Predictive Data Analytics
With the increase in the availability of technical resources, organizations are now able to obtain enormous amounts of data. But on its own, greater volume doesn’t always mean greater value. The competitive advantage lies in recognizing hidden patterns in the data to inform action.
We build applications with algorithms capable of identifying the hidden trends and patterns in your data, from which possible futuristic forecast & outcomes can then predicted. This knowledge enables organizations to formulate strategies & solutions that can directly improve business decisions & results. Below are some of the models we utilize…
Features
Regression Models
Regression models in our applications can be used to determine the character of the relationships between given dependent variables against other variables. This knowledge allows organizations to be aware whether changes observed in the dependent variables can be associated with changes in one or more of the explanatory variables.
An example use case is predicting employee performance based on various factors such as training hours, tenure, and other relevant metrics. This can aid in making decisions related to promotions, compensation, and training programs.
Clustering Models
These models are useful for organizations intending to understand given features of their processes through clustering or segmentation. A common example is the need to segment a business' customer base into different clusters to highlight those that have the highest probability of continuing to do business with the company against, for example, those that are like to churn or stop transacting with the business.
We can build applications with such clustering models capable of breaking down available datasets into meaningful groupings or collections, thereby enabling businesses to estimate & act upon possible behavioral patterns and/or outcomes.
Classification Models
Classification models take data input and assign labels to it. These labels can then be used to make discrete predictions whether the input points or factors fall under one classification or another. Product/service review classification is one use case example for classification models. This is an essential process to any organization as it allows understanding customer insights and improving their experience.
However, analyzing & matching different product/service reviews or any other process requiring classification of input to specific features can take hours. We can build applications that can automate this type of tasks and save businesses time and resources in the process.
Time Series Models
Time Series models work on sets of data points ordered in time, where time is the independent variable. Organizations can employ these models to recognize patterns and trends within data that exhibits variations over time, thereby enabling the generation of forecasts for future values.
Use case examples of Time Series analytics include forecasting future sales performance based on your previous time periods sales data. This can help an organization plan such factors as inventory, staffing, budget, and promotions throughout the year. We build applications that can provide such models and help businesses strategize effectively.
Automate Your Data Handling Capability With Powerful & Simple-To-Use Technologies
Order your demo today to see the complete list of all our Predictive Analytics tools and models...
Automate Your Data Handling Capability With Powerful & Simple-To-Use Technologies
Order your demo today to see the complete list of all our Predictive Analytics tools and models...
