data science in retail

Data science employs specific methods, processes, algorithms, and systems to draw insights from data. And leveraging the maximum benefit of this data to make important decisions is a primary strategy practice for any company.

Still vague about data science applications in retail industry? Have a look at the use cases top case studies of data science in retail. Start now!

How Data Science Apply in Retail Industry?

In the retail sector, data science has become challenging to understand the mentality of millions of customers around the world. That’s why retailers begin harnessing the power of data science for their industry.

Data science helps to achieve meaningful knowledge about customers and market merging trends, aiding retailers in making data-driven decisions. Moreover, retailers can construct different strategies to influence their customers by analyzing the data.

Still vague about data science applications in this industry? Have a look at the top 11 use cases of data science in retail.

11 Practical Use Cases of Data Science in Retail

1. Merchandising

Merchandising refers to all activities that help you promote and sell your product to customers, which has been a crucial part of the retail sector. This notion involves a huge variety of activities and strategies for sales increase and product promotion.

merchandising data science in retail

The practice of merchandising tricks can impact the customer’s decision-making process through visual channels. Rotating merchandise can keep the assortment stay fresh and updated. Enticing packaging and branding can grab customers’ attention and improve visual appeal. Data science in retail will help you achieve all those things.

Through the data, the merchandising mechanisms collect insights and priority sets for customers, considering relevancy, seasonality, and trends.

2. Social media

In the modern world, everyone is using social media as it’s the most convenient way of communication. The retail industries have leveraged the power of these communication bridges or technologies for their marketing activities across the world.

Social media generates an enormous amount of customer data for retailers, which helps to identify patterns, customer behaviors, and trends.

social media data science in retail

In social media companies, data scientists and researchers benefit from accessing the code base and updating it regularly for their specific purposes. They are not restricted to linking onto current processes and hacking together instrumentation to understand the meaning. If they want a piece of data gathered frequently, they can write code to collect it.

For social media marketing, data scientists often create clusters of strategies through complicated modelling. These clusters let data scientists test their results in managed advertising strategies.

The constant process of inquiry, modelling, and testing never halts in the world of data and analytics – it cannot stop as the data is ever-changing.

Understand Retail Analytics 10 Trends

3. Implementing augmented reality

In the context of Data Visualizations, Augmented Reality (AR) can become more complicated and far more dynamic in nature.

The camera displays the image of a specific domain, the domain itself is marked with points, such that when a specific point in the domain stays in view of the camera. Meanwhile, the AR system can show the specific point and then know what that specific point is.

For example, many retail businesses have utilized “Try before you buy” for marketing. AR offers the real-time experience of the product to the user. Retailers start using this incredible technology to reimagine the digital shopping experience with virtual storefronts.

Retailers can engage with their customers in real-time by providing them what they are searching for, with the right data and tools, then helping them make the right decision.

4. Upselling and cross-selling

Efficiently cross-selling even a small-ticket item can result in colossal profit increases. And data science in retail will help you rocket profits without performing different A/B tests. Plus, you can also pitch personalized offers to various customer groups to further drive conversions and sales.

When including predictive analytics in the mix, you will gain an additional edge: you can see precisely when to upsell or cross-sell to satisfy your customer goals.

For example, you can build an algorithm to discover the key value items and key-value categories that are affecting your bottom lines and pitch them to different customers.

5. Customer sentiment analysis

This is the most prevalent technique throughout the world for marketing purposes. With the aid of sentiment, the industries accumulate the subjective information of customers to better understand them. Data science in retail will make customer sentiment analysis become much easier and more cost-saving.

customer sentiment analytics

Analysts can implement customer sentiment analysis based on natural language processing text analysis to gain defining positive, negative, or neutral sentiments. The algorithms run through all meaning layers of speed and categorize all the identified sentiment to specific degrees and buckets. As a result, you will get the sentiment rating in one of the categories and the general sentiment of the text.

More use cases in Retail Analytics Use Cases: Comprehensive Guide

6. Recommendation engines

Recommendation is considered one of the most excellent marketing strategies for retailers. In data science, recommendation engines allow businesses to understand customer preferences and provide them related suggestions to buyers.

There are two main types of recommendation techniques, including:

  • Collaborative-filtering: This technique makes a prediction of what you might like according to customers’ preferences.
  • Content-based filtering: This technique concentrates on the products and suggests products that have similar characteristics and attributes.

7. Customer lifetime value modelling

It’s quite easy to decide who your most profitable and loyal customers are. However, traditional analytics cannot show you when those buyers are beginning to purchase less frequently and why they switch to one of your competitors.

Data science in retail can help businesses realize those root causes. They can uncover the dependencies between different shoppers’ choices/behaviors and employ that data to predict their future actions.

There are several examples of what you can gain with customer lifetime value modelling, including:

  • Cost of acquisition vs. lifetime value: Discover the areas for reducing costs when targeting various demographics with lifetime value potential.
  • Cost attribution: Learn which marketing channels drive the most profitable buyers; and what strategies contribute most to repeat sales.
  • Optimized retention offers: Reach out with the right pitch at the ideal time to win back individual customers.
  • Add more buyable products to the mix: Leverage data to boost the selection of buyable items in your inventory by identifying accurately what your shoppers want.

8. Price optimization

With the support of data science in retail, the price optimization mechanisms allow companies to set a price that will benefit both sellers and buyers.

price optimization

The process of prize optimization will depend on production costs and the price of similar items in the market. The data extracted from the multichannel sources help companies optimize the price of their goods, while considering the location, customer behaviors, etc.

Moreover, the algorithms also take account of customer feedback to the price changes. With powerful techniques, organizations can attract consumers and build personalized pricing schemes.

9. Fraud detection

Customer fraud ranges from the abuse of customer rights to credit risk. It can destroy the reputation of the retailers and lose customer trust.

Data science in retail can help businesses effectively encounter fraudsters via constant monitoring and detecting of suspicious activities.

Platforms powered by deep neural networks use common dimensionality reduction techniques to discover hidden patterns, fraudulent activities and mitigate dubious transactions.

10. Inventory management

With the capabilities of data science in retail, businesses no longer need to fear hindrances in inventory management. The integration of Machine Learning algorithms and data analysis platforms has provided retailers with excellent predictive analytics capabilities, letting them stock their stores with the right items at a proper time. Plus, retailers can also realize patterns in the different operations and processes of the supply chain.

11. New stores’ locations

inventory management

Data science in retail is an incredible tool for picking the most appropriate location for launching a new store. Such decisions need the analysis of a huge amount of data, market trends, customer data, locations of other nearby stores, etc.

Successful Projects in Retail Data Science

1. Amazon

Amazon is considered one of the successful online retailers all over the world. It allows buyers from all corners of the globe to purchase millions of products anytime and anywhere.

amazon

Amazon capitalizes on the power of data science for its inventory management. Whenever we want to purchase something online from Amazon, we go to the website, look for the product and add that product to our cart. Then, Amazon began offering customers recommendations related to some similar items.

With the increasing requirements of customers, Amazon keeps adding products to meet their needs at the right time. Therefore, Amazon needs to analyze a huge amount of customer data and forecast future demand patterns.

2. Target Corporation

This is the second-biggest retailer in the USA that owns various stores across the country.

target corporation

Target Corporation leveraged data science to address some of its business problems. The company stores the records of all the customer data, such as what they bought, whether they used any card, etc. Besides, they also gather some extra details like the buyer’s age, religion, marital status, educational background, income, etc.

This valuable information lets Target create personalized product recommendations for that specific segment of customers. This way, data science in retail turned out to be a landmark for Target.

3. Starbucks

starbucks

With the help of data science techniques and tools, Starbucks analyzes data to select every new opening location by traffic, area demographics, and customer behaviors.

Data science in retail can help Starbucks know whether launching a new store in a specific area will be beneficial for the brand.

4. Netflix

netflix

This video streaming service can access all the preferences and watching habits of its users across the globe. Netflix analyzes the data and suggests the content that the audience will find interesting and choose favorite movies or series.

Conclusion

The ultimate goal of all retail businesses is to meet customer needs. And data science in retail proves the best way for retailers to have a deep understanding of customer expectations.

The applications of data science in retail are bringing retailers a chance to stay in the market, advance the customer experience, and skyrocket sales and revenue. Since technology keeps changing, data science can bring more benefits to the retail world.

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author avatar

Maya

I am a content planner & writer with more than 2 years of experience in Tech. Writing mostly about Data Analytics, AI, Digital Transformation and Blockchain, I consider myself an aspiring and passionate content writer/editor who enjoys learning new technologies and introducing them to others through easy-to-read texts!

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It is crucial for retailers and those who are enthusiastic about retailing...
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The Future of Retail Analytics (10 trends included)

The Future Of Retail Analytics (Top 10 Trends In 2023)

Audio: The Future Of Retail Analytics (Top 10 Trends In 2023) It's...
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Sentiment Analysis - A comprehensive Guide

Sentiment Analysis: A Comprehensive Guide

The power of Natural Language Processing (NLP) enables organizations to implement sentiment...
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What is Data-driven culture

What Is A Data-Driven Culture and How to Create One?

One of the major shifts in today’s business world is the transformation...
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What is Data Visualization?

What Is Data Visualization And Why Is It Important? 

We live in a world of Big Data, in which data visualization...
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Video Analytics For Retail - Definition & Use Cases

Video Analytics For Retail: Definition & Use Cases 

Retail video analytics is becoming more than simply a loss prevention tool;...
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Top 10 Data Visualization Tools

Top 10 Best Data Visualization Tools (Free & Paid)

As the value of data visualization has become widely recognized across companies...
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Databases vs DataWarehouse: Similaries & Differences

Database vs Data Warehouse: Similarities & Differences

What is a database? What is a data warehouse? What are the...
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What is A Database Schema? Types, Benefits, Terms Explained

What Is Database Schema? Types, Benefits, Terms Explained

In this blog, we’ll go further into database schema including database schema...
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Data Warehouse Costs

Data Warehouse Costs: 3 Important Elements to Consider 

It’s essential to select the right data warehouse, business intelligence, and analytics...
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What Is a Data Warehouse? Definition, Benefits, Architecture Explained

What is a Data Warehouse? Definition, Benefits, Architecture Explained

A data warehouse is essential for businesses that wish to drive engagement...
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what is a data warehouse top 10 solutions compared

What is Cloud Data Warehouse? Benefits, Features & Solutions Compared

The growing demand for data warehouses as a service is propelling the...
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data warehouse tutorial for dummies

Data Warehouse Tutorial: Learn from Experts with 6 Steps

Data is gathered at regular intervals from source systems such as ERP...
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benefits of data visualization

What Are The Benefits of Data Visualization?

Data visualization lets us present sets of data through the use of...
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Data Analytics Services for Small Business: Pricing Review

Data Analytics Services for Small Business: Pricing Review

As a small business owner, you’d better implement data analytics if you...
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6 Best Practices for Small Business Analytics

6 Best Practices for Small Business Analytics

Today's successful businesses are propelled by the covert power of small business...
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Data Analytics for SMEs: What You Should Know

Data Analytics for SMEs: What You Should Know

Operating in an ever-changing market, many SMEs are facing challenges and struggling...
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How Your Business Can Benefit From Data Analytics Outsourcing

How Your Business Can Benefit From Data Analytics Outsourcing 

In recent decades, a lot of enterprises - large and SMEs, want...
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Small Business Analytics: 4 Ugly Facts

Small Business Analytics: 4 Ugly Facts

Analytics is an important part of any business, but it's not something...
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5 Data Visualization Trends in 2022

Top 5 Data Visualization Trends in 2022 and More Years to Come!

Due to the fact that technological advances are ever-changing, the future of...
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7 Things to Consider When You Hire a Data Analytics Consultant

7 Things to Consider When You Hire a Data Analytics Consultant

The data analytics consulting industry is a rapidly growing market. The U.S....
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Merge Data from Multiple Sources Effortlessly

6 Detailed Steps to Merge Data from Multiple Sources Effortlessly 

The advancement of technology has created many opportunities for the big data...
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How to Identify Bad Data and 3 Excellent Ways to Improve Data Quality

How to Identify Bad Data and 3 Excellent Ways to Improve Data Quality 

Do you know that 2.5 quintillion bytes of data are generated every...
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Top 6 common payment gateways for eCommerce stores

Top 6 Common Payment Gateways for eCommerce Stores

The world of online business is a fast-paced one. The ability to...
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7 Huge Gaps in Google Analytics And Tools to Fix Them

7 Huge Gaps in Google Analytics And Tools to Fix Them

Google Analytics is a must-have tool in the toolbox of any e-commerce...
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eCommerce advice: How to work with customers as privacy regulations are changing

Ecommerce Advice: How to Work with Customers as Privacy Regulations Are Changing

As the eCommerce industry grows and matures, consumers are becoming more aware...
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Data Warehouse Trends 2022

The Latest Trends in Data Warehouse 2022 

The world has changed since the introduction of data warehouse. Data warehouse...
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Searching for data to make critical decisions? Try these 8 sources

Searching for data to make critical decisions? Try these 8 sources 

Leveraging the gigantic database of your customers can help you seek insights...
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5 Ways Data Science Can Boost Your E-commerce Revenue

5 Ways Data Science Can Boost Your E-commerce Revenue

The 21st century has digitized the world and provided a gigantic database...
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6 Significant Impacts of Geography on E-commerce Conversions

6 Significant Impacts of Geography on E-commerce Conversions 

In online retail, e-commerce conversions happen when customers purchase products or services...
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4 Insightful Books To Master Your Data Visualization

4 Insightful Books To Master Your Data Visualization 

Data analytics is like solving a puzzle. Raw data is a clue...
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How Retail Analytics Can Help You Win Fraudsters

How Retail Analytics Can Help You Win Fraudsters 

Fraud has remained a big concern in the retail industry. The National...
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How You Can Use Data Analytics for Better Inventory Management

How You Can Use Data Analytics for Better Inventory Management  

Inventory is the most important section of a company, ranging from different...
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4 Significant Use Cases of Location Analytics in Retail

Retail Location Analytics | Definition & Use Cases 

Retail Location Analytics supports businesses with robust conditions to directly approach customers,...
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10 Most Reliable KPI Tracking Software Worth Your Consideration 

10 Most Reliable KPI Tracking Software Worth Your Consideration 

With proper KPIs, you can keep track of your campaign’s performance and...
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3 Tips on Selecting the Best E-commerce KPIs for Online Businesses 

3 Tips on Selecting the Best E-commerce KPIs for Online Businesses 

KPIs are always an essential part of any business strategy on the...
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2 Ways Internal Data Can Benefit Your E-commerce Business

2 Ways Internal Data Can Benefit Your E-commerce Business

Simply defined, data is helpful information that you accumulate to support organizational...
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8 Best Free Google Analytics Tutorials for Beginners and Experts

8 Best Free Google Analytics Tutorials for Beginners and Experts

Google Analytics is considered an indispensable tool to every website owner. It...
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3 Clever Tips to Scale Your E-commerce Businesses With Data

3 Clever Tips to Scale Your E-commerce Businesses With Data

In the cut-throat world of business, entrepreneurs must compete fiercely with each...
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10 Best Free WooCommerce Analytics Plugins for Your E-commerce Website  

10 Best Free WooCommerce Analytics Plugins for Your E-commerce Website  

WooCommerce is among the popular platforms that has been revolutionizing the digital...
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Sentiment Analysis and 2 Important Factors to Analyze Customer Feedback

Sentiment Analysis and 2 Important Factors to Analyze Customer Feedback

Satisfied customers are a constant source of happiness to businesses. Usually, every...
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Customer behavior analytics are transforming how retailer sell online

Behavior Analytics Are Transforming How Retailers Sell Online Introduction

Behavior analytics refers to the method of collecting and analyzing qualitative and...
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How Emotion Analytics is Applied in the E-Commerce Analytics to Increase Customer Engagement 

How Emotion Analytics is Applied in the E-Commerce Analytics to Increase Customer Engagement

Emotions are verbal and non-verbal communication cues that play an integral role...
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Retail Analytics Can Boost Your Merchandising Effectiveness

2 Ways Retail Analytics Can Boost Your Merchandising Effectiveness

Physical stores have been through a great transformation due to digitalization. The...
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ar in Retail Analytics

How Can Augmented Reality Be So Helpful in Retail Analytics

In the age of innovative technology, retail e-commerce presents a number of...
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retail analytics use cases

Retail Analytics Use Cases: Comprehensive Guide

Table of Contents[Open][Close]What is Retail Analytics?Effective Use Cases For Retail AnalyticsRetail Analytics...
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retail analytics

How to Boost Conversion Rates With The Use of Retail Analytics

The rapid growth of the retail industry comes with big data and...
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4 Effective Ways to Collect Customer Feedback for Analytics

Understanding the importance of customer feedback helps businesses have a full view...
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4 Ways to Use Speech Analytics for Better Customer Engagement

A significant challenge for any business is getting deep insights about customers...
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data science in retail

Data Science in Retail | Use Cases & Famous Projects

Data science employs specific methods, processes, algorithms, and systems to draw insights...
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secrets of online shopping behaviors

3 Big Secrets of Online Shopping Behaviors You Must Know

Customers have certain online shopping behaviors that businesses in the e-commerce industry...
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Customer Feedback Analysis

Detailed Customer Feedback Analysis Guide 2022

Customers have been leaving feedback more than ever before, and businesses amass...
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Comment 9

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