Auto Parts Retailer Optimizes Product Assortment with APT's Test & Learn Software
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- APT Test & Learn for Sites
技术栈
- Predictive Modeling
- In-Market Experimentation
实施规模
- Enterprise-wide Deployment
影响指标
- Revenue Growth
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 预测分析
- 功能应用 - 库存管理系统
适用行业
- 汽车
- 零售
适用功能
- 销售与市场营销
- 商业运营
用例
- 补货预测
服务
- 软件设计与工程服务
- 系统集成
关于客户
The customer is a leading auto parts retailer with over $5 billion in annual sales. The company operates a vast network of stores across the United States, providing a wide range of automotive parts and accessories to both professional mechanics and DIY enthusiasts. With a strong focus on customer satisfaction and product variety, the retailer continuously seeks innovative solutions to enhance its product offerings and improve overall store performance. The company's merchandising team is particularly keen on leveraging data-driven insights to make informed decisions about product assortment and placement.
挑战
The retailer’s merchandising team wanted to optimize its product assortment within the brakes category. As a part of this effort, the retailer planned to introduce an economy brake kit. However, management was unsure how to accurately quantify the total store impact of the new product, net of potential cannibalization effects on the existing brake product line.
解决方案
Using APT’s Test & Learn for Sites software, the client designed an in-market experiment to test the new brake kit in a representative subset of stores to measure its incremental impact on profit. The software compared 'test' locations, where the new product was introduced, to customized groups of similar 'control' stores, that did not offer the economy brake kit. This methodology enabled the company to isolate the incremental profit impact of the new product amidst the noise of its sales data. APT software combined these significant drivers of performance into a highly accurate model to generate store-by-store sales performance predictions. Targeting the new product introduction to the subset of stores that were expected to perform well, based on APT’s predictive model, the auto parts retailer increased the value of the program by $8.5MM annually.
运营影响
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