*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
Predictive analytics looks at current and historical data patterns to make future predictions. This brand of data science can help you gain insights into both your customers and business operations and uncover opportunities for growth and improvement. Read on to learn everything about predictive analytics and how you can start tapping into your company’s data today.
Predictive analytics uses data, statistical algorithms, and machine learning techniques to make predictions on future outcomes by looking at current and historical data patterns.
Predictive analytics is essentially a branch of data science and is an extremely valuable tool that can be used to solve difficult problems within your business or organization and help to uncover new opportunities and areas of improvement.
You, not just a data scientist, can use predictive analytics to extract insights and make more informed decisions, such as forecasting your inventory needs to manage your resources or determining ways to optimize marketing campaigns.
This blog will tell you everything you need to know about the significance of predictive analytics in the field of data science, as well as the many ways predictive analytics can be applied and how it can improve your business or organization.
Table of Contents
The purpose of predictive analytics is to take a closer look at current and historical data patterns to determine if those patterns are likely to emerge again in the future. This practice allows you to adjust how you’re using your time and resources to take advantage of future opportunities.
To make these predictions, predictive analytics relies on technology and techniques like AI, data mining, machine learning, and statistics. Predictive modeling is one of the commonly used techniques in predictive analytics.
With predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is then validated — or revised — as more data becomes available. Predictive modeling can be used in various scenarios, from calculating the probability a customer will churn to evaluating the risks and opportunities of customer transactions.
Predictive analytics, in general, is a widely used tool as being proactive and well-positioned for the future is one of the best things a company can do to set itself up for success. Countless industries use predictive analytics in their operations, including:
- Weather Forecasting
For example, retailers use predictive analytics to gather insights to forecast demand for certain products which allows them to adjust their inventory effectively. In healthcare, predictive analytics is used to analyze data and predict disease outbreaks. And in the insurance industry, predictive analytics is used to evaluate claims and identify future risk factors.
There are several elements of predictive analytics that are necessary to make the process work and yield reliable results. From start to finish, the key components of predictive analytics include:
- Data Collection and Preprocessing
- Feature Selection and Engineering
- Model Training and Evaluation
- Prediction and Interpretation of Results
First and foremost, you need to have a plan or an idea of what you’d like to accomplish by performing predictive analytics. With a plan in mind, you can start collecting data. You can gather data in many ways, whether it be internal organizational data collected via surveys or from external sources.
When collecting data, you should be aware of the type of data you’re collecting or want to collect. Consider quality and quantity, as well as whether you’re going after qualitative or quantitative data or a mix of the two.
Once you’ve gathered all the data, you can start preprocessing. This is the practice of preparing raw data to be fed into the algorithm. Preprocessing includes cleaning up the data by splitting it into training, validation, and testing sets, addressing missing values, and dealing with outliers. Luckily, there are several software options out there that can make this process smoother.
Another important component is feature selection and engineering, which is necessary for any machine learning model. This is the process of selecting, manipulating, and transforming raw data into features that are used in supervised or unsupervised learning. A “feature” is any measurable input that is used in a predictive model.
Feature selection and engineering essentially create new variables to simplify and speed up data transformations while maintaining and enhancing the model’s accuracy. When done correctly, the resulting dataset is fully optimized and contains all the potential factors that could have an impact on your business.
With all the data ready to go, it’s time to build the predictive model. During the model-building process, you’ll create, test and validate your predictive model to yield the best possible results. There are several predictive models to choose from, including classification, clustering, and time series.
When creating the model, data is typically divided into two sets — training and testing. The training data is used to build and train the model, and when it’s ready, the testing data is used to see how well the model performs and determine its accuracy. A predictive model isn’t ready to be put into action until it has been trained and tested thoroughly.
Once the model is launched, it’s important to monitor it and regularly evaluate its performance. You’ll need to have established metrics and key performance indicators (KPIs) that you’ll be tracking over time. You can also set up automated performance analytics that will send you alerts when something changes.
It’s expected that you’ll eventually need to revise or even retire the model. But it’s important to monitor it to track how fast it degrades over time. The faster it goes downhill, the more likely it is that the model is faulty in some areas.
Once your model starts working, you’ll be able to begin pulling insights to make predictions. But how exactly are you supposed to explain and interpret the results of your predictive models?
There are several ways to do this. You can visualize your results in graphs, charts, tables, and so on, making it easy for anyone to look at your summary and get a clear picture of your findings. Another way of interpreting results is to use historical and current data to show how your results relate to trends and scenarios over time. You can also use benchmarks to show how your findings compare to the normal, expected, and optimal outcomes.
Predictive analytics can uncover so much about your business or organization, revealing opportunities you otherwise would have missed out on. Some of the key benefits of using predictive analytics in data science include:
- Extracting Actionable Insights
- Identifying Patterns and Trends
- Improving Decision-Making
- Enhancing Efficiency and Reducing Costs
One of the biggest benefits of predictive analytics is the ability to extract actionable insights, which can be used to improve countless areas of your business.
For example, by analyzing data, you may find that your customers respond best to your marketing efforts on social media rather than through email. Knowing this, you can tailor your efforts accordingly to improve your connection with your audience. Predictive analytics can also provide insights on risk and even fraud detection, helping you identify areas where you can strengthen your operations.
Predictive analytics is generally used to identify patterns and trends that help businesses and organizations identify risks and opportunities. Identifying patterns and trends can help you anticipate and plan for the future.
For example, suppose you discover that you see an increase in sunscreen sales during the summer months but a significant drop in sales during the winter. In that case, you can adjust your inventory to meet your customers’ needs and avoid having an influx of stock that is collecting dust on the shelves. You can also forecast customer behavior patterns and market trends. This keeps you ahead of the game, which could also provide your business with a competitive edge.
Predictive analytics allows you to make more advanced, data-driven decisions. The more data available and the more it’s analyzed, the more efficient your decision-making will be.
By using predictive analytics to pull insights and identify trends and patterns, you can use this information to fuel your business decisions to determine the best practices for increasing profits, efficiency and improving your business operations as a whole.
Predictive analytics can also be used to reduce supply chain disruptions and unnecessary costs, enhancing a business’s overall efficiency. Supply chain disruptions can have massive impacts on both the individual business — as seen in reduced profits — as well as the entire industry.
For example, consider how supply chain disruptions in the oil and gas industry impact fuel prices. With predictive analytics, you can rely on data to identify trends and supply chain needs ahead of time to avoid these issues and allow your business to run more efficiently.
Predictive analytics can be quite vast, and you can employ several different techniques and algorithms depending on your data and goals. Some of the main predictive analytics techniques and algorithms include:
- Regression Analysis
- Classification Algorithms
- Time Series Forecasting
- Ensemble Methods and Machine Learning Algorithms
A regression analysis is used to determine the relationship between two variables — called a single linear regression — or three or more variables, which is called a multiple regression. The analysis shows how the independent variables change with the dependent variables and what factors impact those changes the most. For example, a business could use regression analysis to predict next month’s sales — which would be the dependent variable — while analyzing how factors like weather, competitors, and price — the independent variables — will impact the number of sales.
Classification algorithms or models are one of the simplest predictive analytics models. It works by categorizing information based on historical data. Classification models are good at answering yes or no questions like “Will this customer shop here again?” or “Is this transaction fraudulent?” Because of its simplicity and broad application, classification algorithms are widely used across several industries.
Time series forecasting evaluates a sequence of data points over time. By analyzing these data points, the algorithm develops a metric that can predict trends within a specified period. Time series models can be used to measure sales over quarters, predict hospital admissions, and any other time-oriented aspect of your business operations.
Ensemble methods are machine learning techniques combining multiple similar models simultaneously to create one optimized predictive model. There are several factors and techniques that go into using machine learning algorithms and creating a successful ensemble method. However, ensemble methods often yield more accurate solutions than a single model would.
Like most things, predictive analytics isn’t without its challenges. The data user needs to meet several requirements to effectively run the model, and this can lead to challenges along the way, including:
- Ensuring Data Quality
- Balancing Overfitting and Under-Fitting Models
- Ethical Considerations
For predictive models to work, you need quality data. This means all the data needs to be cleaned and preprocessed before the model can run. This includes getting rid of errors like inconsistent data, duplicates, and old, irrelevant data. It’s also important to ensure your data set doesn’t have any holes. Missing data points could throw off an entire data set, and the same goes for any extreme outliers.
When working with data, predictive analytics, and machine learning algorithms, it’s important to strive for balance. Overfitting arises when a model fits perfectly against the training data. This may sound great, but when this happens, it means the model isn’t equipped to address unseen data. On the other hand, underfitting is a scenario in which a model can’t effectively represent the connection between input and output variables, yielding a high error rate. This typically happens when a model is too simple and needs more training time.
Instead of falling into one of these extremes, you should strive for a well-balanced model — otherwise called a good fit model. This is a model that has no underfitting or overfitting and performs with high levels of accuracy. It can be difficult and take time to reach a perfect balance, but there are several ways to detect overfitting and underfitting and then make appropriate adjustments to your model.
Any practice that works with data is going to come with ethical considerations, and predictive analytics is no different. One of the primary concerns is data privacy. As data is often collected unknowingly, people have concerns about who has access to their data, how it’s being used, and what their rights are when it comes to controlling their personal information.
Another major concern is data bias and discrimination. If the data gathered is biased, the predictive model will be biased, too. This could lead to perpetuating stereotypes and discriminating against groups of people unintentionally.
It’s also important to be transparent about how collected data is being used. Ethically, the right thing to do is to be clear from the beginning what your intentions for the data are and ensure that you’re not straying away from what you told people the data was for.
Armed with all this information about predictive analytics, its benefits, and its challenges, you’re probably wondering how it can be used. Some applications of predictive analytics include:
- Customer Segmentation and Behavior Analysis
- Fraud Detection and Risk Assessment
- Demand Forecasting
- Predictive Maintenance
Predictive analytics can be a useful tool in the marketing world for customer segmentation and behavior analysis. Customer segmentation is the process of splitting your audience into smaller groups with shared characteristics like certain demographics and behaviors. Creating these segments allows you to target each group and cater to their individual needs.
Behavior analysis is also useful as it measures how your customers interact with your brand. Collecting and analyzing behavioral data is another way to get to know your target audience and determine the best ways to reach them.
Because predictive models are skilled in accurate pattern detection, they can be used to catch criminal behavior ahead of time to prevent fraud. This is especially valuable as concerns about cybersecurity are ever-present.
Predictive analytics can also be used for risk management and assessment. By analyzing past and present data to make predictions, you can factor in risks and their associated downsides into your business decisions. Predictive modeling can even enhance risk assessment by having the ability to scan thousands of data sets at once, generating a long list of actionable insights.
A large use case for predictive analytics is demand forecasting and developing demand models to predict future sales and revenue, which are used to create budgets. Demand forecasting is crucial to supply chain optimization. For example, you may see more sales during the holiday season, so knowing that you can adjust your inventory for the increased demand.
Predictive maintenance uses data analysis tools and techniques to detect anomalies in your operation, defective equipment, and processes and helps you determine how to fix them before they ultimately fail. It essentially uses historical and current data to anticipate problems before they arise.
Similarly, anomaly detection identifies outliers and their contributing drivers. Detecting anomalies is useful because it can help detect potential problems early, like fraudulent transactions. Plus, the techniques used in anomaly detection can be used to build more robust data science models.
Predictive analytics can be used in a variety of industries and have a significant impact on business operations. It’s not a novel concept, though, and several major companies have already used predictive analytics with great success, including:
Amazon uses predictive analytics to analyze individual customers’ browsing and purchase history to predict their behavior. With this data, Amazon can determine with a strong degree of accuracy how a customer will behave in the future, which allows them to make targeted product recommendations.
Making these targeted recommendations is a great way to boost sales. As a customer is shopping and checking out, Amazon will show similar products or products that are frequently bought together to get you to add more to your cart.
Major car manufacturer Volvo used AI and predictive analytics to predict when a car needs to be serviced and, specifically, what parts need to be either replaced or repaired. Through its Early Warning System, Volvo analyzes parts to predict each one’s specific breakdown rate.
The information from the Early Detection System can fuel service recommendations and maintenance plans to serve customers before the part fully breaks, creating a bigger problem.
Progressive, one of America’s largest insurers, uses predictive analytics to analyze customer-driving data. Progressive has collected billions and billions of miles of driving data which it then puts in an algorithm to better understand the factors that contribute to certain driving issues and predict what customers may be at a higher risk for an accident.
Several insurance companies, like Progressive, use telematics apps to gather and analyze customer data, which helps them adjust their offerings to make policies more accurate, which in turn helps save customers money.
Predictive analytics is the process of using data, algorithms, and machine learning techniques to make predictions about future outcomes by looking at current and historical data patterns. It’s a valuable tool that can be used to solve difficult problems within your business and help uncover new opportunities and areas of improvement.
Predictive analytics can be used to detect fraud, improve overall decision-making, reduce costs, better serve customers and enhance the efficiency of your business. It’s not a novel concept, and several major companies across a variety of industries are already using predictive analytics and reaping incredible benefits.
No matter what industry you’re in, predictive analytics has the potential to fully transform your operations and optimize your business in ways you likely didn’t know were possible. It can also be a great tool for maintaining a competitive edge in the market, so the sooner you get started, the better.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with an agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair, Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.