Data Analytics & AI: How it is Used

Data Analytics & AI: How it is Used

15 April 17 April ~ 12 min read 1310 views
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Claspo Blog Data Analytics & AI: How it is Used

The use of AI in data analytics makes this business management tool available to everyone. Five to ten years ago, it was mainly used by large corporations that created special departments and hired dozens of highly paid specialists. Today, data analysis can be used by medium-sized companies, small startups, and even individual entrepreneurs. 

This article will explain how AI in data analytics works, the benefits of new technologies, and what software platforms are available. 

How is AI Used in Data Analytics

Artificial intelligence is some kind of magic. Of course, this is a joke, but there is some truth in it. Unlike computer programs of previous generations, self-learning models do not work according to fixed algorithms. When they receive new information, they change the principles of their work and rebuild and optimize internal processes. 

However, the general principle of how AI works in data analytics is clear: Artificial intelligence searches for relationships between different indicators and groups of indicators, testing various hypotheses - even improbable ones. When it finds the correct answer, it adds it to its code and performs the next phase more efficiently. Where exactly is such a correlation search function useful? Let's take a closer look. 

1. Predictive analytics

This is the No. 1 topic in e-commerce in 2024. Predictive analytics' main task is to study past events to identify patterns, build mathematical models, and forecast the future. It is used in logistics, inventory management, pricing, and other areas. 

However, the most promising way to use AI for data analytics is to predict the behavior of website visitors. Given a limited set of input data, such as traffic source and user's geographic location and search query, artificial intelligence can classify visitors into specific target audience groups and choose the best conversion tactics for them. Each subsequent action of a potential customer will increase the forecasting accuracy.

Predictive analytics allows you to launch personalized marketing campaigns for narrow segments of the target audience and, if you have high computing power, for each website visitor. It significantly increases conversion rates and helps attract customers more effectively. After launching personalized campaigns on various communication channels, it's time to enlist the help of Claspo. With our advanced targeting, you can show widgets only to visitors who came to your website from a specific campaign, source, communication channel, or PPC ad with a particular keyword.

That is, users who came to you from an email campaign and those who got to the site by clicking on an ad in Google will see different widgets with different offers. It ensures a personalized experience and consistent omnichannel communication with customers.


2. Data-driven decision making

According to statistics from the IT consulting firm V500 Systems, artificial intelligence makes mistakes on average in 1.9% of cases, while an experienced manager makes mistakes in 19.4% of cases. The number of AI mistakes is ten times less! Of course, AI requires human supervision, as it cannot account for some psychological factors, has no idea of ethics, and does not always accurately capture people's desires. 

However, you can safely entrust artificial intelligence with solving many issues:

  • Inventory management. If few products are left in a specific warehouse, the program can decide to move them.
  • Choosing the right moment to launch a promotion. The system will analyze consumers’ sentiments and competitors' actions and launch a marketing campaign when it can get the best response.
  • Customer retention. By studying customers’ behavior, artificial intelligence will warn about their dissatisfaction and the high probability of switching to competitors. This will allow you to create a special offer or provide the necessary support in a timely manner. 

Claspo can work in tandem with AI data:

  • Excess goods in stock? Run a sale and promote it with our widgets to eliminate excess and make room for new arrivals.
  • Do you know the perfect moment to launch a promotion? In a few minutes, create a themed widget and specify its start and end dates for its display to fit in with the AI-defined timelines.
  • High probability of customers switching to competitors? Trust our widgets to retain them by offering discounts, bonuses, or personal consultations with the support team. Then, test what works best for customer retention with A/B testing in our builder.

Whatever solution AI offers you, Claspo will help implement it.


3. Natural Language Processing (NLP) 

For a long time, customer support has been one of the least automated business areas. Unlike marketing or logistics, it deals not with numbers but with words, which are very difficult to analyze using traditional methods. 

But artificial intelligence can optimize this process in the following ways:

  • select keywords to define the topic and subtopic of a particular message;
  • set the tone of the message to choose the right degree of importance;
  • link the message to a specific product, marketing campaign, or company unit. 

According to Master of Code, using data analytics and AI in customer support can reduce time spent by 62% and increase the speed of decision-making by 57%. This increases loyalty and reduces the churn rate. 

With Claspo, you can help site visitors easily contact your support service. It will provide AI with data for further analysis and improve the customer experience. Our widgets may offer a callback option or provide a list of instant messengers to reach you quickly. A huge advantage is that they can appear at the right moment. For example, if a user has been inactive on the site for a long time, viewed several pages without proceeding to checkout, or is about to leave the site, they may need help from your support team. Therefore, a corresponding widget would be very appropriate.


Benefits of Using AI in Data Analytics

Artificial intelligence takes the best from a computer program and the human brain. It can perform billions of calculations per second and make decisions on its own, looking for the best ways to accomplish tasks. Of course, it has its drawbacks, so it cannot replace all responsible employees in business yet. 

Nevertheless, using AI in data analytics allows you to get a number of benefits right here and now. What are they? Let's see together. 

1. Availability of analytics

Artificial intelligence works in an autonomous mode. By "feeding" it an array of data, you get all possible patterns with mathematical models and indicators of the strength of the connection. You do not have to spend years studying statistics and information technology. 

2. Higher level of automation

By integrating data analytics and AI into your existing workflows, you can make them faster and more efficient. Artificial intelligence will independently select the necessary data sets, find patterns, suggest marketing campaigns, track their results, and generate reports. 

3. Veracity of information

Unfortunately, there can be mistakes in both calculations and input data. But artificial intelligence can detect them in time and warn you. If an employee fills in a field incorrectly or skips a few lines in a form, it won't cause the next marketing campaign to fail. 

4. Improving the user experience

Data analysis using artificial intelligence allows you to rebuild web pages and create personalized offers on the go. Each user sees their own version of the website, which increases the level of engagement and the probability of conversion. 

5 Best AI Data Analytics Tools

We have already mentioned that artificial intelligence has made analytics democratic and accessible. There are already dozens of web services for automated information processing online. They include corporate platforms and relatively simple solutions with affordable terms of use. We have selected the five best AI data analytics tools for you that are simple and yet powerful. 

1. Tableau

This platform for data science professionals allows you to build scenarios in the form of simple algorithms or customize mathematical models using programming languages such as R, Python, or MATLAB. Tableau offers tools for determining probabilities and risk planning. It is a perfect choice for large companies with a serious scale of activity. 


Subscriptions start from $75 per month per workstation.

2. Microsoft Power BI

This popular AI analytics tool offers almost every mathematical modeling technique known to humanity. Recently, it has added natural language processing functions to work with customer requests, text content, and samples of advertising campaigns. The platform has a relatively user-friendly interface, although it can take a lot of time to get acquainted with it due to the wide range of features. 


Subscriptions start from $10 per month, and there is a trial version with limited functionality.

3. Databricks

This universal platform for data storage and analysis allows users to create data sets and connect information flows from other sources to them. Users can use its built-in artificial intelligence or create their own AI applications. 


The cost of the subscription is determined by the number of transactions — it depends on the scale of the activity.

4. AnswerRocket

This virtual assistant is perfect for analytics beginners. It's as simple as possible to use: you make queries in spoken English, provide clarifying answers if necessary, and get the desired result. 


The subscription cost is determined individually during a discussion with the company's representatives.


This open-source platform offers a free subscription for individual users. Its basic version is very user-friendly — you can create mathematical models in the form of flowcharts by simply dragging and dropping the necessary elements into the development area. 


Subscriptions for corporate clients start from $99 per month.

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