Data Analytics guarantee successful ONLINE SALE
Amazon Great Indian Sale marks the beginning of whole one week celebration of “Independence Day” in India. These campaigns aim to offer products lying in warehouse for too long at discounted price. This attracts more customers who have plans to buy certain product but haven’t yet made a purchase. The customer may have added an in shopping cart and the retailer knows their liking toward the added item. They retarget these customers through other website or social channel. When the customer sees the same ad with the added item on their cart at discounted price on other website or social media channel and they can jump back to any retailer’s shopping cart.
I’m sure the story behind purchase is interesting but today, I am more concerned with the various analytic use cases to generate more sales and revenue for any retailer. I will explain you few topics in simple words without going into complexity of actual implementation. You should know what you’re doing than doing it and never analysing why you’re doing.
Sales Forecasting and Demand Prediction
Advanced analytics project should give insights to Retailer about what is hot and what is not. The way to make that study is through Machine Learning. ML can learn from past purchase history for varied customers across different demographics. When we add Artificial Intelligence to the project, we can spot correlation and identify potential customer and their purchasing habit and divide the list of customers into different categories. Once you have studied the basket size from historical data, you can predict the purchase value for each customer next time. You should identify important drivers for product demand such as seasonal effect and predict demand for hierarchy of products.
After you know how to sell, you can assess how to sell at certain price level and to an extent you should provide discount for particular item. In order to sell a item at profit, you should consider price, quantity, revenue and competition. You may check price to maximize profit.
While browsing through e-commerce website, you many a time find “you might also like...” or “Frequently bought together”. Have you ever thought how few products got listed and the list keep changing for different individual? These products came as result from complicated algorithm that considered the past browsing history, identified preference, interest and user behaviour and predict what they are going to purchase based upon similarity with other customer. There is another type of recommendation where the item is recommended to customer is the item which is very similar to item purchased in the past. You can check more about product recommendation here.