The Power of AI for Customer Data Analytics

Atif M.
5 min readJul 1, 2021

Undoubtedly, the more companies know their customers, the better experience they can provide. However, the traditional ways of acquiring these insights are labor-intensive and lengthy for analysts.

It is expensive for major corporations and even more for small businesses. Therefore, it is not astonishing that many companies have begun to deploy artificial intelligence (AI) to offer a customer experience that is informed and convenient at any given point along the customer’s journey. In fact, Forbes recently reported that 75% of enterprises employing AI and machine learning (ML) can boost customer satisfaction by more than 10%.

AI-powered data analytics provides companies significantly useful insights in a fraction of the time that it would take a human analyst, while simultaneously removing human bias and error. In no way it means that data analyst jobs are likely to become obsolete. Instead, data analysts’ role in connecting the data with the real world would become even more important and nuanced.

Before we explore how AI-powered data analytics helps in driving business results, while revolutionizing the data analyst’s role, let’s first understand AI and ML.

What is Artificial Intelligence?

In a nutshell, artificial intelligence (AI) is a technology that is meant to imitate human psychology and intelligence. It is a computer science field focused on creating machines that seem like they possess human intelligence. We call these machines’ intelligence “artificial” because humans create it, and it does not exist naturally.

Machine learning (ML) is a popular subset of AI. ML algorithms are computer-implementable instructions. They take data as input and perform computations to discover patterns within that data and use those patterns to predict the future.

An ML model improves its performance over time as it encounters more and more data and self-corrects on making mistakes to reduce the chance of repeating them in the future. ML is mostly used in systems that capture huge volumes of data. In marketing, this data is of your customers.

Marketers have plenty of data from lots of different sources. The insights buried in that data is likely to be pure…



Atif M.

CEO @ Inqline — l AI Hub events l Hosting 30-hours data science hackathons l Philanthropist l World Traveler l Athlete