Community Blog Embrace the Power of Data Analytics to Drive Retail Forward

Embrace the Power of Data Analytics to Drive Retail Forward

In this article, guest author, Moin Shaikh, explains the types of data analytics and how they can help shape the future of retail.

By Moin Shaikh

What Is Data Analytics?

Data analytics is the process of collecting, storing, and analyzing data (such as your business’s annual revenue and the numbers of highest and lowest products sold) to understand the current trends and identify any future patterns. In other words, you can understand how your business progress over the last three years and how it could progress over the next year using descriptive and predictive data analytics.

Why Do We Need Data Analytics?

One of the key objectives of data analytics is to find patterns and predictability. In many countries, data scientists, medical researchers, and government agencies have been using the power of data analytics to understand and predict the patterns of COVID-19 cases since the pandemic began last year. The powerful data analysis has helped them curb the spread of the virus, roll out an effective vaccination program, and lift lockdowns and restrictions.

The same is true for retail businesses in understanding and retaining existing customers and attracting new ones. Leveraging the power of predictive data analytics allows many modern analytics tools to offer insights that can help retailers reduce their customer churn rate and improve their customer acquisition cost and retention rate.

Types of Data Analytics

There are four basic types of data analytics: Descriptive, Diagnostic, Predictive, and Prescriptive:

  • Descriptive Analytics – Describes the situation, trend, and history of a given period
  • Diagnostic Analytics – Helps you diagnose any unpleasant situations
  • Predictive Analytics – Predicts future (sort of) based on the historical data and events
  • Prescriptive Analytics – Like a doctor’s prescription, Prescriptive Analytics helps you understand the actions you need to take to improve your key objectives.

Each one of these has specific objectives and applications, but we will focus on Predictive Analytics.

What Is Predictive Analytics?

As described earlier, one of the key objectives of data analytics is to identify patterns and predict future trends from the given set of data. Predictive Analytics helps us understand if the past or current trends are likely to continue or change. Also, if it will change, how much change should we expect.

As the name suggests, predictive analytics predicts the numbers (sales numbers, demand for a product, and inventory level in a warehouse) for a given period. For instance, if a retailer is analyzing their month-over-month sales and inventory levels of the past three years to understand their consumer spending, they can use Predictive Analytics to identify which products have been sold the most (or the least) to see the demand of a particular product for the coming year.

Prescriptive Analytics uses a number of modern tools and techniques, including statistical computing and machine learning models, to understand and identify future trends and predictabilities. We will discuss some of the most popular Predictive Analytics solutions for retail business in a future article.


All the uncertainty and fast-changing consumer behavior could make it more challenging to understand how the future will play out but relevant solutions and correct analysis can help us move forward on the road to retail.

About the Author

Moin Shaikh is a performance-driven and detail-oriented Systems Analyst and Web Developer adept at defining systems requirements and processes to enhance usability and develop websites with PHP and open-source frameworks.

Disclaimer: The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.

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5314130562411424 October 27, 2021 at 5:39 am

Very well explained. I would like to thank you for the efforts you had made for writing this blog about data analytics solutions.

Alibaba Cloud Community

900 posts | 201 followers

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