Using Predictive Analytics To Improve Forecast Accuracy
Special Guest blog by Mike Dion from F9Finance
Are you looking for ways to improve your business forecasting?
Predictive analytics is a powerful tool to help businesses make more accurate forecasts. By using data-driven insights, predictive analytics provides valuable information about future trends and patterns in the market. This helps companies better understand their customers’ needs and plan ahead for potential changes in demand.
With predictive analytics, businesses can stay one step ahead of the competition by accurately predicting customer behavior and responding quickly to changes in the market. It also enables them to identify opportunities before they become apparent, allowing them to capitalize on new trends early on.
Learn more about how predictive analytics can help you improve your forecast accuracy today!
What Are Predictive Analytics?
Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to predict future outcomes. Predictive analytics is rapidly growing due to the expansion of digital finance. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen.
How Predictive Analytics Works:
Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a valuable assessment of what will happen next.
The process typically involves the following steps:
Define Project: Define the project outcomes, deliverables, and business objectives, and identify the data sets used.
Data Collection: Data from various sources is collected.
Data Analysis: The collected data is analyzed to identify patterns and trends.
Statistics: Statistical models are created, tested, and validated.
Deployment: The model is deployed to make predictions.
Model Monitoring: Monitor the model performances to ensure it provides the expected results.
Benefits and Risks of Predictive Analytics:
The benefits of predictive analytics include improved decision-making, increased operational efficiency, risk reduction, and the ability to forecast trends.
However, predictive analytics also comes with risks. Predictions are only as good as the data they're based on - inaccurate data can lead to incorrect predictions. There's also the ethical consideration of how data is used, especially regarding sensitive information. Organizations must use data responsibly, respecting privacy laws and regulations.
Why Is Forecast Accuracy Important?
Forecast accuracy is pivotal in corporate finance, affecting everything from short-term operational decisions to long-term strategic planning. Accurate financial forecasting is the cornerstone of sound financial management, and even slight variations can significantly impact a company's financial health and sustainability.
Critical Financial Decisions
Companies make critical financial decisions based on financial forecasts. These may include decisions regarding capital investments, budget allocations, cost management, pricing strategies, etc. Accurate forecasts enable businesses to allocate resources efficiently, maximize profitability, and maintain financial stability.
Impact on Returns on Investment and Future Projections
Forecast accuracy directly affects returns on investment (ROI). Overestimating revenues or underestimating costs can lead to poor investment decisions and lower returns. Similarly, inaccurate forecasts can skew future financial projections, leading to misguided strategies and missed opportunities.
Mitigating Financial Risk
Accurate financial forecasting is crucial for mitigating financial risk. It helps companies anticipate market trends, identify potential financial risks, and formulate effective risk management strategies. By accurately predicting cash flows, companies can ensure sufficient liquidity, maintain optimal capitalization, and prevent insolvency.
Promoting Long-Term Growth
Accurate forecasting supports sustainable long-term growth. It provides the data-driven insights needed to make strategic decisions about expanding operations, entering new markets, launching new products, or pursuing mergers and acquisitions.
Improving Forecasts with Advanced Technology
Advanced technologies like machine learning are revolutionizing financial forecasting. They enhance accuracy by analyzing large volumes of data, identifying patterns, and adapting to changing conditions. These technologies also reduce human error and bias, further improving forecast accuracy.
Forecast accuracy is not just desirable in corporate finance - it's essential. It underpins effective decision-making, risk management, and long-term strategic planning. And with the advent of advanced technologies, achieving high forecast accuracy is more feasible than ever.
How To Start Using Predictive Analytics
I suggest starting with challenger models as an easy entry point to the expansive and evolving field of predictive analytics.
Challenger Models
The purpose of the Challenger Model is to test and validate the performance of the Champion Model, which is the current model in use. The two models are run in parallel on the same data sets to compare their performance. This approach allows organizations to continually evaluate and enhance their risk models, ensuring they remain effective and relevant.
For example, the Champion Model might be the current statistical model used to predict the probability of default on a loan in credit risk modeling. A Challenger Model would be an alternative model designed to make the same predictions using different assumptions, variables, or techniques.
If the Challenger Model consistently outperforms the Champion Model, it may replace it or lead to adjustments in the current model. This process promotes continuous improvement and innovation in risk modeling.
Structuring the Model and Choosing Variables
The structure of a Challenger Model depends on the problem you're trying to solve. For instance, if you're predicting customer churn, your input variables include past purchase behavior, customer complaints, payment history, etc.
Choosing the right variables is crucial. These should be relevant to your prediction goal and should have a significant impact on the outcome. You should run correlation analyses or feature importance tests to identify the most impactful variables.
Off The Shelf Software Versus Coding
There are several off-the-shelf software options available for predictive analytics. Each comes with its own set of features, capabilities, and pricing models. Here are a few popular ones:
IBM SPSS Modeler: IBM's SPSS Modeler is a leading global predictive analytics platform. It provides predictive models and machine learning capabilities to build accurate predictive models quickly and intuitively without programming.
RapidMiner: RapidMiner offers a platform for data preparation, machine learning, deep learning, text mining, and predictive model deployment.
SAS Advanced Analytics: SAS is a business analytics software and services leader. Their Advanced Analytics software provides a range of techniques and processes for collecting, classifying, analyzing, and interpreting data.
Microsoft Azure Machine Learning Studio: This is a collaborative, drag-and-drop tool to build, test, and deploy predictive analytics solutions on your data.
Alteryx Analytics: Alteryx offers an end-to-end analytics platform for data analysts and scientists. It includes predictive analytics as part of a suite that includes data prep, location intelligence, and data science tools.
KNIME: KNIME Analytics Platform is open-source software for creating data science. It integrates various components for machine learning and data mining.
Alternatively, you can code your model using a Python language. If you know or are willing to learn Python, the internet is full of open-source algorithms and libraries you can build into your model.
Selecting Algorithms
There are several predictive analytics algorithms you could use, including:
Regression Algorithms: These are suitable for predicting numerical outcomes like sales revenue.
Classification Algorithms: These are used when the outcome is a category, such as 'will buy' or 'won't buy'.
Clustering Algorithms: These are used to group similar data points together and can be useful in customer segmentation.
Each algorithm has pros and cons, so choose based on your specific use case, the nature of your data, and the resources available.
Case Study: Predicting Customer Churn
Suppose a telecom company wants to predict customer churn. Their Champion Model uses logistic regression and variables like customer tenure, monthly charges, and type of contract. A Challenger Model could use a random forest algorithm to capture complex, nonlinear relationships and additional variables like total charges and customer complaints.
Testing and Validating the Model
Split your data into training and test sets to test and validate your Challenger Model. Train your model on the training set, then test it on the test set. Use metrics appropriate for your problem, such as accuracy, precision, recall, or AUC-ROC for classification problems and RMSE or R-squared for regression problems.
Maintaining and Updating the Model
Models should be regularly updated as new data becomes available. Monitor your model's performance over time and retrain it if its performance declines or if it no longer reflects current trends.
Frequently Asked Questions
What Are The Three Pillars Of Predictive Analytics?
The three pillars of predictive analytics are data, modeling, and evaluation. Data is used to build the model, while modeling is used to make predictions. Evaluation is done to determine the accuracy of the model.
What Is The Main Objective Of Predictive Analytics?
The main objective of predictive analytics is to predict the future as accurately as possible. It uses data and algorithms to build models that can be used for forecasting, identifying trends, and making decisions.
What Is The Difference Between Machine Learning And Predictive Analytics?
The difference between machine learning and predictive analytics is that machine learning focuses on building models that learn from data, while predictive analytics focuses on making predictions. Machine learning models are typically used to generate insights and identify patterns in data, while predictive analytics models are used to predict future events.
What Types Of Data Are Used For Predictive Analytics?
Predictive analytics use various data types, such as demographic information, past customer transactions, online activities, social media posts, and more. The type of data used depends on the predictive analytics task at hand.
What Are Examples Of Predictive Analytics?
Examples of predictive analytics include: forecasting demand, predicting customer churn, detecting fraud, identifying the best customers to target for marketing campaigns, and more. These models can help organizations make better decisions and increase their profitability.
Quick Recap
Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine-learning techniques to predict future outcomes.
Accurate financial forecasts are essential for mitigating risk, promoting long-term growth, and making informed decisions.
Advanced technologies like machine learning revolutionize financial forecasting by improving accuracy, reducing human error, and providing data-driven insights.
Start with challenger models as an easy entry point to the expansive and evolving field of predictive analytics.
The purpose of the Challenger Model is to test and validate the performance of the Champion Model, which is the current model in use.