Have you ever wondered how the latest retail trends are shaping the way we shop? Or Have you ever experienced personalized recommendations while shopping online? How does it work? 

Well retailers today are increasingly relying on Retail Data Analysts to derive insights, optimize operations, and enhance the overall customer experience and as per a report from Studies and Markets, the global retail analytics market is projected to experience a steady annual growth rate of 19% from 2020 to 2025. Within the next five years, the market is expected to surpass $9.5 billion in value.

So if you're intrigued by the intersection of data and retail, a career as a Retail Data Analyst might be the perfect fit. 

In this guide, we'll explore the world of retail analytics and provide a proven roadmap to help you embark on this exciting career path.

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What Is Retail Analytics?

Retail analytics involves the collection of data from various sources like sales transactions, customer interactions, inventory levels etc and this data can come from point-of-sale (cash registers), online sales, customer loyalty programs, and even social media.

This data is then used in data analysis tools and techniques to uncover valuable insights within the retail sector. It encompasses a wide range of activities, from understanding customer behavior and optimizing inventory levels to enhancing sales strategies and improving overall business performance.

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Types of Retail Data Analytics

There are four primary categories of retail data analytics, each serving a distinct purpose:

1. Descriptive Analytics

• Core focus: Reflecting and explaining past performance.

• Addresses fundamental questions of "how many, when, where, and what."

• It provides the basis for basic business intelligence tools and dashboards, offering weekly reports on sales and inventory levels.

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2. Diagnostic Analytics

• Key objective: Identifying and analyzing issues hindering performance basically knowing the “Why”.

• It utilizes data from various sources, including customer feedback, financial performance, and operational metrics.

• And aims to provide a comprehensive understanding of the root causes of encountered problems.

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3. Predictive Analytics

• Core function: Forecasting future results.

• Anticipates events based on variables such as weather, economic trends, supply chain disruptions, and competitive pressures.

• Often involves what-if analysis, allowing retailers to simulate outcomes based on different actions, like offering varying discounts on a product or estimating stock depletion.

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4. Prescriptive Analytics

• Main role: Combining AI and big data to recommend actions based on predictive analytics outcomes.

• Offers actionable insights; for instance, suggesting real-time offers for customer service agents, such as upselling based on purchase history or cross-selling to meet specific customer needs.

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How much does a Retail Analyst make in India?

The salary of a retail analyst can vary based on factors such as experience, location, industry, and company size. An entry level analyst can expect a salary of around ₹3 LPA however according to Glassdoor, on average salary for retail Analyst is ₹9,68,319 per year in India. The average additional cash compensation for a Retail Analyst in the India is ₹1,15,109, with a range from ₹64,461 - ₹6,46,845.

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How to Become a Retail Analyst: Complete Guide

Step 1. Education and Academic Foundation

If you ask any analyst out there they will have a bachelor’s degree in a quantitative field such as Business, Engineering, Statistics, Mathematics, or Data Science and some of them may have pursued a master’s degree for advanced knowledge and better career prospects.

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Step 2. Building a Data Analytical skillset

A proficient Retail Data Analyst excels in both technical and analytical domains, seamlessly blending programming proficiency with sharp analytical abilities.

1. Technical skills

On the technical front, mastery of programming languages like Python and R, along with a strong command of SQL for database management, enables seamless extraction, manipulation, and analysis of diverse retail datasets. Proficiency in data visualization tools such as Tableau and Power BI facilitates the creation of compelling visualizations that effectively convey insights. Additionally, a sound understanding of statistical packages, advanced Excel skills, and a grasp of machine learning concepts contribute to the technical prowess required for comprehensive retail data analysis. 

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Learning Resources

1. Power BI Learning Overview by Microsoft

• Level : Beginner to Advance

• Fee : Free to Access

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2. Tableau Learning by Tableau

• Level : Beginner to Advance

• Fee : Free to Access

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3. SQL for Data Analysis by Udacity

• Level : Beginner

• Fee : Free to Access

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4. Intro to python by learnpython.org

• Level : Beginner

• Fee : Free to Access

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5. Excel Basics for Data Analysis by Coursera

• Level : Beginner

• Fee : Free to audit

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2. Data Processing & Statistical Skills

Data processing and statistical skills are indispensable for a retail analyst in the retail industry. The ability to adeptly handle and process vast amounts of data ensures accurate and meaningful insights, enabling informed decision-making. Statistical skills like Mean (average), Median (middle value), and Standard Deviation (variation) on the other hand play a crucial role in uncovering patterns, trends, and relationships within the data, allowing analysts to draw actionable conclusions. 

Learning resources

1. Statistics with Python specialization by University of Michigan on Coursera

• Level : Beginner

• Duration : 1 months at 10 hours a week

• Fee : Free to audit

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Step 3. Understand retail fundamentals

We have curated a list of 3 courses to understand retail industry better. In these courses you’ll learn how to strategies retail channels, learn how to operate a CRM software like Salesforce and what are the fundamentals of tech sales. Along with few interesting books to deepen your knowledge.

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Learning Resources

1. Omnichannel Retail Strategy Specialization

• Duration : 1 month at 10 hrs/ week 

• Level : Beginner

• Fee : Free to audit
  

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2. Salesforce Sales Operations Professional Certificate

• Duration : 3 months at 10 hrs/ week

• Level : Beginner

• Fee : Free to audit

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3. Fundamentals of Technology Sales

• Duration : 4 hours

• Level : Beginner

• Fee : Free to audit

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Books: 

"The Art of Retail Buying: An Insider's Guide to Best Practices from the Industry" by Marie-Louise Jacobsen

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"Retail Management: A Strategic Approach" by Barry Berman and Joel R. Evans

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Step 4: Understand common retail software and tools

These software helps retailers analyze various aspects of their business, such as sales, inventory, customers, marketing, and supply chain to help retailers improve their performance, optimize their operations, and increase their profitability.

There are many retail analytics software available in the market, each with different features and capabilities. Some of the best retail analytics software in 2023, according to G2, are:

Crisp Data Platform, Stackline, SAS Retail Analytics, Geckoboard, Retalon, Datapine, these software help retailers and wholesalers plan and optimize their merchandising, inventory, pricing, and promotions. They help them forecast customer demand, allocate inventory, set optimal prices, and execute effective campaigns.

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Step 5. Build On-the-Job Experience

Before you become an expert retail analyst, spend a few years gaining experience. In retail, job roles like Retail Data Analyst, Retail Marketing Analyst, Retail Financial Analyst or Retail Strategy Analyst can be useful. This practical experience not only opens up better job prospects but also helps with certifications.

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Step 6: Apply for job

Try applying for internships, volunteering, or entry-level jobs in retail industry. This practical experience helps you understand how markets function, the issues they face, and how to analyze market data effectively.

You can easily find jobs, internships on the following sites:

• LinkedIn
• Indeed
• Naukri.com
• Glassdoor

As retail continues to seize data-driven decision-making, the demand for skilled analysts is on the rise. By following this proven guide, you can position yourself for success in this dynamic and impactful field, contributing to the growth and success of the retail organizations you work with. 

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Best Luck ⭐

We at Super AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialization, and the organization. With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.