Data Science In Retail Industry
Today, the impact of data science is felt in many sectors. Especially when we consider technological developments in the last few years, this reality has started to emerge more than ever. Perhaps data science is the biggest assistant of industries in this technology race. In this article, we will talk about the importance and uses of data science in the retail industry.
Smarter Customer Experience
Consumers expect that companies produce products for their needs and communicate with them simultaneously. Companies offer right customer experience with personalized products for customers. This also means offering a variety of customer loyalty rewards, promotions and incentives.
Companies can offer real-time offers by directly contacting targeted customers with the collected personal data. Thus, they can increase their sales and reduce their costs.
Powerful Referral Engines
Based on a user’s purchase history, items in the shopping cart, items they have recently viewed, a variety of suggestions for customers can be created. For example, more than 35% of all Amazon sales are produced by engines.
Referral engines are one of the uses of data science in retailing. Applying machine learning models to historical data creates accurate and effective suggestions.
Using Social Media to Predict Customer Trends
In the retail industry, social media helps identify customer trends by providing many free and valuable informations. Many companies can reach wider audiences through social networks such as Pinterest, Instagram and Twitter. They can communicate with these audiences 7/24.
By determining which products and services are liked, they can direct their production accordingly. They can see and prevent negative reviews about their brands, product or services For example, by clustering analysis, it can be determined which age group people prefer which product group more. While young people studying at the university prefer affordable care products, the 25–35 age group can prefer more expensive care products. In this case, campaigns can be organized in certain products depending on age groups.
Customer Lifetime Value Estimation
In the retail industry, customer lifetime value (CLV) is the total profit that a customer can bring to the company throughout the entire customer-business relationship.
CLV models collect, filter, and clean data about customers’ preferences, expenses, purchases, and behavior for a particular product. After carefully processing the data, an idea of the possible value of customers is obtained. With data science statistical methodologies and machine learning algorithms, retailers can more easily understand their customers and their needs for improving products or services.
Formula to calculate customer lifetime value:
(Average Order Value) x (Number of Repeated Orders) x (Average Customer Life)
Customer Churn Analysis
According to researchs, gaining new customers is more costly than holding existing ones. For this reason, companies try not to lose their customers. Churn Analysis is the process of using data to understand why customers stop using the product or service. In this analysis, customers’ demographic informations, past movements, frequency and duration of using the service / product they received, etc. Thus, customers who are about to lose can be identified in advance.
Product Price Optimization
Having the right price for a product directly affects sales, losing customers and gaining new customers. So how can the right price be determined? Retailers who benefit from data science tools gain a great advantage in this regard.
In most cases, you also need to know the prices that your competitors apply to determine the correct price. Detailed product price information of competitors can be accessed on their websites by using algorithms.
Fraud Detection
Fraud is increasing year by year in the retail industry. Retail sector has been the target of fraudsters in cases such as credit card, personal checks and cash fake payments (chargebacks), return receipt fraud or in-store credit application fraud. A store may be subject to fraudulent transactions, including counterfeit currency, credit cards and checks (personal, cashier, gift or travel voucher).
These kinds of activities are fraudulent transactions that are easy to overlook but cause losses. Retail stores can easily destroy their profits due to retail fraud. Today, various data analytics approaches and data science methods are needed to produce quick solutions to detect and / or prevent these frauds.
Business Intelligence Specialist
