In this blog, we will go through various applications Data Science in retail industry such as:
Let us discuss each one of them in detail.
Need for Data Science in Retail Industry
Nowadays, Data Science is one of the most in-demand technologies. It is transforming the business sector in such a way that it helps businesses manage their workflows and boost their revenues. But, why do we need Data Science? Have you ever observed that you get an advertisement for the product you have searched for in any of the retail websites? The advertisement might come on your Facebook account, on similar websites you visit, or in your mobile applications. Also, you must be getting recommendations as ‘Products Recommended for you.’ This is where the application of Data Science in retail industry comes into the picture.
You must be thinking how this could be possible. Let me tell you, this is all the miracle of Data Science. The retail websites you visit collect data of your searched product with the help of Data Science tools. Then, the recommendation engines of these websites search for similar products. After that, they send the data of the searched product, along with similar ones, to other websites for advertisement. By this type of marketing, retailers increase their sales and revenue. All major retail companies such as Amazon, Flipkart, Zalando, eBay, Myntra, etc. apply Data Science for enhancing their business. This is how Data Science helps the retail sector in identifying potential customers for selling a product. Now, we will move on to the different applications of Data Science in retail.
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Recommendation engine is one of the most widely used tools of Data Science for recommending products to customers. Nowadays, it has proved to be one of the greatest tools for marketing. A recommendation engine filters customer data to extract meaningful insights out of it. It collects the data of customers’ search history.
Also, the recommendation engine uses content-based and collaborative filtering techniques. The content-based filtering considers the data of past search history, previous shopping, preferences, etc. On the other hand, collaborative filtering looks for the data to fetch the indexes of similar products to recommend them to potential customers.
The algorithms of the recommendation engine are created in such a way that
it tries to learn from the data and adjust as per the behavior of the customer. With the help of the
recommendation engine, retailers can understand the behavior of customers and
their preferences over products. Further, it helps them in growing the business
by improving sales and thereby boosting the revenue.
Now, let us move on to the next Data Science use case in retail that is market basket analysis.
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Market Basket Analysis
This is one of the traditional Data Science tools used for customer data analysis in the retail industry. It has been widely used for years to make profits in business. The efficiency of market basket analysis depends on the amount of customers’ data collected by an organization. Data Science tools help in predicting the choices of the customers. Also, the record of customers’ data, along with their likes and dislikes, helps retailers set the appropriate price for their products. Further, it helps in targeting customers with the right advertisement.
The Data Science technique behind the methodology of market basket analysis is the rule mining algorithm. It consists of functions that divide the input dataset on the basis of certain factors and remove the useless data. Then, it builds certain links between products using association rule mining and tries to establish a relation between them. Finally, it helps in predicting that if customers buy Product A, then they are likely to buy Product B. These insights help in increasing the business revenue by making effective marketing strategies.
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Customer Sentiment Analysis
Customer sentiment analysis is one of the best and effective customer segmentation techniques. It is a Data Science and Machine Learning tool that is cost-efficient and less time-consuming.
Customers’ data gathered from various social networking platforms and websites are processed for sentiment analysis. Then, engineers perform sentiment analysis through the techniques of natural language processing such as text mining. This helps extract customers’ responses to a particular product. Then, algorithms categorize the responses into different groups for analysis. This helps them get an idea of the positive or negative attitude of customers toward the product. These categorized responses help know the customers’ feedback for the product and improve retail services. This is how Data Science in the retail industry helps enhance business through sentiment analysis.
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It is obvious that if a product’s price is comparatively less, then customers will likely buy the product from the retailer that gives it at a lesser price. Thus, the price optimization of a product becomes one of the most important tasks for any business.
A significant advantage brought by the optimization mechanism is having feasible pricing for both customers and retailers. Price optimization tools include a number of online tricks and approaches toward customers. First, data gained from multichannel sources is analyzed. It helps define the flexibility of prices, the location of customers, their buying attitude, the season of purchase, and the competitors’ pricing. By using a real-time optimization model, the retailers have an opportunity for attracting the customers to retain their attention and make use of personal pricing schemes. Essentially, it also helps the retailers give the pricing of those retailers the customers have viewed. It provides a personalized experience to the customers and other retailers to boost consumer pricing perception and retailer profitability.
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Financial losses due to fraud are common problems in any sector. Customers, as well as retailers, suffer from credit risk, financial losses, and fraud in deliveries. Due to an increase in digitalization and online transactions, the risks of fraudulent activities have also increased over the past few years. Due to such activities, customers lose trust in retailers. This is where the application of Data Science in retail industry helps.
The Data Science tool here helps in collecting and analyzing the customers’ and retailers’ data. Then, using several data visualization techniques, it tries to understand the trends and patterns in the dataset. It also tries to detect unusual activities or behavior in the dataset. Suppose, if a person makes a transaction through a credit card and the amount is deducted without asking any OTP or PIN, then it is marked as a flagged transaction by the payment gateway.
Also, it is an efficient way of protecting your money and credit from fraudsters. Previously, the software that the retailers used employed traditional methods of fraud detection. These had a typical rule-based approach that identifies the known fraud activities. These rule-based systems cannot identify an unprecedented fraud. The benefit of using Data Science and Machine Learning is that, they can learn from the previous data. Then, they can find new patterns and suspicious data that help identify fraud. This gives retailers more efficiency in detecting fraud.
To conclude, the advancements and the acceptance of Data Science applications in real-world have led toward a new era. Various application of Data Science in the retail industry has revolutionized the entire world. Researches are still going on in this field to create new tools and techniques to enhance and leverage the systems with high computing power.
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