Use Cases in Retail
How data science is impacting the retail sector
Data Science has seen many applications in retail, with the most common example being Amazon. They are recommending products to customers based on similar users and improving their inventory management and shipping processes through machine learning models that can accurately predict who will order what and when. According to IBM, 62% of retailers say the use of Big Data techniques gives them a serious competitive advantage. Here are some examples of such techniques and their impact on business.
Recently, data science has become an essential tool for retailers, providing powerful insights and capabilities that can help businesses improve their operations and customer experience. From personalized product recommendations to predictive analytics, data science is helping retailers drive sales, increase revenue, and provide a better experience for their customers.
Making Strategic Decisions Based on Data-Driven Insights
Customer Feedback Survey Based on Sentiment Analysis
Accelerating Venue Booking
Boosting Sales with Market Basket Analysis
Digital Transformation of a Retail Store
Monitoring Operations Through Dashboards
Supporting Customers Through Chatbots
Recommending Relevant Banking Products
Churn Prediction for Gym Customer Retention
Other use-cases
Reduce Inventory Costs
Reducing inventory costs but always having enough products for demand is very hard to do, not to mention in uncertain times like COVID-19 brought us. More than purchase data, we need to take into account external data such as macroeconomic conditions, climate, and social data.
This complex task is suitable for machine learning algorithms since they can adapt to new circumstances and quickly analyze the vast amount of data available.
Boost sales with smarter product placement
Previous sales data can uncover purchase patterns and preferences from your customers through Market Basket Analysis. These findings can help you with suggestions of what items to put next to each other and get customers to purchase more products.
You might need to rethink some product sections to optimize cleaning time and availability, or even create a zone with extra sanitary measures.
Optimizing such a layout is certainly not an easy task, so you will need all the help you can get!
Build long-term customer relationships
Client acquisition is expensive so it's better just to avoid losing them! Having the data from your e-commerce or fidelity card means you can get to know your customers a little better and suggest to them the products they enjoy the most.
Market Basket Analysis will help you identify these preferences, which can be used to create gifts or discounts on certain products which help you build a relationship with them. This will prevent customers from going to your competitors and also contribute to acquiring new clients from your happy customers' positive reviews.
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