What is the biggest value of a business? We think it is definitely collected data on the customers. Customer information is hugely important in many aspects of the company, including marketing, onboarding, customer support, and product development.
However, to extract great value from customer data, you need to have an efficient data analytics system. As in many areas in today’s business world, AI finds its place in data analytics as well, as a very powerful tool to help decision-makers during and after the analysis process.
In this article, we’ll tell you about how you can use AI in data analysis for a more effective and valuable strategy and whatever you would like to use data for.
Putting the Results into Words for Better Understanding
Data analytics is about explaining insights and examining the data as deeply as possible to make conclusions. This can be quite challenging for those who aren’t as big of an expert on reading between the lines, however, with the help of AI, anyone can gain access to valuable and important information from huge datasets.
AI can analyze patterns, anomalies, or trends based on large databases, which would otherwise take days for data analysts to do by themselves. In addition, AI is great for the analysis of streaming real-time data.
Imagine it this way. You ask AI to create a report of all customers who visited your website but left within 5 minutes. All with asking in natural language. This is particularly useful in, for example, churn analysis.
In my conversation with Dhurim Arllati, Data Researcher at Veeva Systems, he said “I think everyone can use AI to their advantage, AI tools can process data much faster than humans, meaning that the insights you gain from your analyses are quicker, and more accurate. This makes it easier for organizations to make and act on decisions quickly. So one of the best ways to use AI is to use it for speed and efficiency.”
Basically, you have a virtual colleague who works extremely fast, looks at real-time data, and extracts the information you ask for in a matter of seconds.
AI for Sentiment Analysis in Customer Data
What takes AI to the next level is the ability to process and understand natural language. This capability plays an essential role in sentiment analysis. But what is this really good for, you may ask.
Sentiment analysis involves looking at the customers’ perceptions of the product or service through the feedback they give, comments on social media, or even conversations with customer support. AI can determine whether the customer’s stance is positive, negative, or neutral.
With this, you can easily gain insight into what is working as it should in the company, what are the pain points of your customers, which you can obviously hook on later, or any potential areas where your product needs improvement.
One company that utilizes sentiment analysis like a professional is Netflix. Research done by Priti Bagkar, Aishwarya Borude, and Zarrin Aga on the Sentiment Analysis on Netflix said “From consumer research Netflix has conducted, it suggested that an ordinary Netflix user loses interest after 60 seconds of choosing or reviewing more than 10 to 20 titles in detail. Therefore, Netflix developed a recommender system over the years, which consists of various algorithms that are combined into an ensemble method”.
Using AI for Predictive Analytics and Looking into the Future
If you provide a large enough dataset, AI excels in predicting what might happen in the future. Obviously, people can do that too, but it requires a lot of hard work and even more time to analyze the data and guess what might happen in the future based on that.
AI is capable of seeing patterns easily in historical data, which then is transformed into predictive suggestions about sales, customer behaviors, customer needs, and marketing efforts. How good is it that you can anticipate what will happen in your business and prepare yourself to take advantage of it as much as possible?
One example of a successful implementation of predictive analytics is the Bank of America. They use it to gain a better understanding of the relationship between ECM (Equity Capital Markets) deals and investors. David Reilly, the CIO of and Finance Technology Executive at the Bank of America said in an interview by CIO, “We’re bringing data and analytics to the table. Not to replace anything, but to supplement that rich relationship and market intelligence data our banking partners have when they’re out in the field.”
When asked about the usage of AI in data analysis, Srushti Shah, Data Analyst at Folloze, mentioned, among many other things, that “AI can analyze historical data and identify patterns to predict future trends. This allows businesses to make data-driven decisions and optimize their processes. For instance, supervised machine learning can be used to categorize data, such as food recipes in point-of-sale systems. This not only improves accuracy but also streamlines operations by integrating the model into existing systems, leading to increased efficiency.”
Preventing Fraud and Detecting Anomalies
Nowadays, online businesses especially, need to put an emphasis on fraud prevention for an ideal operation. There are more and more scammers who want to exploit different services and products in an unauthorized manner.
To avoid such misuse, the best way is to look at real-time data and examine any anomalies that might indicate that someone is using the product unrightfully. However, this requires continuous, ongoing, never-stopping analysis of real-time information, which is almost humanly impossible to carry out. However, AI can lend a helping hand at that as well.
Also by looking at patterns or trends, AI can quickly detect unusual patterns or outstanding and suspicious activities in customer data, allowing for an instant reaction and prevention.
I brought a real-life example of a successful implementation of this technology as well. And this is Spotify. Among many other ways Spotify uses AI, like smart recommendations, AI DJ, or Daylists, it uses the technology to detect fraudulent streaming activities as well. This way, they can decrease such false streamings by bots that would otherwise increase the plays of songs and generate false success.
Conclusion
In conclusion, AI significantly enhances data analysis by efficiently converting complex datasets into understandable and actionable insights. It supports companies in comprehending customer sentiments, forecasting future behaviors, and safeguarding against fraudulent activities.
Furthermore, AI's ability to process and analyze data in real time allows for a chance to adapt quickly to market changes and improve customer experiences and innovation.
AI is the future in many aspects, including data analytics. So, how would you use artificial intelligence in your online business’s data analysis?
