Predictive modeling is useful because it gives accurate insight into any question and allows users to create forecasts. To maintain a competitive advantage, it is critical to have insight into future events and outcomes that challenge key assumptions.
Analytics professionals often use data from the following sources to feed predictive models:
Analytics leaders must align predictive modeling initiatives with an organization’s strategic goals. For example, a computer chip manufacturer might set a strategic priority to produce chips with the greatest number of transistors in the industry by 2025. Analytics professionals could construct a predictive model to forecast the number of transistors per chip to become a leader if they feed the model product, geography, sales, and other related trend data. Additional sources could include data about the most transistor-dense chips, commercial demand for computing power, and strategic partnerships between chip manufacturers and hardware manufacturers. Once initiatives are in motion, analytics professionals can perform backward-looking analyses to assess the accuracy of predictive models and the success of the initiatives.
Analysts must organize data to align with a model so computers can create forecasts and outputs for hypothesis tests. BI tools provide insights in the form of dashboards, visualizations, and reports. A process should be put in place to ensure continued improvement. Important things to consider when integrating predictive models into business practices include:
Of the four types of data analytics, predictive modeling is most closely related to the predictive analytics category. The fur types of data analytics are:
Descriptive analytics describes the data. For example, a software-as-a-service (SaaS) company sold 2,000 licenses in Q2 and 1,000 licenses in Q1. Descriptive analytics answers the question of how many licenses were sold in Q1 vs. Q2.
Diagnostic analytics is the why behind descriptive analytics. To use the previous example, diagnostic analytics takes data a step further. A data analyst can drill down into quarterly software license sales and determine sales and marketing efforts within each region to reference them against sales growth. They could also see if a sales increase was a result of high-performing salespeople or rising interest within a certain industry.
Predictive analytics utilizes techniques such as machine learning and data mining to predict what might happen next. It can never predict the future, but it can look at existing data and determine a likely outcome. Data analysts can build predictive models once they have enough data to make predicted outcomes. Predictive analytics differs from data mining because the latter focuses on discovery of the hidden relationships between variables, whereas the former applies a model to determine likely outcomes. A SaaS company could model historical sales data against marketing expenditures across each region to create a prediction model for future revenue based on marketing spend.
Prescriptive analytics takes the final step and offers a recommendation based on a predicted outcome. Once a predictive model is in place, it can recommend actions based on historical data, external data sources, and machine learning algorithms.
Predictive modeling has many uses in the field of HR analytics, from hiring to retention. HR professionals can use predictive modeling to make important decisions for strategic HR leadership regarding workforce planning, performance management, and much more.
Predictive modeling can help HR professionals predict a wide variety of key issues. Here are some common HR analytics uses of predictive modeling:
Employers often use the Predictive Index (PI) to evaluate potential candidates and existing employees on interpersonal factors such as dominance, extroversion, patience, formality, decision-making, and enthusiasm. It utilizes an un-timed self-assessment and applies predictive modeling to find the best fit candidate or identify leadership within a company.
If a predictive model is accurate, it is said to have predictive validity. For example, if a pre-employment exam can correctly predict future job performance, it has predictive validity.
Predictive modeling is a critical way of maintaining a competitive advantage in human resources. Maintaining an information advantage over the competition can allow HR leadership to continually hire the best candidates, identify workforce needs before they happen, promote the right people, retain high-performing employees, align incentives properly, and so much more.
Customer churn prevention is a common business analytics use case for both B2B and B2C organizations. In any business, keeping current customers happy is of the highest importance. If dependable customers suddenly stop purchasing a business’s product, the company must work extra hard to replace that revenue by finding new customers or selling more to other existing customers. Moreover, customer acquisition costs are often relatively high, meaning that new customers are harder to find than previous or current customers, making customer churn an even more critical priority. Luckily for businesses, predictive modeling can be used to prevent customer churn. With enough data, businesses can produce models to identify the best predictors of customer attrition, such as specific customer behaviors like customer service communications, demographics, or segment predictors. Armed with this information, businesses can then act to prevent customer churn by ensuring quality experience within certain customer groups, fixing any problematic product features, or giving special treatment to customers who exemplify signs of dissatisfaction. This use case can be applied to a wide variety of industries and product segments, so long as the company has sufficient data – CRM or otherwise – to create a robust and valid model. Predictive analytics can add significant bottom-line value by giving businesses a pathway toward reducing customer churn.
Medical diagnosis is one of the best examples of predictive modeling in healthcare, which has already experienced major changes as a result. With millions of data records every year, the quantity of data available in the medical field is sufficient to create extremely accurate models. There are many use cases for predictive modeling in the medical field, but predictive diagnosis has already had significant impact on the field and continues to make newsworthy breakthroughs on a regular basis. One example is the Q-Poc, a diagnostic tool produced by British medical device company QuantumMDx, that uses predictive modeling to reach diagnoses in less than 20 minutes. If widely adopted, devices like this one could revolutionize the way professionals give medical care around the world and address pain points like inaccurate diagnosis, wait times, and more. Another use of predictive modeling in the healthcare space is diagnosing rare diseases. For example, in 2016, IBM announced a partnership with the Undiagnosed and Rare Diseases Centre at the University Hospital in Marburg, Germany. There, patients who have seen multiple doctors – some as many as 40 – come to medical professionals who specialize in rare diseases. In addition to IBM, Google has partnered with several British hospitals for similar projects. Improving diagnostics in both rare diseases and medicine in general could help millions of people per year in the future.
Although some systems and devices that use predictive modeling and algorithms to reach a diagnosis can now outperform medical professionals, it seems unlikely that doctors will be replaced by computers. However, improved predictive diagnostic modeling will certainly change the way doctors work. Natural language technologies can ease the burden on medical professionals by reducing the time required for data entry and processing and subsequent predictions. Thus, doctors’ work may shift away from diagnosis as a result.
Outside of sales and marketing applications, many of the use cases for predictive modeling revolve around cost reduction initiatives, which, in many industries, is a critical source of competitive advantage. In businesses such as manufacturing, automotive, specialty chemicals, consumer packaged goods, oil and gas, and utilities, there is a premium on cost-cutting measures because of the highly competitive nature of the industries. These industries also tend to be capital-intensive, meaning that much of the money required to produce the finished product is invested in equipment and factory costs. Predictive modeling can unlock ways to save costs on the maintenance of these critical resources. Predictive models trained with data about equipment usage, interior video data, and temperature data can be used to determine when machines need maintenance. Companies in these industries stand to save millions by avoiding equipment malfunctions and larger repair issues. These firms can leverage predictive models for performing maintenance proactively.
Sales and marketing boast a wide variety of potential use cases for predictive modeling. One of these use cases is analyzing and forecasting total lifetime customer value. Being able to accurately forecast customer lifetime value is of great importance to any business. Imagine being able to predict which customers will spend the most at your stores over the next five or ten years. Wouldn’t these customers be the best to target with special offers, generous loyalty programs, or special treatment? Luckily for businesses, predictive modeling can provide significant insight into the issue of customer lifetime value. With enough relevant data, a predictive model can churn out accurate predictions for the lifetime value of customers.
Anomaly detection powered by predictive models and machine learning is used by financial organizations to detect fraudulent transactions. These organizations can look at the historical patterns of spend based on factors like amount, time, and geographic location to determine a baseline for normal spend behavior. If there is an anomaly, the organization is notified and can relay the warning to the consumer to verify the purchase before additional transactions can be made against their account.
Another cost-reductive application of predictive modeling is logistics optimization. In industries that require intensive logistical support, such as delivery, predictive modeling can ease the burden of logistics planning, make cost-saving adjustments, and provide real time feedback to employees. For example, predictive models can optimize the route that delivery vehicles take. This can shorten the total distance driven, improve fuel efficiency, and reduce delivery times, which can provide a boon to customer satisfaction. In one case, a trucking company centered in the European market was able to cut its fuel costs by 15% using predictive modeling. Sensors gather data about vehicle performance and driver actions, and the model automatically coaches the driver on the optimal driving behaviors, including how to adjust speed to optimize fuel consumption. Logistical applications of predictive modeling can have a significant impact on fuel costs and maintenance in these types of industries.
Decision support systems are digital information systems designed to organize, compile, and present data for decision-makers to solve problems. These are used in wide-ranging applications, from financial dashboards to geo-spatial maps with data overlays. Predictive modeling is used in advanced decision support systems to provide decision-makers with an array of possible outcomes and how likely they are to occur based on historical data. DSS combined with a visual analytics capability can speed up the decision-making process, since it’s often easier for people to learn complex associations through visual representations instead of grid formats.
Broadly speaking, predictive models fall into two camps: parametric and non-parametric. Although these terms might seem like technical jargon, the essential difference is that parametric models make more assumptions and more specific assumptions about the characteristics of the population used in creating the model. Specifically, some of the different types of predictive models are:
Each of these types has a particular use and answers a specific question or uses a certain type of dataset. Despite the methodological and mathematical differences among the model types, the overall goal of each is similar: to predict future or unknown outcomes based on data about past outcomes.
At its core, predictive modeling significantly reduces the cost required for companies to forecast business outcomes, environmental factors, competitive intelligence, and market conditions. Here are a few of the ways that the use of predictive modeling can provide value:
Predictive models and technologies promise huge benefits, but that doesn’t mean these benefits come seamlessly. In fact, predictive modeling presents a number of challenges in practice. These challenges include:
The future of predictive modeling is, undoubtedly, closely tied to artificial intelligence. As computing power continues to increase, data collection rises exponentially, and new technologies and methods are born, computers will bear the brunt of the load when it comes to creating models. The global management consulting firm McKinsey and Co. recently studied future trends, some of which are detailed below.
Partially due to recent advancements in computing power and data quantities, predictive modeling technologies have improved the impact of regular newsworthy breakthroughs. Predictive algorithms are becoming extremely sophisticated in many fields, notably computer vision, complex games, and natural language.
With more intelligent computers, the work of predictive modeling professionals, much like with other occupations, will change to adapt to newly available predictive technology. People who work in predictive modeling will not likely become obsolete, but their roles will shift in a way that complements new predictive technological features and abilities, and they will need to acquire new skills to excel in these new roles.
Advances in predictive technology are extremely promising in terms of commercial and scientific value creation, but they do require risk mitigation as well. Some of these risks center on data privacy and security. With exponential increases in data volume, the importance of protecting data from hackers and mitigating other privacy concerns increase as well. Additionally, researchers point out the risk of hard wiring overt and unconscious societal biases into predictive models and algorithms, an issue that will be of great importance to policymakers and big technology companies.
Despite its numerous high-value benefits, predictive modeling certainly has its limitations. Unless certain conditions are met, predictive modeling may not provide the entirety of its potential value. In fact, if these conditions are not met, predictive models may not provide any value over legacy methods or conventional wisdom. It is important to consider these limitations to capture the maximum amount of value from predictive modeling initiatives. According to McKinsey and Co., which recently analyzed use cases, value creation, and limitations, here are some of the challenges:
Especially in Machine Learning, in which a computer is constructing the predictive model, data must be labeled and categorized appropriately. This process can be imprecise, full of errors, and a generally colossal undertaking. However, it is a necessary component of constructing a model, and, if proper classification and labeling cannot be completed, any predictive model produced will suffer from poor performance and issues associated with improper categorization.
In order for statistical methods to be consistently successful at predicting outcomes, a basic tenet needs to be met: sufficient sample size. If a predictive modeling professional doesn’t have sufficient amounts of data to construct the model, the model produced will be unduly influenced by noise in the data that is used. Of course, relatively small datasets tend to exhibit more variation or, in other words, more noise. Currently, the number of records required to reach sufficiently high model performance ranges from the thousands to the millions. In addition to size, the data used must be representative of the target population. If the sample size is large enough, the data should have a wide variety of records, including unique or odd cases, to refine the model.
As more complex and esoteric models and methodologies become available, it will often be a great challenge to untangle models to determine why a certain decision or prediction was made. As models intake more data records or more variables, factors that could explain predictions become murky, a significant limitation in some fields. In industries or use cases that require explainability, such as environments that have significant legal or regulatory consequences, the need to document processes and decisions can hinder the use of complex models. This limitation will likely drive demand for new methodologies that can handle huge data volumes and complexities while also remaining transparent in decision making.
Generalizability refers to the ability of the model to be generalized from one use case to another. Unlike humans, models tend to struggle with generalizability, also known as external validity. In general, when a model is constructed for a particular case, it should not be used for a different case. Although methods like transfer learning, an approach that attempts to remedy this very issue, are in development, generalizability remains a significant limitation of predictive modeling.
Though it’s more of an ethical or philosophical issue than a technical one, some argue that researchers and professionals creating predictive models must be careful when choosing which data to use and which to exclude. Because historical biases can be engrained at the lowest level of data, great care must be taken when attempting to address these biases, or their repercussions could be perpetuated into the future by predictive models.
Recognized in the technology industry for its distinctive yellow elephant logo, Apache Hadoop, commonly referred to as Hadoop, is a collection of open source software utilities that are designed to help a network of computers work together on tasks that involve massive quantities of data. Hadoop mainly functions as a storage and processing utility. The processing utility is a MapReduce programming model. Hadoop can also refer to a number of additional software packages in the Apache Hadoop ecosystem. These packages include:
Hadoop has become extremely useful and important in the field of predictive modeling, especially for models or problems that require big data storage. Predictive modeling professionals with skills or expertise in the Hadoop ecosystem, especially MapReduce and packages like Apache Hive, can find a salary premium for those skills.
R is an open-source programming language for statistical computing and graphics. Analysts will require technical skills to work efficiently with this tool. It includes capabilities such as linear regression, non-liner modeling, and time-series tests. Use cases include:
Python is a high-level programming language made for general programming. While R was built specifically for statistics, Python exceeds R when it comes to data mining, imaging, and data flow capabilities. It’s more versatile than R and more commonly used with other programs. Python is generally easier to learn than R, and is best used for task automation.
MicroStrategy is an enterprise analytics and mobility platform which includes R, Python, and Google Analytics integration. It has 60+ data source connectors, so analysts can gain insights by blending disparate data. This data can be output into data visualizations and dashboard reports to gain insights quickly, and can be easily shared throughout the organization. MicroStrategy also includes advanced analytics capabilities, including predictive analytics, with over 300 native analytics functions and open source and 3rd party statistical programs. Some examples include:
Predictive modeling is a field poised for high growth in the coming years due to the explosion of data, technological advances, and proven value add capability. In fact, in 2017, IBM forecasted that demand for data science and analytics professionals would grow by 15% by the year 2020.
While many companies know they need to apply predictive modeling to their businesses, there is currently a shortage of candidates with the appropriate skillsets. Because of this, businesses have offered substantial salaries to qualified applicants in order to lure them away from competitors or other jobs. While the number of qualified candidates is increasing, the demand for such professionals is growing at a significant rate.
Some common job titles include:
Salaries vary depending on a candidate’s background and the company’s need, but data science skills translate into higher salaries. Some of the skills that pull higher salaries are MapReduce, Apache Hive, and Apache Hadoop.