Your business can use predictive analytics to dive deep into historical data and pair it with current trends to predict future outcomes—enabling your company to adapt to changes within the marketplace and remain competitive.

Predictive analytics isn’t new, but more companies—across all industries—are realizing the value of being able to predict future outcomes related to all aspects of their business using data. In this blog, we’ll cover how predictive analytics fits into the advanced analytics journey, how you can use it within your own business, and how to properly set up a predictive analytics implementation.

What is predictive analytics?

Simply put, predictive analytics is a form of advanced analytics that determines what is likely to happen based on historical data using machine learning.

What Makes Predictive Analytics Different from Other Types of Analytics?

To understand how predictive analytics can give your business an edge, you need to first put it into context with how other types of analytics work.

  • Descriptive analytics tells you what happened in the past by looking at historical data and finding patterns. Most organizations with some level of maturity on their analytics journey are already doing some degree of descriptive analytics.
  • Diagnostic analytics helps organizations understand the “why” behind the “what” of descriptive analytics. It enables better decision-making, as well as generates better predictive use cases through that understanding.
  • Predictive analytics also uses historical data but predicts what will happen in the future.
  • Prescriptive analytics goes one step further by recommending automated actions you can take to affect those outcomes. It is about empowering the people who are making decisions to make the right decisions based on predictions.

What Are the Different Use Cases for Predictive Analytics?

Predictive analytics can be very valuable to any organization, in any industry, and in almost all lines of business by providing insights into future outcomes. It enables business users to plan ahead, avoid missed opportunities, and preemptively make more informed decisions. Here are a few different ways you can use predictive analytics within your organization:

  • Sales Forecasting: When it comes to sales forecasting, the ability to home in on opportunity, avoid mistakes, and build relationships is key. Predictive analytics might assess historical data on purchasing activities and link it with trends such as customer behavior and weather patterns to forecast sales opportunities for any given period of time. Predictive analytics can also provide insights into the types of products and services that will be in demand, giving your organization the ability to maximize on those sales opportunities.
  • Marketing Analysis: The marketing function within any organization opens doors to new business as well as keeps the door open for existing relationships. Predictive analytics can be used to better understand how to do both effectively. It can be used to predict and avoid customer churn by identifying signs of dissatisfaction. It can be used to identify sales opportunities and create campaigns to move customers through the pipeline. And it can be used to understand how your customers interact with your business and allow you to make it easier for them to do so.
  • Product Maintenance: Predicting maintenance issues and preventing machines from breaking down is critical for the manufacturing industry. Costs related to a slowdown of production can far outweigh the cost of repair. Predictive analytics can use real-time data to accurately predict when a machine may breakdown, allowing the business to address it before it causes a sequalae of problems.
  • Credit Risk and Fraud Prevention: Within the finance industry, as well as the finance line of business, determining credit risk and identifying fraud is a top priority in conducting business. Predictive analytics can be used to learn potential areas of risk from various data points, enabling the organization to make more informed decisions. It can also be used to identify and prevent fraudulent transactions by monitoring and flagging transactions that stray from typical or expected behavior.

What Are the Steps for A Predictive Analytics Implementation?

Before you use predictive analytics, you must understand the why, what, and how behind implementing it within your organization. Here are six steps to get started with predictive analytics:

1.) Define Your Objectives and Goals

There is no point in creating a predictive analytics model without knowing why you are doing so. To define your objectives and goals, you need to:

  • Identify a problem to solve. A good place to start is with already existing KPIs because you know what your targets are, and chances are you’ve got some good insight on what’s influencing those targets.
  • Define what it is you want to predict and what you will achieve by doing so. Be specific about what decisions that can be made by the organization based on the predictions that are being made.

2.) Prep and Profile Your Data

Data prep is about the organization of your data, and data profiling is about understanding what’s in your data. As data sources continue to multiply, it becomes even more important to put an emphasis on data quality so that what you are using for your predictive analytics model is trustworthy and capable of meeting your defined objectives and goals. To start, you should:

  • Collect existing data. There are all types of data available—whether it’s from transactional or operational systems, or third-party. Pull data that is relevant to what you are looking to predict and integrate it into one place—whether it’s a data lake or a data warehouse. You may have disparate systems that don’t talk to each other, so data collection is a critical first step.
  • Organize data in a useful way to allow for data modeling. You might have good data, but it might not be organized well enough to be able to see. A data governance program can be useful in organizing your data.
  • Impute and cleanse your data. This will help ensure data accuracy and in refining your data sets.
  • Review data quality. Without high quality data, the outcome will be flawed and untrustworthy. Look at summary statistics about targets and features to understand things like mean, variance, data normality, etc., which helps in determining predictive power.
  • Determine modeling objective. Make sure to understand the types of outcomes you are trying to achieve. If your target is binary, you likely need to do classification. If your target is numeric, perhaps you are looking to make linear predictions.  Regardless, it’s essential to understand how your data features and targets might fit into the right modeling types.

3.) Model Your Data

Modeling your data allows you to create, train, and test a machine learning data model that can be used to forecast the probability of something happening, or project specific numeric outcomes. The two most common types of predictive analytics models include classification models and regression models.

  • A classification model puts data into categories or classes based on what it learns from historical data and is typically used to answer yes/no questions.
  • A regression model is used to determine the best fit of predictor values and target values in determining predictor strength, forecast over time, or a cause-and-effect relationship.

A predictive data model—whether classification or regression—includes objects or concepts you want to track data about, and relationships, or business rules, among them. The goal in modeling your data is to create predictive data models that can be automated and that can be scaled. When modeling your data, you need to:

  • Train your data within different models (e.g. linear regression vs. Bayesian linear regression) and score them for things like accuracy, precision, and time. The larger the data sample data sets, the more likely you are to have accurate results.
  • Test your trained models using different modeling techniques. Some types of modeling techniques include linear regression (as discussed above), logistic regression (generally used for binary or categorical outcomes), decision trees (laying out probability paths), K-nearest neighbor (feature similarity), or neural networks (deep learning or pattern recognition).

Predictive models can be built with your coding language of choice—the most popular being Python or R. This can be done through a Jupyter Notebook, DataBricks, Azure ML, or Amazon SageMaker to name a few platforms. Additionally, there are many platforms that that allow users to do machine learning with limited coding knowledge, or via AutoML, such as DataRobot. Whatever the model type selected, there might be additional variations that can be used in experiments, and then scored to see which is the most accurate and the most efficient, and then validating against new data based on these results.

4.) Validate Results

Once you have trained and tested your data model, you should validate the results. It’s important to make sure that you are comfortable with the results prior to deploying into operations, as a bad model may break, or questionable results may result in limited adoption or trust.

5.) Deploy Your Predictive Analytics Model

After the model has been validated, it is time to deploy it in a real environment and let it get to work. Operationalize your results by embedding them into applications or dashboards where they can be immediately used.

6.) Monitor Your Predictive Data Model

Once you have deployed your predictive data model, it is essential to monitor it and its performance. Just because a data model works now, does not mean that there won’t be unforeseen situations that can cause data drifts in the future. Continually review your data model and put into place the ability to adjust on the fly based on data changes. There are many ways to effectively monitor your data, but two in particular should be done to ensure trust, continued adoption, and accuracy over time:

  • Create a dashboard to monitor your expected results against your actual results. If you see that the results are drifting apart, then it is an indication that something is wrong with your predictive data model and you should make adjustments.
  • Create a dashboard to track results of business recommendations made using the predictive data model and compare between business users following the recommendations and those that aren’t. If there isn’t a difference, then that is an indication that you need to adjust your predictive data model, or make sure that your end users are adopting the recommendations based on predictions.

You don’t have to dive in headfirst with predictive analytics. You can start with a small segment of your business and test out a predictive analytics model to see how it works. As you learn and see results, build on that and scale across other segments of your organization. Although you can’t predict the future with absolute certainty, predictive analytics will get you closer to understanding future outcomes and how you can prepare to meet them.

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