Predictive Modeling with Web Data: Anticipating Investment Opportunities and Risks

Predictive modeling plays a critical role in the modern-day digital business landscape. As a subset of AI, it helps business organizations tap into the power of predictive modeling techniques, predictive analytics, and current and historical data to predict future outcomes, identify investment opportunities, and anticipate risks. 

Thanks to the power of AI and machine learning predictive modeling, companies can build predictive analytics models to forecast future events and trends and boost business growth. Data analytics provide accurate and up-to-date insights into your competition, target audience, and business operations. 

Feeding your forecast the right data points can help your organization improve every aspect of business-related operations. Today, we’ll delve deeper into predictive modeling to explain what it is and how it can benefit your business. In addition, we’ll mention the best places to look for sample data for your predictive modeling algorithms.

What is predictive modeling?

Predictive modeling uses complex mathematical processes to forecast future outcomes and market events by recognizing patterns in your data collection. Predictive modeling tools use given input data to fuel different predictive analytics algorithms to identify patterns and gather insights that you can use to predict future outcomes. 

For example, you can forecast the outcome of your future marketing campaigns or use popular predictive analytics models to empower your data scientists to develop a unique statistical technique based on historical data. 

Predictive techniques rely on input data and common predictive algorithms to forecast trends, behavior, and activity across markets and industries. Aside from predicting the future, predictive analytics tools can also forecast outcomes. 

That’s why the benefits of predictive modeling are many and include enhanced fraud detection, improved customer relationship management, and turning unstructured data into actionable insights. 

Common predictive algorithms are vital elements of predictive analytics that rely on big data (current and past data) to predict an array of business-related applications, such as market trends, consumer preferences, and competitor activity. Predictive modeling helps you forecast likely outcomes based on existing data gathered from current and past events. 

You can use all available types of data as training data for your predictive model-building efforts, such as:

• New data on economics;
• Public web data;
• Relevant data on your target audience;
• Customer service data;
• Advertising data;
• CRM data;
• Transactional data.

You can rely on predictive machine learning models to analyze historical data and tackle challenges such as overcoming technical and organizational barriers. Predictive modeling is vital to gaining a competitive advantage, increasing profits, and improving decision-making.

Benefits of predictive modeling

Using predictive modeling and analytics in your organization can benefit your business in various ways. For example, predictive models excel at detecting fraud. You can identify and mitigate fraud attempts before they escalate beyond repair. 

Thanks to the immense power of predictive modeling, you can shield your operations and data from cyber attacks to take your cybersecurity measures to the next level. Many business organizations rely on predictive models and analytics to detect vulnerabilities and anomalies in cybersecurity systems. 

On the other hand, predictive modeling is the perfect solution for mitigating collective risk. The truth is that predictive modeling can benefit every aspect of your business. For example, you can get an accurate and up-to-date demographic picture of your target audience, including buying habits, spending preferences, interests, etc. 

Whether your business operates in the finance, healthcare, insurance, or IT sector, predictive modeling can help you optimize your marketing strategy and enhance your campaigns with accurate customer data to forecast future marketing decisions. 

Other benefits of predictive modeling include:

• Gaining actionable insights on your competitors;
• Reorganizing marketing strategies to reach wider audiences;
• Optimizing existing products and services for wider markets;
• Understanding your customers and their needs;
• Understanding industry and market events and trends;
• Reducing cost, effort, and time of predicting outcomes;
• Detecting cybersecurity threats and identifying financial risks;
• Forecasting external factors that impact operational workflows and productivity;
• Streamlining resource and inventory management processes;
• Identifying future trends;
• Churning analyses;
• Planning workforce.

Types of predictive models

All types of predictive models fall into two categories: parametric and non-parametric. Parametric models base their predictive power on specific sets of parameters, such as discrete numbers. 

On the other hand, non-parametric predictive models require data from sources that don’t operate with specific factors or sets of parameters. 

With that in mind, let’s briefly explain common predictive modeling types.

Gradient-boosted model

This predictive model generates rankings based on several related decision trees by building one tree at a time. It fixes errors from the first tree to build upon and create another enhanced one. 

A gradient-boosted model enables you to build several decision trees depending on your needs. This model is perfect for predicting potential search engine results.

Generalized linear model

This model is a powerful statistical tool that turns variable data into actionable insights by analyzing how variables interact with each other. Many data experts see this model as an integral part of statistical processes, such as logistic regression analysis. 

Put simply, a general linear model helps your organization make a prediction based on all data in your pipeline by comparing all attributes to find discrepancies and ascertain the accuracy of the prediction made.

Neural networks

A neural network is a complex predictive modeling technique that empowers decision-making processes. It uses algorithm collaboration to recognize patterns, identify data clusters, and categorize them into variable datasets. 

Each neural network has three layers:

• The input layer – transports data to the next layer;
• The hidden layer – this is where complex functions generate predictors;
• The output layer – gathers data from predictors and provides the user with a comprehensive outcome.

Neural networks are perfect tools for clustering data without data loss or leaks.

Decision tree

A decision tree is a machine-learning algorithm that gathers multiple data sources to graph them into a structure that resembles a tree. A decision graph analyzes different decisions and branches them into possible outcomes to match each decision with potential outcomes. 

Decision trees are ideal for turning organizational data into actionable insights to improve collaboration and ensure every department in your company is on the same page.

Outliers model

An outlier model analyzes datasets to identify outlying or abnormal information within them. That can be useful when ensuring your datasets are accurate and up-to-date across your pipeline. 

Outliers' predictive models are perfect for detecting fraud and preventing unauthorized access during financial transactions.

Clustering model

Clustering models are superb predictive models for categorizing data. They help you separate data sets and group them based on similar attributes. 

A clustering model will take data from each group and cluster it on a large scale to help you assess potential outcomes for each cluster. 

This type of predictive model is ideal for custom-tailoring marketing campaigns according to specific demographics.

Forecast model

A forecast model generates numerical outcomes by processing historical data and assessing information based on the given data. This model is ideal for e-commerce websites and online retailers to ascertain customer demand within a set window of time.

Classification model

Classification models are commonly used in finance and retail industries to collect and categorize historical data to answer questions that require simple yes/no responses. 

A classification model allows for a high level of customization as you can custom-tailor and modify it according to your specific needs.

Predictive modeling use cases

Because predictive modeling can provide accurate and actionable insights relating to your business, market, industry, competitors, and customers, it can help you improve your business in many fields. Let’s briefly touch upon common real-life business applications.

Enhanced audience targeting

The success of your marketing strategy depends on matching your messaging with the customer’s needs. In other words, you must target the right audience with your marketing campaigns to drive the desired results. 

Predictive modeling allows you to reach your target audience by helping you understand which consumers are likely to buy your products and services.

Streamlined buyer journey insights

The buyer journey is an incredibly complex territory prone to constant changes and fluctuations. Navigating such a territory is virtually impossible without a chart. 

Predictive modeling acts as a compass that leads your customers further down the sales funnel by offering fragmented experiences to each consumer. 

It gives you real-time customer insights to help tailor the buyer journey and customer experience according to consumer expectations.

Accurate investment risk assessment

Predictive modeling excels when it comes to predicting investment-associated risks. Aside from harnessing the power of past, current, and future data to help you discover investment opportunities across new markets, predictive models improve your investment efforts by empowering you to recognize lucrative endeavors more efficiently. 

You can rely on predictive modeling for forecasting investments in particular markets based on historical data to determine how many opportunities can yield acceptable ROI. Assessing and avoiding investment risks is crucial for driving business growth. Thankfully, you can quickly evaluate emerging risks to put adequate mitigation measures to prevent them.

Data acquisition for predictive modeling

You can consider free and premium data sources for predictive modeling. Top free sources for data acquisition for predictive modeling include:

• Datasets for Regression and Classification;
• Datasets for Text Mining;
• Kaggle Competition;
• UCI Machine Learning Repository;
• Amazon Public Datasets;
• Microsoft Research Repository;
• Yahoo Datasets;
• AppliedPredictiveModeling R Package;
• Machine Learning Data Set Repository.

Though these sources offer free data for predictive modeling, no one can guarantee you’ll find what you’re looking for with any of these solutions. If you don’t want to leave anything to chance, consider premium data acquisition sources like Coresignal. 

Data providers like Coresignal offer you 24/7 access to a reliable, accurate, and up-to-date stream of public web data for your business needs.

How to use web data for predictive modeling?

Modern consumers are creatures of habit. The reasons that made them buy your products in the past will be the same reasons they will continue to be loyal to your brand. So, you can learn from previous interactions with your brand and leverage that knowledge to enhance your approach toward meeting their future demands. 

The key to accomplishing this goal is in gathering as much web data on your customers as possible. Essentially, predictive modeling with web data requires you to complete three vital steps. First, you perform thorough data analysis to determine what worked for your customers in the past and recognize customer patterns. 

Then you use that knowledge to ascertain new patterns your customers will likely follow. Next, you fuel a predictive model with future customer insights to determine which marketing approach worked the best in the past by analyzing different segments of your customer base. 

That should tell you which marketing strategy gives the best results for each segment. Finally, you use web data to ascertain which products or services had the most success during each campaign, including the ones that did or did not successfully increase your sales. 

Then, you apply those insights to your current marketing campaigns to predict whether those same products or services have a reasonable probability of success if marketed. 

Conclusion

Predictive modeling is an excellent way to predict your next business in an overwhelmingly competitive business landscape. That’s why it supports an array of business applications. Though it requires a steep learning curve to get into it, predictive modeling can improve your decision-making, help you avoid risks, and make your business more profitable. 

However, accomplishing such goals requires you to select a suitable predictive model for your business niche and acquire the right data sets for training your model to seamlessly integrate with your ongoing systems and processes. 

Thankfully, the web gives you access to a fine selection of free and premium data sources for your consideration.