Understand Your Customers Like Never Before with AI Data Analysis and Intelligent Queries

Would you want to be able to predict your customers' needs before they even know them? It might have been only a distant thought at the back of your mind. However, it can quickly turn into reality. 


There is so much that AI can do now in so many areas of our business. This includes data analysis as well. For those who have been following the trends these days, it won’t be surprising that data is considered to be one of the highest-value assets of any online business this year. 


We accumulate a vast amount of data from our customers, but merely having that data in your system won’t go a long way in improving our business in any way. We have to do something with it; we have to take advantage of the fact that we have so much knowledge on our hands. We simply have to analyze it. And why do I say “simply” when it’s such a complex process? Because we have artificial intelligence at our disposal that’ll help us create a short and effective process out of this. 


To understand how AI can help us analyze data for our business, we’ll need to get familiar with such aspects as Intelligent Queries, events, milestones, and reports. This article will help you learn exactly what these are and how you can take advantage of them. 


AI Intelligent Queries for Data Analysis


What Are Intelligent Queries and How Should We Use Them 


Before we start discussing intelligent queries, it’s crucial to grasp what they are based on: the events, milestones, and specific reports on these. Let’s see what they mean exactly. 


Events are every interaction that your customers engage in while using your product or service. To simplify, events are every movement your customers make when they are on your website. These can include site visits, registrations, logins, purchases, and specific feature usage. 


Now, based on the events, you can also determine specific milestones. Milestones are the points in your business's usage where customers can get value, where they can be successful, or where they can receive something that will benefit them. What is important to note here is that these milestones must be measurable and monitorable, and reaching them will represent a meaningful success for your customers. 


These milestones will then be the foundation for the reports we want to extract from the data we have in our hands. This is where intelligent queries will help us. Imagine intelligent queries as super smart questions that you think about daily when you wonder what your customers are really doing and wishing to have. What are they using most frequently? What is the most significant feature that drives them to make a purchase? Where do they come from? If you have such questions in mind, I have great news for you: Artificial Intelligence in data analytics is specifically designed to answer all of your questions in just seconds by turning human text into understandable business reports. How? Let’s see. 



What Can You Get When Analyzing Data with AI


Intelligent queries can be used in many areas to analyze data. I’ll cover some of the most important domains where they can be useful: marketing attribution, engagement rates, churn rates, and fraud detection. 


Marketing attribution is all about knowing the channels and marketing campaigns that your potential customers come from. Everyone wants to know which are the best strategies for reaching the largest number of people. Marketing attribution gives you just that. Knowing which channels perform the best will help you improve your marketing strategies and campaigns. 


Next is engagement rates. Once you have your customers, it would be best to see what they enjoy the most, what features they consider to be the most helpful, and what products they like the most. An intelligent question you can ask here is: What are the pages that customers visit most frequently and spend the most time on? Once you know that, you can guide all your customers to the best places in your business. 


  • For example, Netflix is very well known for its use of machine learning and AI to recommend the right movies and series based on our previous watches. The company reports that it has saved them $1 billion per year. 
  • We can also take another example from Bank of America. They use a Predictive Intelligence Analytics Machine to understand the relationship between ECM deals and investors. 

Then, we have the churn rates. Customer retention can be just as tricky as acquisition, but with the right information at hand, it can also be simplified. The best strategy to follow here is to identify where you lose customers. What are the points where customers’ engagement rates drop significantly? The answers will tell you where you must improve your features or your communication. 


Finally, let’s talk about fraud prevention. As the data you analyze is real-time information, it can be used for fraud detection as well. As AI can be used to detect unusual patterns and anomalies in large amounts of data, it is something we cannot overlook. For example, Spotify has been using AI to prevent fake music streaming activities that would otherwise enhance publicity and popularity. 





In conclusion, let’s just say we have something that we never had before. Something that helps us understand and get to know our customers on a level that is so deep yet so effective and easy that anyone can do it. All with the help of AI and intelligent queries. 


In the article, we’ve mentioned some purposes of data analytics that are crucial for an online business, such as marketing attribution or churn rate determination, but the possibilities are only limited to your creativity and curiosity. 


Ask anything, and AI will tell you exactly what you need in the form of reports. What would you ask about your customers? 

Csilla Fehér
Csilla Fehér
Public Relations and SaaS Enthusiast | PR Coordinator at SAAS First

Your go-to source for SaaS insights-eager to network with SaaS leaders and fellow wordsmiths!