What Is a Predictive Analytics AI-Powered SaaS Platform &?
This article was published as a part of the Data Science Blogathon.
Predictive analytics powered by AI technology is changing the world as we know it. Nowadays, there is an ocean of data available for every industry. However, finding the gems that answer your business questions is as hard as navigating a real ocean on a raft. The majority of tools available now are either lacking in capacity or speed to process vast swarms of information, let alone draw insights from them. This is where predictive analytics AI-powered platforms are coming into play. These solutions have the potential to make valuable insights obtained from big data accessible to any business. Even companies with limited budgets and time-sensitive campaigns can draw great value from such SaaS tools.
SaaS platforms, in general, are diverse and getting more popular with every passing day. It’s a well-known fact that SaaS companies often have the best customer success teams. And developers understand that increasing customer success will grow their own revenue. SaaS platforms for predictive analytics have the potential to take your business’ success further.
Predictive Analytics AI-Powered SaaS Platform: The Basics
In essence, predictive analytics technologies are divination machines with tremendous processing power. But instead of divining answers to your questions, as street charlatans do, these platforms use machine learning technology to analyze enormous blocks of raw data. During this, they are using complex algorithms to identify patterns within the data. Finally, they use even more complex processes to analyze historical development patterns, combine them with current data, and predict possible outcomes based on these parameters.
A predictive analytics SaaS platform can perform three main types of data analytics:
At this stage, AI analyzes historical data to determine patterns that led to specific events in the past. This process stage mainly consists of data mining, aggregation, processing, and developing summary statistics.
AI will take a step further in its analysis to identify reasons that caused certain events. At this stage, the tech can identify specific patterns and issues that you might want to investigate more in-depth.
The primary purpose of a predictive analytics SaaS platform is to take all the information obtained during two previous steps to estimate and forecast future trends. It’s at this stage that the power of machine learning is used in full.
A truly advanced AI-powered SaaS platform can go one step further, all the way to prescriptive analytics. It means that the AI will use the data to provide you with actionable insights. Basically, this is advice on what to do and how to use the information most beneficially for your business.
In order to provide insights, predictive analytics SaaS platforms use multiple complex technologies and tools, including:
● Quantitative analysis.
Quantitative analysis is a technique that relies on mathematical and statistical modeling to identify patterns of behavior. It runs on numbers and can be applied in any field that can be quantified.
● Machine learning.
It’s the basis of all advanced AI technology. Machine learning enables machines to learn from the patterns they process, thus improving their abilities to ‘think’.
● Neural networks.
Neural networks, in the technological sense, are systems that emulate human neural networks. They are used for solving complex AI problems. The system is organized similarly to human neural networks, hence the name. It uses specialized algorithms to facilitate machine learning.
● Predictive modeling.
Predictive modeling uses statistics of past events to predict the future. Note that this type of modeling can be applied to any situation. In fact, its efficiency can be verified when trying to ‘predict’ past events.
● Simulation analysis.
Simulation analysis is the process of creating a computer model of the process and analyzing its potential outcomes.
SaaS platforms are powered by AI because the human brain is simply incapable of processing such a massive volume of data. Unfortunately, the same goes for standard research tools used today. They can do the analysis, but they either have limited databases or not enough capability to unlock valuable insights from the data.
Data analytics agencies have the power to do it. However, hiring them for market research will cost a business upwards of $100,000.
AI-powered SaaS platforms are much more affordable. Moreover, machine learning technology improves with every search. Therefore, there is no match for AI-Powered predictive analytics platforms in terms of providing actionable insights based on Big Data.
AI-Powered Predictive Analytics Platforms Are the Future of Tech
AI-powered tech is making huge waves right now and with good reason. The majority of people might not even notice it, but AI is present in nearly all tech-related aspects of our lives. This presence is sure to grow further as, according to McKinsey research, even at its current level of development, AI can resolve over 400 longstanding business challenges.
SaaS predictive analytics platforms enable SMEs and entrepreneurs to use the processing power of AI. This service is vitally important in the competitive market of today. There can be no doubt that technology will develop further in response to the demand.
However, one needs to remember that these platforms have to integrate with existing tech. There are also legal considerations to keep in mind. All SaaS platforms must have a well-thought-out agreement to protect both the developer and customers from litigation. And this situation will gain new layers of complexity as AI technology becomes more prevalent in the world.
How to Use Insights Delivered by Predictive Analytics SaaS Platforms?
The main benefit of using predictive analytics SaaS systems for business is that they can deliver results that do not merely show you what’s happening now. They also show the user what to do about the situation. Therefore, a predictive analytics AI-powered platform develops insights in real-time that can be implemented to the greatest impact to gain a competitive advantage.
Some of the ways one can apply predictive analytics insights in business situations include:
● Designing personalized marketing campaigns that target a wide range of audiences.
● Developing product improvements that meet predicted changes in customer needs, thus getting ahead of the times.
● Analyzing the competition to develop a killer USP (unique selling proposition).
● Predicting business outcomes in shifting global economic circumstances.
● Identifying areas for improvement within the business itself and automating a variety of processes.
● Using available resources in the most efficient manner.
AI technology is transforming every industry from eCommerce to healthcare. Already it’s hard to imagine how a business can succeed without using the power of Big Data. As it’s impossible to derive quality insights from it relying on human power, AI-powered SaaS platforms will keep growing more popular. With machine learning tech literally evolving with every use, we are sure to see breakthroughs in the immediate future. Developers who want to stay on top should take this into account and come up with ideas of how to integrate predictive analytics AI into their products.
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