How to Use Predictive Modeling to Lower Business Expenses

Predictive Analytics is not just an effective tool for boosting your ROI. It can just as effectively pull your company out of a hole. Since there is a dollar amount attached to every operational task, cutting costs without losing business leads to a healthier bottom line.

Predictive Modeling provides information and insights that can help to lower business expenses in multiple ways, such as:  

  • Effectively balancing work across departments;
  • Optimally distributing available skills
  • Reducing marketing expenditures through more accurate targeting
  • Forecasting market trends; and
  • Efficiently managing inventory.

And that’s just a handful of the diverse array of benefits.

Here is a closer look into a few predictive models that can turn your organization into a lean, mean, cost-cutting machine.

Using Predictive Modeling to Create Heat Maps for Data Visualization

Humans are visual creatures and our brains are hard-wired to understand visual data. Heat maps are visual guides that tap into human psychology by highlighting various data values using different color intensities. Darker colors or shades indicate action and density, while lighter colors are indicators of less action or low density.


Heat maps can be used to show density and clustering in geographical areas, allowing for better business resource management. For example, they are often used to evaluate and improve marketing campaigns in areas that are showing low responses and low sales. They can also be used to tailor campaigns toward regions with a high population of their target demographic.

Heat maps can also help businesses optimize product inventories based upon regions showing higher sales. Other insights can indicate new potential markets or elevated sales pipelines in areas which once were active but are now showing lighter coloring (indicative of lower sales volume). They have also been used to change or add distribution centers and stores when they were mapped against customer-dense regions. Heat mapping use cases are endless.

In addition to using heat maps for geographic data, these visuals can also be used for website traffic data. The color gradients and “hot spots” can highlight locations where viewers focus their attention while on-page, in addition to indicating where users click and whether they scroll to the bottom of the page. This can help website designers to optimize page length, overall layout and positioning for buttons and CTAs.

Heat maps can distill massive volumes of data, creating visuals that are easy to understand. By comparing sales territories with those of their competitors, businesses can strategize new competitive campaigns, while avoiding market saturation. They can also leverage these insights to ensure that their website layout directs attention to the most important features in a user-friendly manner.

Predictive Modeling Uses: Neural Networks for Improved Forecasting

Artificial neural networks mimic human brain neurons with intelligence from AI and Machine Learning. A brain neuron receives input and fires off an output that is transmitted by other neurons and results in a specific action. Similarly, an artificial neural network detects patterns in data and uses it to understand a particular process and forecast outcomes. Machine Learning capabilities allow a neural network to learn and adapt as it continually receives new data. The result: increasingly accurate forecasts and predictions.


Neural learning is finding application in every sector. In retail, this technology can offer a very accurate picture of what products are most often purchased. With this insight, companies can increase sales by encouraging or recommending additional products that are often purchased together. Improved fraud detection is one of the ways that insurance, banking and e-commerce sectors are using predictive modeling to lower business expenses and find big savings. Banks can also utilize neural networks to approve loans. DialogTech uses neural networks to classify and analyze inbound customer calls. It then uses the information it receives to help companies deliver a better customer experience, which in turn, increases sales volume.

Creating Decision Trees for Making Business Decisions

Decision trees are one of the most transparent predictive models, as they lay out all choices, risks, monetary gains and information needed in an “ If… then…else” model. It is a management favorite since this imagery can be used to depict a statistical probability and offer other insights that are critical for choosing the right course of action. In a single view, the branching method can illustrate the possible outcomes of a particular decision. Another benefit of this model is that statistical knowledge is not required to understand the decision tree. Each node of the tree represents a test variable and each branch represents the outcome. This could be why this visualization tool is a management favorite, since they don’t need to spend hours examining dry, hard-to-understand data.


As the name implies, decision trees have practical applications in virtually any decision-making scenario. They are used in business development as well as project management. Decision trees are especially popular in the business and financial sectors. The best use cases can even involve complicated choices such as handling mergers or expanding operations on a global scale. Decision trees can ensure that investments are thoroughly considered prior to implementation. However, it is important not to rely on decision trees blindly. As with any predictive model, they should be used as aids for making more informed business decisions.

Predictive Modeling to Generate Linear Regression Models for Testing Relationships Between Variables

There are many types of analysis regression models, but all of them are based upon how one or more independent variables can influence a dependent variable. Linear regression models were named for the line that is created when two variables are plotted against each other. A correlation coefficient can be calculated to determine the strength between the two variables.


This model is particularly useful when a hypothesis needs to be tested. Let’s say a regional sales manager is meeting with his team to discuss the next quarter’s sales numbers. A number of assumptions are thrown into the mix and these variables can affect the bottom line. Independent variables such as inventory volume and a competitor’s product launch take up central positions among other variables. This calls for a regression analysis using historical sales data, sales data for the corresponding period and competitor market share data. When the first variable is plotted on the “y” axis, surprising insights can come to light from the “x” axis. Drawing a line through the data can reveal the relationship between the two variables. This can then be used to create a forecast. Linear regression models can also be used to inform staffing decisions under many different conditions.

With predictive modeling, companies can save a significant amount of money thanks to their newfound ability to lower business expenses and increase revenue.  By using heat maps, neural networks, decision trees and linear regression models, businesses can allocate their resources effectively, in addition to improving forecasting, optimizing decision-making and testing variable relationships. Current technology and new approaches are allowing businesses to break free from traditional ways of operating. Data-driven decision-making capabilities are helping businesses to become more innovative, efficient and competitive.

As a software as a service provider for data lake creation, data visualization and predictive analytics, Sertics puts the power of data-driven insights into the hands of business users without the need for data scientists. Contact the team at Sertics today to find out what Sertics can do for your business.

Venkatesh Kalluru has more than two decades of experience spearheading agile development projects for firms including ReThink IT, GCE, and AT&T. Venkatesh has expertise in a diverse range of technologies including Predictive Analytics, Machine Learning, AI, IoT and more. Venkatesh studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India, and earned his master's degree in computer science from George Mason University in Virginia.