alteryx’s main product is the APA platform. This is analytic process automation. It combines data science with predictive analytics. The goal of Alteryx is for non-coders to use the platform. It gives you hundreds of automation “building blocks” to apply to your data. This turns big data into actionable data insights. Alteryx can handle unstructured data. It can also do sentiment analysis on raw data. Alteryx Analytics Hub makes it easy for teams to share insights. It’s also a simple automation builder for creating scheduled workflows.
There are many packages with Alteryx. Alteryx Analytics Hub goes for $72,000 per year.
Alteryx is best for:
· Team collaboration
· Large businesses
· Enterprises
· Data science
· Business analytics
· Unstructured data
· Sentiment analysis
Website: Alteryx
IBM SPSS (good predictive analytics tools for researchers)
IBM (international business machines) is a well-known name in all things technology. SPSS is its statistical product and service solutions. It first came out in 1968.
IBM SPSS specializes in advanced statistical analysis. There are two modules. SPSS Statistics and SPSS Modeler. The SPSS Statistics module does predictive analytics. It combines ad hoc analysis, hypothesis testing and geospatial analysis. This is useful when you want to find specific answers in your data. Then there is an SPSS Modeler. It’s more open-ended than SPSS Statistics. This transforms predictive analysis into graphic visuals. It makes it easy to spot patterns and anomalies in data. You can also use IBM’s Watson AI tool. This gives you more advanced data science features used for predictive analytics.
IBM SPSS Statistics starts at $99 per user per month. IBM SPSS Modeler starts at $499 per user per month.
IBM SPSS is best for:
· SMBs
· Large businesses
· Enterprise
· Academics
· Data scientists
· Statistical analysis
· Data modeling
Website: IBM SPSS
TIBCO Spotfire (best free predictive analytics software)
TIBCO is a 20-year old big data firm. They are based out of Palo Alto. Spotfire, its data analytics software, was acquired in 2007.
TIBCO Spotfire has many tools to work on large data sets. When it comes to predictive analytics, Spotfire is easy enough for anyone to use. Spotfire has something called one-click predictions. These are pre-programmed ways to classify and cluster data. It also shows relationships and does forecasting. Spotfire has nice data visualization. It’s always reading data and updates in real-time. It’s simple to build your own apps to work with the platform. Spotfire’s machine learning algorithms get more in-depth insights.
TIBCO Spotfire has a free version. Premium plans are $25, $65 or $125 per month.
TIBCO Spotfire is best for:
· Free users
· One-person business
· Startups
· SMBs
· Predictive analytics
· Data visualization
· User-friendly
Website: TIBCO
H2O.ai (good open-source predictive analytics tool)
H2O.ai is an open-source platform. It comes out of Silicon Valley. H2O’s products are used by nearly half of the Fortune 500 companies.
H2O has many products with a range of business intelligence tools. It relies heavily on artificial intelligence and machine learning. Because it’s open-source anyone can create custom features to work on the platform. There is one product called H2O Driverless AI which is for data scientists. H2O AutoML is easier for non-technical people. H20 has many templates that are industry-specific. For finance you can do KYC or fraud prediction. For insurance there is credit rating. Marketing lets you do lead scoring and helps you predict customer behavior.
There are free trials for H2O’s products. For full prices contact the vendor.
H2O.ai is best for:
· Single business users
· Startups
· SMBs
· Enterprise
· Marketing
· Finance
· Insurance
· Healthcare
Website: H2O.ai
ibi WebFOCUS (good predictive analytics tool for beginners)
Information Builders (ibi) is an American company. It was founded back in 1975. It is one of the largest software companies that is privately held.
WebFOCUS is the analytics platform of ibi. It’s a self-service tool. Anyone can access it with a web browser. It gets users access to many databases. It lets you monitor and track lots of useful KPIs. WebFOCUS has good interactive dashboards. An insight mode makes it easy for non-tech users to see what stands out in the data. There are many more analytics tools in ibi. They have solutions for healthcare, finance, logistics and retail. What’s good about these tools is that they are easy to learn. Also the platform is flexible. Small companies can scale up with it.
There are free trials for WebFOCUS and other ibi products. Or you can request a demo. For prices you’ll have to contact the vendors.
Ibi WebFOCUS is best for:
· Single business users
· Startups
· SMBs
· Large business
· Enterprise
· Team collaboration
· Ease of use
Website: Ibi WebFOCUS
Emcien (top predictive analytics tools for marketing)
Emcien is the brainchild of a Georgia Institute of Technology prof who authored mathematical software libraries.
The platform is called EmcienPatterns. It’s built on the idea of two engines. The Analysis engine uses the data to look for patterns. Then it gives it to the Prediction engine. This is where it continues to do decision-making. You get actionable insights updated in real-time. And not only is there data visualization, but also graph analysis. You get weighted predictions and likeliness scores. It can handle unstructured data and dirty data with no prep. EmcienPatterns helps with marketing like customer retention and subscriber churn.
You can try EmcienPatterns for free. For prices contact the vendor.
Emcien is best for:
· Startups
· SMBs
· Large businesses
· Marketing
· Decision-making
· Customer retention
· On-time delivery
Website: Emcien
Sisense (good business intelligence software for data scientists)
Sisense is a BI company out of Israel with offices all over the globe. Today it’s a bona fide unicorn. They boast tens of thousands of users worldwide.
As far as business intelligence platforms go Sisense is pretty decent. It’s mostly data scientists and developers who will use this. Although managers use it as well. You create apps to do custom data mining. From there it gets you predictive modeling and analytics. On top of that Sisene has solid visualization features. New insights constantly show up on the dashboard. Then there is Sisense Narratives. It interprets the agnostic data in your visualizations. You also have pulse alerts for when new business insights emerge. These are easily set up based on your KPIs.
To get a price quote from Sisense contact them through their website.
Sisense is best for:
· SMBs
· Large businesses
· Government
· Data science
· BI
· Digital marketing
· Manufacturing
Website URL: Sisense
KNIME
KNIME is a data analytics company from Switzerland. It used to be just for the pharmaceutical industry. Now this data analytics software is used in many fields.
The KNIME software is open source. It is divided into two. There is the KNIME Analytics platform. This does data mining for insights as well as predictive analysis. The KNIME SERVER app is for teams and enterprise collaboration. Creating visual workflows is easy with KNIME. You can clean your data and derive statistics quickly. You can build machine learning algorithms. These let you perform things like decision-trees. KNIME also integrates with Apache Spark for making predictions. You can host this on Microsoft Azure or Amazon’s Web Service.
The KNIME Analytics platform is free. KNIME Server starts at $29,000 per year for 5 users.
KNIME is best for:
· Free users
· Single business users
· Startups
· SMBs
· Workflow building
· Business analytics
· Decision trees
Website: KNIME
Best tools for predictive analytics:
So what are the key takeaways? First off, you don’t need a PhD in a programming language like Python to use the predictive analytics SaaS. It’s OK if you don’t understand deep learning or neural networks.
Both SAP Analytics Cloud and SAS Advanced Analytics are top predictive analytics tools overall.
For good free predictive analytics tools you got RapidMiner, KNIME and TIBCO Spotfire.
Emcien Patterns is the surprise little guy in the bunch. This is less known predictive analytics SaaS. But it’s a great tool for marketers.
What’s important is this. Information streams in all the time from many data sources. Collecting it and understanding it only goes so far. You need the best predictive analytics solution to use that data to see into the future. Foresight is how you beat the competition.
Knowledge is power. Foresight is business power with an edge.
Michael Scheiner CRM Best-predictive-analytics-tools
6 Top Predictive Analytics Tools
Predictive analytics tools are evolving. Enhanced with AI, easier to use and geared to both data scientists and business users, these tools are more business-critical than ever.
In alphabetical order, here are six of the most popular predictive analytics tools to consider.
1. H2O Driverless AI
A relative newcomer to predictive analytics, H2O gained traction with a popular open source offering. The company's H20 Driverless AI simplifies AI development and predictive analytics for both experts and citizen data scientists through open source and custom recipes. Of note are various automated and augmented capabilities for feature engineering, model selection and parameter tuning, natural language process and semantic analysis. The company also offers a variety of capabilities to simplify the development of explainability into predictive analytics models using causal graphs, LIME, Shapley and decision tree surrogate methods.
2. IBM Watson Studio
IBM became a leading predictive analytics tools vendor with the acquisition of SPSS in 2009. SPSS was founded in 1975 and grew into one of the top statistical and analytics packages over the years. IBM continued to innovate the vendor's core capabilities and integrated them into its more modern Watson Studio on IBM Cloud Pak for Data platform. This consolidated offering combines a broad range of descriptive, diagnostic, predictive and prescriptive analytics functions. The platform simplifies predictive analytics for expert data scientists and improves collaborative data science for business users. The platform also includes various features to enhance responsible and explainable predictive models.
3. Microsoft Azure Machine Learning
Microsoft has long been a leader in various analytics capabilities through its Power BI analytics platform and Excel, which has become the analytics front end of choice for most business users. The company's Azure Machine Learning complements these core tools with capabilities for managing the complete predictive analytics lifecycle. Supporting tools include Azure Data Catalog, Azure Data Factory and Azure HDInsight.
The company supports all types of users, from expert data scientists to business subject matter experts. It also provides strong integration with its various application development and RPA tooling, which makes it easier to deploy predictive analytics capabilities directly into applications and business workflows.
4. RapidMiner Studio
RapidMiner has built a comprehensive set of predictive analytics tooling around its core data mining and text mining strengths. These core capabilities simplify extracting data from a diverse set of sources, cleaning it and incorporating it into various predictive modeling workflows. The company offers both commercial and free versions of its core products that allow anyone to get started and learn the basics. RapidMiner Notebooks simplify the development of predictive analytics models for both novices and experts alike. The company also provides various augmented capabilities for data prep (Turbo Prep), model generation (Auto Model) and model deployment (Model Ops). A new feature-sharing catalog simplifies the sharing of predictive models across the organization. The platform also supports various explainability and governance features when required.
5. SAP Predictive Analytics
SAP Predictive Analytics is a good example of how enterprise application platforms can extend their core offerings to support predictive analytics workflows. The tool is a good choice for enterprises with an extensive SAP deployment, particularly those looking to create predictive analytics for logistics, supply chain and inventory management use cases. The current offering was released in 2015 and built on two prior tools first released in 2012. The tool supports advanced and business users through various features that simplify data aggregating, predictive modeling and model analysis across separate user interfaces. Automated analytics helps business users with data preparation, modeling, social graph analysis, recommendation and predictions. Expert analytics helps experts explore various statistical techniques, visualizations and code applications using the R programming language.
6. SAS
SAS Institute is one of the oldest statistical analytics tool vendors. The first version of the company's tools started in 1966 as part of a U.S. government initiative to improve data analysis for healthcare. The company was officially launched in 1972 after its government contract ran out. It has continued to innovate various tools used by statisticians and data scientists and is a clear leader in all kinds of analytics tools and techniques, including predictive analytics.
More recently, the company has modernized its core tool sets with various data science and machine learning workflows that take advantage of modern data stacks, augmented workflows and simplified deployment. The company has hundreds of tools for various domains. Core offerings for predictive analytics include SAS Visual Data Science, SAS Data Science Programming, SAS Visual Data Decisioning and SAS Visual Machine Learning. The company also maintains strong relationships with leading cloud providers and enterprise software platforms to simplify predictive analytics development and deployment across various workflows.
George Lawton Techtarget 6-top-predictive-analytics-tools
Predictive Analytics Software FAQs
FAQs on predictive analytics software tools :
What are examples of predictive analytics?
Some examples of predictive analytics are: In marketing it helps you reach the right people with campaigns. In banking it does credit scores and fraud detection. In manufacturing it helps plan supply chains and equipment maintenance.
What are predictive analytics models?
Some predictive analytics models are: Forecast models learn from historical data and apply it to new data. Outlier models find anomalies in the data. Classification models categorize data. Time series models find data within set time periods.
What is the goal of predictive analytics?
The goal of predictive analytics is knowing the future. Predictive analytics analyzes the data that a business produces. Based on that data, it gives you the likeliness of things happening, like the chance a customer will cancel a subscription.
How are predictive analytics commonly used?
Predictive analytics are commonly used in business. Good CRM and ERP SaaS use predictive analytics. Academics and research benefit from predictive analytics. Healthcare uses predictive analytics in anything from viral detection to hospital staffing.
What do you need for predictive analytics?
To use predictive analytics you need data. All businesses and organizations create data. You need to make sure to collect as much data as possible and clean it. Many of the best predictive analytics software vendors have tools for data integration and prepping.
Michael Scheiner CRM Best-predictive-analytics-tools
Frequently Asked Questions
What are predictive analytics?
Predictive analytics use historical data to make claims about future actions, trends or events. They rely on complex software to analyze existing data and construct predictive models.
What are predictive analytics used for?
Predictive analytics have a wide range of uses, including sales and supply chain forecasting, modeling behavior, and financial modelling.
Why are predictive analytics important?
Predictive analytics software is crucial to leveraging your data as effectively as possible when predicting figure activity and decision-making.
What’s the difference between predictive analytics and machine learning?
Machine learning is a method of programming and teaching your system to take certain actions and develop certain models, while predictive analytics focuses on creating a future-focused output. Machine learning can be a method to conduct predictive analytics.
How much does predictive analytics software cost?
Some popular predictive analytics tools are free to use, but require more coding experience. More accessible products, with easier interfaces, can range from $20 per user per month to over $100 per user per month.
Trustradius Predictive-analytics
What Is the Best Model for Predictive Analytics?
The best model for predictive analytics depends on several factors, such as the type of data, the objective of the analysis, the complexity of the problem, and the desired accuracy of the results. The best model to choose from may range from linear regression, neural networks, clustering, or decision trees.
The Bottom Line
The goal of predictive analytics is to make predictions about future events, then use those predictions to improve decision-making. Predictive analytics is used in a variety of industries including finance, healthcare, marketing, and retail. Different methods are used in predictive analytics such as regression analysis, decision trees, or neural networks.
Clay Halton Erika Rasure Investopedia predictive-analytics
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