What are the Benefits of Predictive Analytics Software and its Features?

As a business owner, you seek to attract more customers, increase sales and revenues, get higher ROI, and ensure customer satisfaction. But to do that, you need business forecasting or predicting. 

Forecasting is done in two ways: market surveys and analysis of available past and present data. Most businesses prefer the latter method because of its accuracy and reliability. Using this data, companies create predictive models to determine the future outcomes regarding sales, customer behavior, buying patterns, etc.,

But instead of building these models manually, it is much better to use an AI predictive analytics platform that helps you get actionable predictions in the shortest time possible. 

Predictive analysis benefits businesses in plenty of ways, including gaining a competitive advantage, searching for new revenue opportunities, knowing customer needs, and deploying effective strategies to gain a competitive advantage.

Analytics software helps you achieve these objectives and others. It uses historical data and extracts information datasets to guess future outcomes. Most of all, it provides an intuitive platform to add data, deploy predictive models, and track outputs. 

But what are some other benefits of this software, and what are some things to know about it? Read on to find out.

Benefits of Using Predictive Analytics Software 

Automatic Data Preparation

Manual data preparation for predictive models can take a lot of time, delaying your project by weeks or even months. In contrast, using software automates the data structuring process, encoding, cleansing, feature selection, imputation, and engineering. 

Plug-in your data sources, letting the software do the rest. It’s faster, more efficient, and provides accurate results.

Monitors and Optimizes the Models

AI algorithms optimize your predictive models, ensuring you receive effective results in the shortest time possible. However, drift and leakage are two problems people face with these models. 

Drift, in predictive models, refers to a situation in which the future data ends up being similar to the past data. Leakage refers to a situation where a data set has the relevant data but is not available for use when required, that is, for model predictions. 

Using software helps you avoid both of these conditions. It continuously monitors and optimizes your models and enhances their functionality with relevant external data. 

Actionable Dashboards

The platform enables you to remain focused on your business KPIs, regardless of the model you use for them, as it is organized into models and projects. When it comes to taking action or viewing the results, the software instantly connects to the data source.   

Also Read: Understanding Sales Trend Analysis

What are Some Features to look out for?

These are some features that you should look for in an analytics platform.

Data set maker

The platform should let you connect with various databases through code-free integration and the absence of database manipulation. After you connect to the database, it should allow you to use a feature like drag and drop interface to create AI-ready data sets. 

Data Enrichment

It should help you supercharge your models through data enrichment, a process that collects relevant global data for your analysis for faster results.

Autoprep

The platform should prioritize features as they change and save time on the predictive process through features like automating feature engineering, encoding, and feature selection. 

AutoML

The software should construct deep neural networks by determining your data’s nature, size, and required predictions. After assessing various networks, it selects one trained neural network for maximum accuracy. 

Monitoring

It should continuously monitor your data for accurate predictions using AI-powered optimization. It must have a feature like a live dashboard that helps you monitor performance and take relevant action. 
You should consider using AI predictive analytics software because it efficiently uses data and continuously monitors the predictive models to provide accurate results. It speeds up the data preparation and helps you stay focused on your business KPIs.




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