For enquiries call:

Phone

+1-469-442-0620

Easter Sale-mobile

HomeBlogData Science15+ Use Cases of Data Science in Retail

15+ Use Cases of Data Science in Retail

Published
12th Sep, 2023
Views
view count loader
Read it in
26 Mins
In this article
    15+ Use Cases of Data Science in Retail

    Data is the raw material from which knowledge is forged in a business setting. These days, data is the driving force behind every business. Many large corporations from different industries want to tap into the data's potential benefits. Many economic sectors are seeing a sea change due to the answers provided by data scientists. This way, information has grown significantly for business people seeking to make sound judgments. In addition, it is possible to influence or even manipulate consumers' choices via in-depth analysis of massive amounts of data. Various information and communication methods are used for this function. 

    The retail industry is growing quickly. Retailers can analyze data and create a unique psychological image of a shopper to identify that person's pain spots. As a result, consumers are often swayed by the strategies used by stores. 

    This article provides the top sixteen data science use cases in retail and the need for data science in the retail sector to keep you abreast of the here and now. We'll explore how innovative uses of Data Science in retail are changing the industry. Are you looking for the best course material to learn data science retail projects and topics from scratch? Check out Data Science Courses Online, and get guidance from experts and industry professionals. 

    Top Data Science Use Cases in the Retail Industry 

    We'll start with a high-level overview of data science use cases for the retail industry before diving into the details. 

    1. Price optimization 
    2. Personalized Marketing 
    3. Fraud detection 
    4. Adoption of Augmented Reality 
    5. Inventory Management 
    6. Sentiment Analysis 
    7. Recommendation System 
    8. Customer lifetime value prediction 
    9. Warranty analytics 
    10. Location of New Stores 
    11. Merchandising 
    12. Intelligent cross-selling and upselling 
    13. Managing real estate 
    14. Social media trend forecasting 
    15. Behavior Analytics 
    16. Market Basket Analysis 

    Let's take a deep dive into the data-driven innovations in the retail industry, going over each of these data science use cases individually. 

    1. Price Optimization

    Seventy percent of shoppers said low cost is crucial when making a final decision. True, according to the logic of the producer price, the retail price also relies on the kind of client who buys the product and the number of resources utilized in manufacturing. Data analysis technologies allow us to go closer than ever before to solving this problem. 

    A crucial benefit brought about by the optimization methods can find a pricing point that is satisfactory for both the consumer and the store. The process of determining prices is contingent not only on the expenses involved in producing an item but also on a typical client's purchasing power and the deals other businesses offer. The tools available for data analysis provide a new degree of sophistication to solving this problem. 

    If you're in the field, several data science optimization tools can assist you (retailers) in figuring out your customers' hidden shopping habits, if you aren’t, Bootcamp Data Science is for you. A few of them are: 

    • Price Partition 

    Setting appropriate pricing is challenging since it is hard to know whether or not consumers would think it's fair. Loss of potential gain occurs in either scenario. 

    In a buyer's view, products with comparable value and a set of features ‘stick together' in clusters or segments. Customers are quite clear about the minimum, median, and maximum prices they are willing to spend for items in each sector. 

    Pricing decisions may make or ruin a company. It is possible that imitating your competition would result in a pricing war, whilst relying on guesswork might result in awful sales figures. 

    • ‘Magic’ Price Points 

    A price point is a selling price that permits a product to maintain a reasonably high level of demand. 

    According to their own subjective 'value' indicator, consumers place things into distinct price segments or groups marked by clear 'thresholds. Within these zones, sales are maximized in the middle and driven to zero in the periphery. 

    Because the seller's stock doesn't always reflect these regularities, sales performance is less-than-ideal. Because of this, most stores will lose money if they raise the price of "wrong" goods or discount them. 

    • Price Ladder 

    Like the king in a game of chess, the retailer must strategically set pricing to eliminate the threat posed by rivals or take advantage of any openings presented by them. Managing this gets more difficult as more categories emerge and price fluctuations grow more frequently. 

    The retailer's success depends on its ability to "align" with the category leaders– 

    Which prices do customers like to pay? 

    What is the price spectrum, if any, between the "premium" pricing of more expensive rivals and the "reasonable" price of a deep discount or a normal price at a discount? 

    In this market, consumers have little preference between items that perform similarly. The same holds for the price range (maximum = regular price, minimum = sale price). The ideal pricing should be based on the leader's most commonly seen price. The "spread" that best suits the market is the range of prices offered by the market leader. 

    The pricing optimization model in Data Science employs several algorithms to conduct real-time analysis of customers' opinions and reactions to promotions such as sales, discounts, and advertising campaigns. To understand such algorithms, you can check out online Data Science courses. 

    How to Employ Data Science in Retail for price optimization? 

    In retail companies, Machine Learning methods may be used in various ways to improve prices. Machine Learning enables merchants to create far more effective and intricate methods for achieving their primary business purpose. 

    Optimizing prices requires a specific set of data, i.e., customer surveys, behavior, demographics, psychographics, historic sales, etc. After the data collection phase is complete, the machine learning loop may begin. As a Data Scientist, you're familiar with the routine.

    2. Personalized Marketing

    • Retailers might benefit from implementing a marketing plan since it is the most effective method for attracting consumers. In terms of how it works, it gathers information about consumers' financial dealings. This method can potentially forecast future decisions and preferences on a massive scale. Marketing scenarios benefit more from insider information about consumer preferences and product previews. 
    • This is a mechanism that retailers employ to provide customers with tailored suggestions based on their preferences and previous actions. It also allows stores to develop ROI-boosting, hyper-specific marketing strategies. Retailers can do all this if they have access to data and can learn useful lessons from it. 
    • In many cases, this is where data science may be of use. Predictions of future consumer behavior may be produced by combining information from several customer data sources. 
    • Typically, a rule-mining algorithm is used to accomplish the analysis. It sifts through the information and pulls out what's helpful, using a specialized function to take in the data, divide it up according to certain criteria, and discard everything that's not essential. 
    • Your marketing team can better interact with the data scientist if they have a firm grasp of the data science process. Regression, classification, and clustering are just a few data science approaches that have changed marketing from an artistic discipline into a scientific one, where user behavior is studied and manipulated scientifically. 
    • Example of data science in retail: one marketing strategy that Himalaya pioneered to win over its target demographic was to provide customers with individualized solutions. The items they recommend are based on the needs and desires of the consumer base. 

    3. Fraud Detection

    • Fraud-related financial losses are a widespread issue worldwide. Credit risks, financial losses, and delivery fraud affect customers and stores alike. In recent years, fraud threats have grown with digitization and online transactions. Customers lose faith in stores as a result of such actions. This is where the retail sector may benefit from using Data Science. 
    • Rule-based approaches to fraud detection are ineffective since they pit the criminal against the seller's system. We can use massive amounts of data already acquired from online transactions to create accurate predictions about fraudulent ones. At the same time, the conventional method is rigid and cannot adapt to changing circumstances. 
    • The Application of Data Science in retail may be used to keep the company's name pristine. There is a growing difficulty in identifying fraudulent transactions for merchants. As a result of recent financial setbacks, businesses are turning to cutting-edge digital technologies like deep learning for assistance. It allows them to monitor everything at all times and catch any fraudulent behavior as soon as it occurs. 
    • Several outlier detection methods are also used to identify fraudulent activity. Time series analysis, cluster analysis, real-time monitoring of transactions, and other methods are the only ways outlier identification systems approach the issue. 
    • Data Science is used to collect and examine information on shoppers and stores. Following this, it employs several data visualization methods to deduce the underlying patterns and trends in the dataset. Also, it looks for out-of-the-ordinary actions or patterns in the data. The payment gateway will flag a transaction as suspicious if the cardholder isn't prompted for an OTP or PIN before the funds are taken. 

    4. Adoption of Augmented Reality

    • Since the 2020 pandemic drove models and photographers to work from home, Asos has increased its usage of the augmented reality technology known as "See My Fit," which allows garments to be "digitally fitted" onto models. 
    • Customers may get a feel for a product in real-time with the help of augmented reality. In a short time, augmented reality has become a must-have tool for retailers. 
    • The use of Augmented Reality driven by Data Science in retail is not particularly uncommon. 
    • Pull & Bear's parent company, Inditex, has released an augmented reality game developed in tandem with Facebook's 'Creative Shop' called Pacific Game. Players move their heads as they "fly" from California to Tokyo, dodging obstacles and picking up bonuses along the route. 
    • The game was made with social media in mind, and it can be played on the store's website, as well as on Instagram and Facebook. Instagram's front-facing camera function allows for playful experimentation. 
    • Even though high-end design houses like Gucci and Moschino have previously entered the gaming sector, this is one of the first instances of an accessible brand doing so, with the hope of appealing to the ninetieth percentile of Generation Z that plays video games. 
    • Earlier this year, Ikea's in-house design studio, Space10, updated Ikea's augmented reality product to make it more practical and engaging for users. 
    • You could virtually furnish a space using the Ikea Place app until a certain time. The brand-new Ikea Studio app takes advantage of the iPhone's built-in LiDAR sensor to allow users to take photos of complete rooms and then digitally remodel them, down to the furniture, paint, and carpets. 
    • Amazon Salon, the e-commerce giant's first hair salon, was put up to test new retail technologies. Amazon's 'Point and Learn' feature lets users point at a product on a display shelf to see brand videos and instructional resources. Customers may scan the shelf's QR code to view the product page on the e-commerce site. 
    • With the addition of this function in November 2019, Adidas has made it easier for its iOS app users to make a purchase decision without ever setting foot in a store. Developed with the computer vision platform Vyking, the augmented reality software follows the user's footsteps to simulate how sneakers appear on their bare feet. 

    5. Inventory Management

    • Retailers aim to anticipate how much of a certain item or service consumers would want to buy during a given period and then stock up accordingly. They can stockpile commodities in preparation for times of need with the assistance of this demand forecasting. 
    • Providing the right goods to the right consumer at the right time, in the right condition, and at the right location is the primary objective of every successful retail enterprise. 
    • Retailers uncover and find trends and correlations across supply chains using a variety of data analysis platforms and machine learning methods. It is useful in defining the most effective stock and inventory management techniques. 
    • The trends in sales are analyzed concerning the patterns found, and plans are developed to maximize the efficiency of the delivery of products and the management of inventories. 
    • Sophisticated machine learning algorithms and data analysis systems uncover patterns, correlations, and supply chain relationships. The best stock and inventory strategies are defined by an algorithm that is always evolving and altering its parameters and values. High demand patterns are identified, and plans for future sales trends are developed, all while supply chain management and inventory are optimized via the analysts' use of incoming data. 

    6. Sentiment Analysis

    • The method has demonstrated the greatest success in every region where it has been practiced. Sentiment analysis allows businesses to gather consumers' subjective data for a complete picture of them. Data science's introduction to the retail sector has facilitated the collection and interpretation of consumer feedback. 
    • Data Scientists may gauge the public's opinion of a brand based on input collected through surveys, social media, and other digital channels. Information from social media platforms is easily accessible. Because of this, analytics are far less of a hassle to set up on social media sites. Sentiment analytics is a branch of linguistics that uses language processing to analyze consumer feedback and determine whether or not it is positive or negative. 
    • Data Science uses text analysis and natural language processing to determine if a piece of content is favorable, negative, or neutral. The algorithms explore every level of semantic speech. All the detected emotions fall into predetermined bins and tiers. You'll get a rating for how positive, negative, or neutral the text is in those three categories, plus an overall rating. 
    • Customers' opinions on a product may be gleaned more easily this way. For further examination, algorithms classify the replies into several categories. In this way, they may evaluate whether or not buyers are enthusiastic about the goods. These sorted comments are useful for learning about consumers' experiences with the product and adjusting to the retail environment. 

    7. Recommendation System

    It is an algorithm that analyzes a user's online behavior to determine what content they most likely find interesting and useful. It is an effective method for businesses to anticipate their clients' buying habits. 

    The recommendation engine's algorithms are designed to enable it to adapt to each user by gathering and analyzing data about their preferences and actions. Merchants may learn more about their customer's habits and preferences with the recommendation engine. Additionally, it aids in the company's expansion by increasing sales and, by extension, income. Recommendation engines govern themselves and modify themselves according to the choices made by the clients. The three most common forms of recommendation algorithms are: 

    • Collaborative Filtering 

    This filtering is performed based on the product description or other data supplied for it. The system determines which items are comparable to one another depending on the context or description of those products. The user's history is considered to locate items comparable to those enjoyed by the user in the past. 

    For instance, if a user enjoys movies and wants to buy a laptop, an online retailer's website would suggest accessories necessary for using computers, such as a laptop bag, a cover, etc. 

    This sort of technology uses the preferences of many other users to generate an educated guess about what the person in question would enjoy. It assumes that if person A loves Samsung and person B likes both Samsung and Sony, then person A may also prefer Sony. 

    • Content-based Filtering 

    This kind of system concentrates its attention on the things themselves rather than on the users of the system, and it provides product suggestions based on the features or characteristics that are shared by the various products. 

    The ideas are arrived at by observing how the user interacts with the system. The history of the user's behavior is an important consideration in this scenario. For example, if the user 'A' is fond of the words 'laptop, ‘shoes,' and 'bags,' and user 'B' is fond of the words 'clothing,' 'laptop,' and 'bags,' then it's reasonable to assume that the two users have a lot of the same interests in a variety of areas. Therefore, there is a very high likelihood that user 'A' will be interested in 'clothing,' and there is an equally strong chance that user 'B' will be interested in ‘shoes.' This is the method via which the process of content-based filtering is carried out. 

    • Hybrid Filtering 

    This is a system that makes use of the aforementioned two methods and combines the outcomes that they provide. 

    8. Customer Lifetime Value Prediction

    • In the retail industry, the term "customer lifetime value," or CLV, refers to the aggregate value of a customer's contribution to a company's bottom line throughout the course of their entire association with that firm. Because of their lower degree of predictability compared to expenses, revenues get a disproportionate amount of focus. Both historical and forecast customer approaches are substantial contributors to lifetime value based on direct purchase data. 
    • Every single one of the projections is based on data from the past, all the way up to the most current transactions. As a result, the algorithms governing the life of a consumer inside a single brand are established and examined. In most cases, the CLV models will gather data about the customers' preferences, spending, most recent purchases, and behavior in order to form them into the input. This data will then be classified and cleaned. Following the processing of this data, we are provided with a linear depiction of the potential value presented by both the current and potential clients. The algorithm is also able to identify the interdependencies that exist between the qualities of the consumer and the decisions that they make. 
    • The utilization of the statistical approach provides assistance in determining the purchasing pattern of the client up to the point when the client ceases making purchases. Data science and machine learning ensure that the retailer understands its consumer, allowing for the enhancement of services and the establishment of priorities. 

    The following is the formula to determine the worth of a client throughout their lifetime: 

    • (Average Order Value) x (Number of Repeat Orders) x (Average Customer lifespan) 
    • The value of prior orders is used in the calculation of the average order value. 
    • The number of times a certain order has been placed is the same as the number of repeat sales. 
    • The amount of years that a person has been a client of your business is referred to as the Average Customer Lifespan. 

    9. Warranty analytics

    • Warranty analytics have found their way into the domain of retail as a tool that can be used for the monitoring of warranty claims, the discovery of fraudulent conduct, the reduction of expenditures, and the rise in quality. Data mining and text mining are both aspects of this process, with the ultimate objective being an improved ability to recognize recurring patterns of claims and trouble spots. The data are then transformed into plans, ideas, and recommendations that may be put into action in real time via the use of segmentation analysis. 
    • The methods of detection are very refined as a result of the fact that they must contend with unclear and copious amounts of data flows. They have a particular focus on recognizing anomalies that may be present within warranty claims. Utilizing sophisticated internet data platforms may allow for a significant acceleration of the process of processing a large number of warranty claims. This provides the retailers with a wonderful chance to convert the issues that have been occurring with the warranties into information that can be acted upon. 
    • The practice of assessing warranty data is an essential aspect of the reliability analysis operations carried out by manufacturing companies. It is essential for companies to keep a close eye on how their products perform after they are in the hands of customers so that they may conduct reliable, dependable research and make accurate forecasts. For the purpose of estimating a failure distribution, warranty data analysis makes use of the information such as the age and number of returned units, as well as the age and number of units still functioning out in the field. In the time domain, this is a task that can be completed with relative ease. Information about the time at which a component failed or survived may be extracted as of the analysis date. However, when the primary factor in determining dependability is consumption rather than time, the research takes on a more complicated nature. 
    • There are several uses that have flaws that are due to their use rather than their age. For example, the failure rate of most things in the automotive industry is determined more by the number of miles they have been driven than by how long they have been available on the market. 

    10. Location of New Stores

    • The application of data science in retail to the question of where to put the new shop shows to be an extraordinarily effective solution. In most cases, a significant amount of data analysis is required prior to making a choice of this kind. 
    • The method is simple yet incredibly effective. The analysts investigate the data of the internet clients, giving special attention to the demographic aspect of the information. The coincidental combination of ZIP code and geographic location provides a foundation for comprehending the market's potential.  
    • Additionally, unique configurations regarding the proximity of other retail establishments are taken into consideration. In addition to that, a network analysis of the retail establishment is carried out.  
    • The algorithms determine the answer by connecting all of these locations to one another. This data can be simply added to the platform that the store uses, allowing it to increase the options for analysis in another area of its operations. 

    11. Merchandising

    • The field of merchandising has developed into an important component of the retail industry. This concept encompasses the overwhelming majority of actions and approaches that are geared at increasing product sales and promoting the product. 
    • The use of merchandising tactics contributes to the facilitation of an impact on the decision-making process of the consumer through visual channels. Having products that are constantly being changed out helps to keep the range seeming new and exciting.  
    • Enhanced visual appeal and client retention may be achieved via the use of packaging and branding that is visually appealing. In this particular instance, a significant amount of data science analysis will continue to take place behind the scenes.  
    • Taking into consideration factors such as seasonality, relevance, and trends, the merchandising mechanisms sift through the data, extracting insights from it, and using them to formulate priority sets for consumers. 

    12. Intelligent cross-selling and upselling

    • Every retailer uses cross-selling and upselling to increase profits. Cross-selling refers to the process of selling related items to existing clients. 
    • In contrast, upselling is the process of offering a more expensive but superior product to a consumer. 
    • Profits in retail may be boosted with the aid of Data Science even without the use of A/B testing. We can increase our earnings by targeting certain groups of customers with targeted advertisements thanks to data science. 

    13. Managing real estate

    • The field of data science may assist major merchants in reducing the costs associated with real estate management. It is possible to avoid catastrophic failure situations by analyzing the maintenance data of the many pieces of equipment found in a structure. 
    • Retailers may save money by effectively utilizing their expenditures, which can be accomplished via the examination of historical data and the prediction of the components that need repair. The field of data science not only creates a budget but also investigates opportunities for enhancements in existing buildings, such as retail malls. 
    • Zillow, an American real estate business, has devised a prediction they call a "Zestimate." This estimate is intended to forecast how the price of a property will vary anywhere from five to 10 years in the future. 

    14. Social media trend forecasting

    • The greatest method for utilities to communicate with one another in the modern world is via social media. Many businesses in the retail sector are using new forms of global communication and marketing to expand their businesses. Retailers may benefit greatly from the wealth of consumer data provided by social media in identifying patterns, customer behavior, and trends. 
    • People express anything on social media, which is a massive platform. As a store, social media provides important information that may assist you in identifying trends. 
    • Social media is mostly composed of unstructured data, which includes a massive volume of text, photos, and videos. Natural Language Processing (NLP) techniques extract information from social media. The data is then utilized to analyze patterns and forecast what consumers want to purchase. 
    • When social media businesses' data scientists and researchers have unfettered access to the code base, they may make changes and improvements as they see appropriate. If they want a certain piece of data gathered regularly, they may write the code to do so rather than being constrained to linking onto preexisting processes and patching together instrumentation to instill meaning. 
    • Data scientists start by creating clusters of techniques for social media marketing, and then they use advanced modeling to evaluate those outcomes in controlled ad campaigns. Micro-campaigns, which allow for the controlled testing of certain data combinations, are developed by Strike's data scientists and media teams in tandem. Ad budgets are reallocated from underperforming ad sets to ones that are more on target when a combination performs or fulfills key performance benchmarks. 
    • For data analytics professionals, questioning, modeling, and testing never end, and it never can since the data is always shifting. 

    15. Behavior Analytics

    • To succeed in retail, firms need customer insights more than anything else. This data helps companies boost conversion rates, tailor their marketing efforts to each consumer, increase sales and retention, and lower the price of customer acquisition.  
    • Customers' activity data might be gathered through various channels, including smartphones, social media, brick-and-mortar stores, and e-commerce platforms. Because of their training in data science, they will be able to collect and analyze the information. Moreover, the information helps businesses identify the most valuable customers, learn what motivates them to buy a certain product, and so on. This aids firms in attracting new clients and retaining those they already have. 

    16. Market Basket Analysis 

    • The retail business has long relied on this method of analyzing consumer data, and it is now one of the most used Data Science methods. Traders have relied on it for years to increase their bottom line. Market basket analysis is only as useful as the quantity of client information a firm has. To foresee what clients will buy, data-science technologies are useful. Further, keeping track of client information and preferences allows stores to charge reasonable prices. Additionally, it aids in delivering relevant advertisements to the proper consumers. 
    • Market basket analysis is based on the rule mining algorithm, a Data Science tool. It comprises many functions that take an input dataset and segment it based on various criteria to filter out irrelevant information. Then, it uses association rule mining to create a connection by constructing a set of linkages between goods.  
    • Last but not least, it aids in forecasting the likelihood that clients would purchase Product B if they purchase Product A. 
    • Retailers may greatly benefit from enhanced development plans and marketing approaches thanks to the insight data. The selling efforts also perform at their highest level of efficiency. 

    Need for Data Science in Retail Industry

    • Investing heavily in retail data analytics is a must for any firm that cares about meeting customer expectations.  
    • Suppliers can use retail analytics to enhance their business strategy by drawing actionable insights from raw data.  
    • To boost income while cutting costs, try some of these strategies. For maximum sales potential in a highly competitive industry, both are essential. 
    • These are necessary for drawing accurate judgments about the business's financial health.  
    • Retail transaction analysis from a strategic and data-driven approach may provide valuable insight into consumer needs. On top of that, it offers a treasure trove of data on the success of stores, items, and vendors. To understand more about use cases of data science in retail banking, check out KnowledgeHut’s Data Science using R syllabus. 

    Conclusion

    Retail data's amount, diversity, and value rapidly expand each year. The retail industry uses data science to generate data-driven strategies and increase profit margins. These data science applications to retail enable businesses to compete, provide a better shopping experience for customers, and boost income. In addition, there is a lot more data science can do to improve retail as technology develops. 

    In conclusion, a new age has begun due to the development and widespread use of Data Science applications in the real world. Data science's many retail applications have changed the world as we know it. There is ongoing research to develop better methods of maximizing the potential of powerful computer systems. 

    Data scientists want to put their methods to use in all walks of life. Each business uses its unique data analysis methodology to provide better service to its clientele. Everything from sales to emails to search queries to past purchases is tracked and processed so that advertising and product placement may be fine-tuned. 

    Frequently Asked Questions (FAQs)

    1How is data science used in retail?

    The retail industry uses data science to generate data-driven strategies and increase profit margins. These data science applications to retail enable businesses to compete, provide a better shopping experience for customers, and boost income.

    2Why is data science important in retail?

    The retail industry uses data science methods to help save costs, improve decision-making, and discover new uses for data. Example applications include optimizing operational operations by predicting demand and footprint. A deeper familiarity with customer purchasing behavior may lead to increased revenue. 

    3What is retail data analytics?

    The term "retail analytics" refers to delivering essential analytical data for marketing and procurement choices based on stock levels, supply chain movement, customer demand, sales, etc.

    Profile

    Ritesh Pratap Arjun Singh

    Blog Author

    RiteshPratap A. Singh is an AI & DeepTech Data Scientist. His research interests include machine vision and cognitive intelligence. He is known for leading innovative AI projects for large corporations and PSUs. Collaborate with him in the fields of AI/ML/DL, machine vision, bioinformatics, molecular genetics, and psychology.

    Share This Article
    Ready to Master the Skills that Drive Your Career?

    Avail your free 1:1 mentorship session.

    Select
    Your Message (Optional)

    Upcoming Data Science Batches & Dates

    NameDateFeeKnow more
    Course advisor icon
    Course Advisor
    Whatsapp/Chat icon