Skip to main content
Esta página ainda não está disponível em português.

AI Predictive Analytics: The Key to Unlocking Business Insights

AI predictive analytics can help you make predictions about the future, ensuring better business decisions. Learn more about using AI for this purpose.

In today's digital and connected world, companies have more data than ever about their customers and business operations. Many companies may be familiar with using the information collected to make predictions about future data trends. However, generating effective and timely business insights has become more challenging with the large amounts of data available. Although predictive analytics may be a cornerstone of business operations, artificial intelligence (AI) is the key to success.

When it comes to incorporating AI into predictive analytics, understanding the role that computer algorithms and models play in analyzing a company's data and generating predictions can help that company drive future growth. Business insight is always about looking forward and being able to use lessons learned from the past. Incorporating AI into existing business practices can help a company look at the future in a whole new way.

What is AI predictive analytics?

The use of artificial intelligence for predictive analytics has become common in business intelligence. Big and small companies are taking advantage of advances in computer-based models, often AI models, to quickly analyze their data related to sales, customers, marketing, and more.

We live in a world where data has become available in several formats, ready to be used for various applications. While predictive analytics has been used for as long as businesses have collected data, the inclusion of AI into the process has allowed organizations to quickly and effectively use the vast quantities of information they have at their disposal.

Difference between artificial intelligence and predictive analytics

When it comes to understanding AI predictive analytics, asking the question, "what is predictive analytics?" is a good place to get started.

While many people don't have a good grasp of AI, being able to understand predictive analytics and how it applies to your business is important. So, what is a good predictive analytics definition?

In data science, predictive analytics looks at what comes next. In other words, it's about analyzing data from the past and evaluating what will happen in the future based on that data.

While predictive analytics was possible and used prior to the development of AI models, manual predictive analytics methods can take hours to generate a solution based on a couple of hundred data points.

AI is very effective at performing predictive analytics because it can collect, organize, and analyze data quickly. AI predictive analytics models will be able to generate a solution based on millions of data points in a matter of minutes. Of course, the capabilities of AI extend far beyond predictive analytics.

Key components of AI predictive analytics

When a company wants to use AI in marketing campaigns, it's important to have an idea of what AI is and what it's capable of.

With this understanding, the question "how does predictive analytics work?" can be answered, plus the capabilities of AI can be further explored to develop solutions unique to a business and its needs.

The components applicable to using AI for data analytics are the same as those used in other applications of AI, so comprehending how these pieces work together can help determine how to develop an AI-based solution.

In all uses of AI, the 3 key components are data, algorithms, and predictions.

Data

Data is what an AI model is trained on. In business applications, the data may come from customer or sales records or information gathered through a website. Whatever the source of a company's data, data tracking and understanding what's contained in a dataset is essential for determining how to use that information. When it comes to predictive models, whether AI is involved or not, a company will need to have access to historical data to make future predictions.

Algorithms

In general, an algorithm is a sequence of steps that are carried out to achieve a goal. Computers operate based on algorithms, and AI is certainly no exception. The development of AI algorithms has advanced considerably in recent years, and AI models include machine learning and deep learning networks. These algorithms carry out the data analysis necessary for making predictions.

Without considering AI, the predictive analytics algorithm would simply involve looking at the existing historical data and creating a prediction of the future. When AI is utilized, predictive analytics capabilities are greatly expanded. The data can be evaluated from different perspectives and in ways that an individual or team of people is incapable of doing. Various algorithms have been developed to carry out different tasks, and AI models can specialize in the interpretation and use of certain data.

Predictions

The outcome of any AI algorithm will be predictions based on the data. In predictive analytics, these predictions will be about the future. For a business, AI predictive analytics will be used to develop an idea of future sales trends or which marketing campaign will be most effective. Ultimately, the predictions can be compared with the actual results and fed back into the AI algorithm to improve it.

Applications of AI predictive analytics

Artificial intelligence has become prevalent in a number of fields. For organizations that regularly make use of predictive analytics, employing AI in the process can enhance results and improve the overall effectiveness of an organization.

Take a look at how AI predictive analytics is used in different industries below:

Healthcare

One of the biggest fields that takes advantage of AI and predictive analytics is healthcare. Doctors around the world have used predictive analytics to improve diagnoses and treat illnesses. With the inclusion of AI, these same doctors can evaluate much more data, significantly improving their diagnosing abilities and outcomes.

Finance

Organizations regularly use predictive analytics in financial analysis. From stock traders building models to predict future market changes to major corporations using their own historical transactions, the effectiveness of predictive analytics can't be ignored. With AI, more advanced evaluations based on larger data quantities can provide more accurate predictions about future finances.

Marketing and sales

An ideal place for a business to take advantage of predictive analytics is website analytics tools. By using data gathered from website users, a company can develop a marketing plan aimed directly at specific individuals.

With AI predictive analytics, different plans can be rapidly evaluated to determine which solution may be most effective for individual users. The specific marketing solution can also be implemented in real time and directed at users browsing a website.

Customer service

As companies look to enhance customer experience by providing personalized services, the use of AI and predictive analytics has provided new ways to use customer data. By applying AI to customer data that a company already has and new information gathered from new customers, a company can rapidly create a solution that will boost satisfaction.

Benefits of using AI predictive analytics

When it comes to AI predictive analytics, one of the most important reasons to use it is its ability to improve decision-making. By utilizing predictive analytics, a company can make informed decisions and anticipate that those decisions will have a better chance of a positive outcome.

Because a company can quickly and accurately evaluate vast amounts of information, AI can improve its overall efficiency. Rather than relying on a team to look at historical data, AI predictive analytics will reduce the burden on your employees and increase efficiency.

As a business, customers are everything. By developing unique solutions for individual customers using data gathered in real time, a company can improve the customer experience while delivering targeted campaigns that are more likely to be successful.

At the same time, delivering these specific solutions can give a business a competitive advantage over a different company that isn't using the same data to its full advantage through AI predictive analytics. Ultimately, AI and predictive analytics can help a company mitigate risk and improve the overall performance of its campaigns.

The future of AI predictive analytics

As more companies turn to advanced business intelligence solutions, the use of artificial intelligence in predictive analytics will continue to increase. Because predictive analytics is something that many businesses already include in various operations, the incorporation of AI into these processes won't require a significant deviation. Advances in AI have made the technology more widely available, and more businesses are looking to take advantage of the benefits that AI provides.

Enhance your business operations with AI-based predictive analytics

If your company is familiar with predictive analytics and uses it in daily business operations, you may be ready to take the next step and include AI. For companies that still need to take advantage of predictive analytics, using AI can be a good way to get started with this valuable tool.

Regardless of how your company uses predictive analytics, data science, or artificial intelligence, Mailchimp has individualized solutions to help enhance and drive your business success through an array of marketing tools.

Share This Article