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Advanced Analytics is a hot topic in the digital world and it’s not going to disappear anytime soon. It’s an umbrella term that refers to analyzing data.
There are many forms of analytics, but we are going to focus on advanced analytics in this article. Advanced Analytics is a subset of analytical methods used for intelligent data analysis and insight discovery from raw data.

We can break down Advanced Analytics into two main branches: First Party Analytics and Third-Party Analytics.

First-party analytics involves using data that belongs to your company or organization, such as sales figures from your POS system, register or ERP system, inventory levels at different depots, etc.

Third-party refers to analyzing data outside your company – for example, looking at market research reports like Mintel or NewCrop, stock prices of competitors like Google or Amazon, and even social media statistics like the number of followers on Instagram or competition on Twitter.

 

 

What is Big Data and how does it relate to Advanced Analytics?

Big data is created every day through internet activities, social media, tracking of supply chains, and product use. The rise of mobile and IoT has led to a huge increase in data collection.

The amount of data stored across all industries is growing exponentially and will soon exceed 1 Zettabyte – that’s 1 billion Terabytes! So how do we process this amount of data? Advanced Analytics uses the latest technology to process these huge amounts of data and provide companies with insights and valuable information.

Big Data is a huge amount of data that cannot be processed using conventional methods or tools, the data scenario of some industries or institutions stores exorbitant volumes of information about their processes, sales, and market scenarios.

We use our phones to track our fitness and health, smart devices collect data, retailers track their stock via RFID, and product use data is collected by smart washing machines, TVs, and other smart appliances.

All this data that various devices and sensors are generating needs to be processed. Only in the last few decades has the advancement of hardware allowed the emergence of algorithms that extract insights from this data in this volumetry, transforming previously unmanageable databases into very powerful sources of information.

 

Why use Advanced Analytics in Business?

In this day and age, it’s more important than ever to understand your customer, operations, and business and how they interact with your brand. Companies that put a high value on customer experience deliver that experience much more effectively when they know what their customers want and where their pain points are. First-party analytics helps you understand your customers better.

Let’s say you run a retail business and you want to understand your customers better. Your POS data will tell you things like average spend per customer, how many items customers are buying, what items are selling well, which items are not selling etc.

This information can help you adjust your staff training and customer service approach. You can see what items customers struggle to find in your store, their preferred payment method, and more.
All of this will help you to better serve your customers and increase your sales.

Types of advanced analytics

  • Cohort Analysis: A cohort analysis is a method of analyzing user behavior by grouping them according to specific criteria, such as the time they first signed up. This makes it easier to observe and analyze user behavior.
  • Sentiment Analysis: Sentiment analysis is used to determine the feelings and attitudes of people towards a certain product, brand, or event. You can use sentiment analysis to get feedback from customers about your product or service and how you can improve it.
  • Natural Language Processing and Text Mining: Natural language processing and text mining are two subfields of textual data analysis. NLP deals with the level of language in data, whereas text mining deals with extracting knowledge from unstructured data. Text mining is used in many different fields and is also called text analysis or text-based analysis.
  • Predictive Analysis: Predictive analysis is a set of methods used to explore the likelihood of future outcomes. Businesses use predictive analysis for everything from predicting demand for their product to predicting future supply chain issues.

 

A word on Machine Learning and AI

Machine learning is a subfield of AI that uses data to train algorithms to make predictions and solve problems.
Companies like Amazon and Netflix have become experts in utilizing machine learning to improve their business and maximize profit.

One example is Netflix’s use of machine learning to determine which TV shows you will like based on what you’ve already watched. Amazon uses machine learning to decide which products you will buy based on what you’ve previously purchased.

While all of this is useful and some of it is very cool, it’s important to not get carried away with the hype. You’ll hear a lot of people talking about artificial intelligence (AI) and how it will change the world. AI is a subfield of computer science that attempts to replicate human intelligence through machines. While AI is a fascinating field and it is making great strides, it’s important to note that AI isn’t sentient and it won’t be taking over the world anytime soon.

Wrapping up

Advanced analytics is an exciting field that is constantly evolving and changing. It’s an excellent way to understand your customers better and to make improvements to your business.
With the amount of data increasing each day, it’s important for companies to learn how to process this data and find insights that will help them grow.

It’s also important to remember that analytical tools are only as good as the data that you put into them and you need to make sure that your data is clean, accurate, and properly structured. Whether you’re looking for new ways to grow your business or want to make sure you’re making the most of your existing data, advanced analytics can be incredibly useful.

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