Data Science in Retail Analytics: 6 Retailer Use Cases

Ever noticed how stores like Walmart and Target understand you better than you do yourself? In fact, that is data science in action transforming the face of retail without you even knowing. Whether it is to forecast what products will walk off the shelves or customize your shopping, data science is a game changer in retail analytics. Here’s how retailers use so much of that dream marketing data to increase sales, and also how they stock the shelves and let us shop easier than ever!

6 Data Science Retail Use Cases

The magic behind the scenes that encourages your brands to bring you everything you like is data science. Retailers are using data for more effective shopping, whether it's recommending products, forecasting trends, or stocking shelves. Here is how six top brands have been utilizing data science for the betterment of their businesses and make us go there again and again.

Amazon - Personalized Product Recommendations

If there's one thing we know to be true, it's that Amazon is very, very good at suggesting exactly the bag or pair of shoes we had JUST been looking at and thinking about buying. Do you ever get the impression that Amazon has your shopping style down better than any of your buddies? That’s data science at work! Amazon has complex machine learning algorithms that take into account everything from your browsing history, past purchases and reviews to make personalized recommendations.

Each click, search and ratings submission you make is adding to one of the largest databases, with which Amazon uses to determine what you will likely purchase next. Its like an AI-powered online shopping assistant! Learn more about how Amazon uses Data Science to achieve profits in our detailed guide.

Walmart - Optimizing Supply Chain Inventory

Walmart is huge, it's the world's number 1 company by revenue, and keeping its shelves stocked right is a challenge. Ever wonder how they do it? It's because of data science. They use data analytics to track inventory levels in real-time and predict product demand, and supply chain optimization. By just analyzing local events, historical sales data, and weather patterns, Walmart can predict when and where products will be needed. This technique helps them avoid overstocking, reduce waste, and keep the supply chain running smoothly. Thanks to data science, you can always grab that last-minute item at Walmart.

Target - Predictive Analytics for Customer Insights

Target is so good at knowing what their customers want before they even know it. By using predictive analysis, Target tries to understand customer behavior by deep digging into customer data such as past purchases, shopping frequency, and demographic information.

This helps them to understand their buying trends in the future, plan marketing campaigns accordingly, and offer them personalized deals. Their data science game is so strong, once they sent baby product ads to a teen because their algorithm predicted her pregnancy before she even told her family.

Starbucks - Location-based Sales Prediction

These days, Starbucks is pretty much everywhere you look. That’s no accident. They use data science algorithms to find the best locations for their new stores based on demographics, foot traffic, and even neighborhood trends. By analyzing data from previous sales, geographic information, and local events, they predict where their stores will thrive. They also use the same data to optimize which products they are gonna stock in each store. So whether you are in a quiet town or downtown of a big city, Starbucks knows how to serve you the best coffee, exactly where you need it.

Zara - Fast Fashion Demand Prediction

Zara, the brand everyone love and is the master of trending fashion. Data science helps them stay ahead of the fashion game. Have you ever noticed how they change their clothing lineup every week and how all of the stores don't have similar clothes? They use demand prediction model in order to forecast which styles will be popular in which areas.

They make sure they produce the right amount of stock without overproducing. Analyzing customer behavior, sales data and trends in real time, Zara can adjust production and restock the stores with new clothes within just weeks. They use data driven approaches to decide which items to discount or pull from shelves which helps them maximize profits while staying on trend.

Sephora - Customer Loyalty and Personalized Marketing

Sephora differentiates itself in the beauty retail industry because it applies data science to increase customer loyalty and drive personalized marketing. Their beauty insider loyalty program is their key strategy, where the customer gets points with every purchase that counts them into special events, offers and products. With this program, they don't only increase repeat purchases but also collect vast amount of customer data based on their preferences. This way, analyzing such data allows Sephora to launch more pointed marketing campaigns, providing customers with product recommendations based on what they are interested in.

To learn more about the connection between data science and marketing, check out this blog post on how data science is transforming marketing strategies.

How to get a job in Retail Data Science

If you have a passion for combining data science with retail industry, it can open many opportunities for you. Here's a little guide on how you can get a job in Retail Data Science.

• Be Familiar with Retail Landscape: Understand key concepts such as supply chain management, customer behavior and inventory control. This foundation understanding will help you see how data science applies in the retail industry.
• Build Skills: Be proficient in programming languages like Python or R, focus on statistical analysis, and learn data visualization tools such as Tableau. Familiarize yourself with retail-specific systems like POS and inventory management software.
• Networking: Go to events within the industry, network on LinkedIn with professionals. Gain practical experience through internships and personal projects that showcase your ability to analyze data.

Networking is a key skill in every form of Data Science, read this Behavioral Interview Guide to find key tips like the STAR method to make you more memorable in conversations!

• Tailor your Application: Customize your resume and portfolio to show your experience with related activities from the job description, then practice some common data science questions in preparation for the interview. Stay updated with trends in the retail industry that makes you a more appealing candidate.

Start applying for positions and explore our Data Science job board to find exciting opportunities that match your skills and interests!

Skills in Retail Data Science

To get a job in Retail Data Science, you need to have the right skills. A solid foundation in statistics, A/B testing, being proficient in SQL, and understanding machine learning algorithms to analyze customer behavior and sales trends in mandatory.

To help you get a data science job in retail, DataLemur has a ton of free resources:

• Statistics: This A/B Testing guide is helpful for statistical preparation.
• Machine Learning: Brush up foundations with this Machine Learning overview.
• SQL: Practice SQL with this SQL tutorial.
• Interview Questions: To get an idea of what kind of interview questions could be asked, check Product Sense interview questions.