地球资源数据云——数据资源详情

零售价格优化

该数据集《Retail Price Optimization》主要用于回归/预测任务，数据形态以文本为主。 题目说明：“The moment you make a mistake in pricing, you’re eating into your reputation” 任务类型：文本回归/预测。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：retail_price.csv。 Context Price optimization is using historical data to identify the most appropriate price of a product or a service that maximizes the company’s profitability. There are numerous factors like demography, operating costs, survey data, etc that play a role in efficient pricing, it also depends on the nature of businesses and the product that is served. The business regularly adds/upgrades features to bring more value to the product and this obviously has a cost associated with it in terms of effort, time, and most importantly companies reputation. As a result, it is important to understand the correct pricing, a little too high, you lose your customers and slight underpricing will result in loss of revenue. Price optimization helps businesses strike the right balance of efficient pricing, achieving profit objectives, and also serve their customers. Content