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Tech Accelerator What is predictive analytics? An enterprise guide
Prev Next 6 challenges of building predictive analytics models Predictive
analytics vs. machine learning

Definition


PREDICTIVE MODELING


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By
 * George Lawton
 * Joseph M. Carew
 * Ed Burns


WHAT IS PREDICTIVE MODELING?

Predictive modeling is a mathematical process used to predict future events or
outcomes by analyzing patterns in a given set of input data. It is a crucial
component of predictive analytics, a type of data analytics which uses current
and historical data to forecast activity, behavior and trends.

Examples of predictive modeling include estimating the quality of a sales lead,
the likelihood of spam or the probability someone will click a link or buy a
product. These capabilities are often baked into various business applications,
so it is worth understanding the mechanics of predictive modeling to
troubleshoot and improve performance.

Although predictive modeling implies a focus on forecasting the future, it can
also predict outcomes (e.g., the probability a transaction is fraudulent). In
this case, the event has already happened (fraud committed). The goal here is to
predict whether future analysis will find the transaction is fraudulent.
Predictive modeling can also forecast future requirements or facilitate what-if
analysis.

"Predictive modeling is a form of data mining that analyzes historical data with
the goal of identifying trends or patterns and then using those insights to
predict future outcomes," explained Donncha Carroll a partner in the revenue
growth practice of Axiom Consulting Partners. "Essentially, it asks the
question, 'have I seen this before' followed by, 'what typically comes after
this pattern.'"

This article is part of


WHAT IS PREDICTIVE ANALYTICS? AN ENTERPRISE GUIDE

 * Which also includes:
 * Predictive analytics vs. machine learning
 * 7 top predictive analytics use cases: Enterprise examples
 * Descriptive vs. prescriptive vs. predictive analytics explained




TOP TYPES OF PREDICTIVE MODELS

There are many ways of classifying predictive models and in practice multiple
types of models may be combined for best results. The most salient distinction
is between unsupervised versus supervised models.

 * Unsupervised models use traditional statistics to classify the data directly,
   using techniques like logistic regression, time series analysis and decision
   trees.
 * Supervised models use newer machine learning techniques such as neural
   networks to identify patterns buried in data that has already been labeled.

The biggest difference between these approaches is that with supervised models
more care must be taken to properly label data sets upfront.

"The application of different types of models tends to be more domain-specific
than industry-specific," said Scott Buchholz, government and public services CTO
and emerging technology research director at Deloitte Consulting.

In certain cases, for example, standard statistical regression analysis may
provide the best predictive power. In other cases, more sophisticated models are
the right approach. For example, in a hospital, classic statistical techniques
may be enough to identify key constraints for scheduling, but neural networks, a
type of deep learning, may be required to optimize patient assignment to
doctors.

Once data scientists gather this sample data, they must select the right model.
Linear regressions are among the simplest types of predictive models. Linear
models take two variables that are correlated -- one independent and the other
dependent -- and plot one on the x-axis and one on the y-axis. The model applies
a best fit line to the resulting data points. Data scientists can use this to
predict future occurrences of the dependent variable.

Some of the most popular methods include the following:

 * Decision trees. Decision tree algorithms take data (mined, open source,
   internal) and graph it out in branches to display the possible outcomes of
   various decisions. Decision trees classify response variables and predict
   response variables based on past decisions, can be used with incomplete data
   sets and are easily explainable and accessible for novice data scientists.
 * Time series analysis. This is a technique for the prediction of events
   through a sequence of time. You can predict future events by analyzing past
   trends and extrapolating from there.
 * Logistic regression. This method is a statistical analysis method that aids
   in data preparation. As more data is brought in, the algorithm's ability to
   sort and classify it improves and therefore predictions can be made.
 * Neural networks. This technique reviews large volumes of labeled data in
   search of correlations between variables in the data. Neural networks form
   the basis of many of today's examples of artificial intelligence (AI),
   including image recognition, smart assistants and natural language
   generation.

The most complex area of predictive modeling is the neural network. This type of
machine learning model independently reviews large volumes of labeled data in
search of correlations between variables in the data. It can detect even subtle
correlations that only emerge after reviewing millions of data points. The
algorithm can then make inferences about unlabeled data files that are similar
in type to the data set it trained on.

Predictive modeling algorithms include logistic regression, time series analysis
and decision trees.



COMMON ALGORITHMS FOR PREDICTIVE MODELING

 * Random Forest. This algorithm combines unrelated decision trees and uses
   classification and regression to organize and label vast amounts of data.
 * Gradient boosted model. Similar to Random Forest, this algorithm uses several
   decision trees, but in this method, each tree corrects the flaws of the
   previous one and builds a more accurate picture.
 * K-Means. This algorithm groups data points in a similar fashion as clustering
   models and is popular in devising personalized retail offers. It create
   personalized offers by seeking out similarities among large groups of
   customers.
 * Prophet. A forecasting procedure, this algorithm is especially effective when
   dealing with capacity planning. This algorithm deals with time series data
   and is relatively flexible.

A neural network is a type of predictive model that independently reviews large
volumes of labeled data in search of correlations between variables in the data.



WHAT ARE THE USES OF PREDICTIVE MODELING?

Predictive modeling is often associated with meteorology and weather
forecasting, but predictive models have many applications in business. Today's
predictive analytics techniques can discover patterns in the data to identify
upcoming risks and opportunities for an organization.

"Almost anywhere a smart human is regularly making a prediction in a
historically data rich environment is a good use case for predicative
analytics," Buchholz said. "After all, the model has no ego and won't get
bored."

One of the most common uses of predictive modeling is in online advertising and
marketing. Modelers use web surfers' historical data, to determine what kinds of
products users might be interested in and what they are likely to click on.

Bayesian spam filters use predictive modeling to identify the probability that a
given message is spam.

In fraud detection, predictive modeling is used to identify outliers in a data
set that point toward fraudulent activity. In customer relationship management,
predictive modeling is used to target messaging to customers who are most likely
to make a purchase.

Carroll said that predictive modeling is widely used in predictive maintenance,
which has become a huge industry generating billions of dollars in revenue. One
of the more notable examples can be found in the airline industry where
engineers use IoT devices to remotely monitor performance of aircraft components
like fuel pumps or jet engines.

These tools enable preemptive deployment of maintenance resources to increase
equipment utilization and limit unexpected downtime. "These actions can
meaningfully improve operational efficiency in a world that runs just in time
where surprises can be very expensive," Caroll said.

Other areas where predictive models are used include the following:

 * capacity planning
 * change management
 * disaster recovery
 * engineering
 * physical and digital security management
 * city planning


HOW TO BUILD A PREDICTIVE MODEL

Building a predictive model starts with identifying historical data that's
representative of the outcome you are trying to predict.

"The model can infer outcomes from historical data but cannot predict what it
has never seen before," Carroll said. Therefore, the volume and breadth of
information used to train the model is critical to securing an accurate
prediction for the future.

The next step is to identify ways to clean, transform and combine the raw data
that leads to better predictions.

Skill is required in not only finding the appropriate set of raw data but also
transforming it into data features that are most appropriate for a given model.
For example, calculations of time-boxed weekly averages may be more useful and
lead to better algorithms than real-time levels.

It is also important to weed out data that is coincidental or not relevant to a
model. At best, the additional data will slow the model down, and at worst, it
will lead to less accurate models.

This is both an art and a science. The art lies in cultivating a gut feeling for
the meaning of things and intuiting the underlying causes. The science lies in
methodically applying algorithms to consistently achieve reliable results, and
then evaluating these algorithms over time. Just because a spam filter works on
day one does not mean marketers will not tune their messages, making the filter
less effective.

Analyzing representative portions of the available information -- sampling --
can help speed development time on models and enable them to be deployed more
quickly.


BENEFITS OF PREDICTIVE MODELING

Phil Cooper, group VP of products at Clari, a RevOps software startup, said some
of the top benefits of predictive modeling in business include the following:

 * Prioritizing resources. Predictive modeling is used to identify sales lead
   conversion and send the best leads to inside sales teams; predict whether a
   customer service case will be escalated and triage and route it
   appropriately; and predict whether a customer will pay their invoice on time
   and optimize accounts receivable workflows.
 * Improving profit margins. Predictive modeling is used to forecast inventory,
   create pricing strategies, predict the number of customers and configure
   store layouts to maximize sales.
 * Optimizing marketing campaigns. Predictive modeling is used to 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.
 * Reducing 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.

The techniques used in predictive modeling are probabilistic as opposed to
deterministic. This means models generate probabilities of an outcome and
include some uncertainty.

"This is a fundamental and inherent difference between data modeling of
historical facts versus predicting future events [based on historical data] and
has implications for how this information is communicated to users," Cooper
said. Understanding this difference is a critical necessity for transparency and
explainability in how a prediction or recommendation was generated.


CHALLENGES OF PREDICTIVE MODELING

Here are some of the challenges related to predictive modeling.

Data preparation. One of the most frequently overlooked challenges of predictive
modeling is acquiring the correct amount of data and sorting out the right data
to use when developing algorithms. By some estimates, data scientists spend
about 80% of their time on this step. Data collection is important but limited
in usefulness if this data is not properly managed and cleaned.

Once the data has been sorted, organizations must be careful to avoid
overfitting. Over-testing on training data can result in a model that appears
very accurate but has memorized the key points in the data set rather than
learned how to generalize.

Technical and cultural barriers. While predictive modeling is often considered
to be primarily a mathematical problem, users must plan for the technical and
organizational barriers that might prevent them from getting the data they need.
Often, systems that store useful data are not connected directly to centralized
data warehouses. Also, some lines of business may feel that the data they manage
is their asset, and they may not share it freely with data science teams.

Choosing the right business case. Another potential obstacle for predictive
modeling initiatives is making sure projects address significant business
challenges. Sometimes, data scientists discover correlations that seem
interesting at the time and build algorithms to investigate the correlation
further. However, just because they find something that is statistically
significant does not mean it presents an insight the business can use.
Predictive modeling initiatives need to have a solid foundation of business
relevance.

Bias. "One of the more pressing problems everyone is talking about, but few have
addressed effectively, is the challenge of bias," Carroll said. Bias is
naturally introduced into the system through historical data since past outcomes
reflect existing bias.

Nate Nichols, distinguished principal at Narrative Science, a natural language
generation tools provider, is excited about the role that new explainable
machine learning methods such as LIME or SHAP could play in addressing concerns
about bias and promoting trust.

"People trust models more when they have some understanding of what the models
are doing, and trust is paramount for predictive analytic capabilities," Nichols
said. Being able to provide explanations for the predictions, he said, is a huge
positive differentiator in the increasingly crowded field of predictive analytic
products.


PREDICTIVE MODELING VERSUS PREDICTIVE ANALYTICS

Predictive modeling is but one aspect in the larger predictive analytics process
cycle. This includes collecting, transforming, cleaning and modeling data using
independent variables, and then reiterating if the model does not quite fit the
problem to be addressed.

"Once data has been gathered, transformed and cleansed, then predictive modeling
is performed on the data," said Terri Sage, chief technology officer at
1010data, an analytics consultancy.

Collecting data, transforming and cleaning are processes used for other types of
analytic development.

"The difference with predictive analytics is the inclusion and discarding of
variables during the iterative modeling process," Sage explained.

This will differ across various industries and use cases, as there will be
diverse data used and different variables discovered during the modeling
iterations.

For example, in healthcare, predictive models may ingest a tremendous amount of
data pertaining to a patient and forecast a patient's response to certain
treatments and prognosis. Data may include the patient's specific medical
history, environment, social risk factors, genetics -- all which vary from
person to person. The use of predictive modeling in healthcare marks a shift
from treating patients based on averages to treating patients as individuals.

Similarly, with marketing analytics, predictive models might use data sets based
on a consumer's salary, spending habits and demographics. Different data and
modeling will be used for banking and insurance to help determine credit ratings
and identify fraudulent activities.


PREDICTIVE MODELING TOOLS

Before deploying a predictive model tool, it is crucial for your organization to
ask questions and sort out the following: Clarify who will be running the
software, what the use case will be for these tools, what other tools will your
predictive analytics be interacting with, as well as the budget.

Different tools have different data literacy requirements, are effective in
different use cases, are best used with similar software and can be expensive.
Once your organization has clarity on these issues, comparing tools becomes
easier.

 * Sisense. A business intelligence software aimed at a variety of companies
   that offers a range of business analytics features. This requires minimal IT
   background.
 * Oracle Crystal Ball. A spreadsheet-based application focused on engineers,
   strategic planners and scientists across industries that can be used for
   predictive modeling, forecasting as well as simulation and optimization.
 * IBM SPSS Predictive Analytics Enterprise. A business intelligence platform
   that supports open source integration and features descriptive and predictive
   analysis as well as data preparation.
 * SAS Advanced Analytics. A program that offers algorithms that identify the
   likelihood of future outcomes and can be used for data mining, forecasting
   and econometrics.


THE FUTURE OF PREDICTIVE MODELING

There are three key trends that will drive the future of data modeling.

 1. First, data modeling capabilities are being baked into more business
    applications and citizen data science tools. These capabilities can provide
    the appropriate guardrails and templates for business users to work with
    predictive modeling.
 2. Second, the tools and frameworks for low-code predictive modeling are making
    it easier for data science experts to quickly cleanse data, create models
    and vet the results.
 3. Third, better tools are coming to automate many of the data engineering
    tasks required to push predictive models into production. Carroll predicts
    this will allow more organizations to shift from simply building models to
    deploying them in ways that deliver on their potential value.

This was last updated in January 2022

NEXT STEPS

14 most in-demand data science skills you need to succeed

What is data mining?

7 top predictive analytics use cases: Enterprise examples

Predictive analytics vs. machine learning

CONTINUE READING ABOUT PREDICTIVE MODELING

 * Ten steps to start using predictive analytics algorithms effectively

 * Beat the challenges of predictive analytics in big data systems

 * Talking Data podcast: Predictive modeling techniques

 * Faster modeling techniques in predictive analytics pay off



RELATED TERMS

data engineer A data engineer is an IT professional whose primary job is to
prepare data for analytical or operational uses. See complete definition Gemma
Gemma is a collection of lightweight open source generative AI models designed
mainly for developers and researchers. See complete definition model card in
machine learning A model card is a type of documentation that is created for,
and provided with, machine learning models. See complete definition

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