Regional Grocery Chain Optimizes Promotion Pricing to Compete More Effectively
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- AD-IN
技术栈
- Predictive Analytics
- Machine Learning
- Proprietary Algorithms
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Revenue Growth
- Customer Satisfaction
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 零售
- 电子商务
适用功能
- 销售与市场营销
- 商业运营
用例
- 需求计划与预测
服务
- 数据科学服务
- 系统集成
关于客户
The customer is a regional supermarket chain that operates in a highly competitive market dominated by big-box, low-cost retailers like Walmart. The chain has a significant presence in its region and is known for its commitment to providing quality products and services to its customers. However, the increasing competition from price-driven providers has put pressure on the supermarket chain to find innovative ways to maintain its market share and profitability. The company has a large customer base that values both the quality and affordability of its products, making it crucial for the chain to balance competitive pricing with maintaining healthy profit margins.
挑战
A regional supermarket chain needed to defend its market share against big-box, low-cost retailers and other price-driven providers. The retailer responded by matching the lowest prices in their markets, knowing this was not a viable approach long-term due to the negative impact on margin. For a sustainable advantage, the company needed a more effective and efficient promotion solution to improve its competitive position without 'giving away the store.'
解决方案
The grocer turned to Antuit to improve its retail relevancy through a better understanding of its shoppers’ wants, needs, and behaviors. Antuit worked collaboratively with the retailer to implement a solution that maintained margins and boosted its ability to compete. AD-IN, Antuit’s weekly ad circular optimization tool, was a key component of the solution. Using the retailer’s transaction and loyalty data, Antuit applied predictive analytics within AD-IN to identify key purchasing behaviors, including visit frequency, basket sizes, price elasticity, and promotion responsiveness. Using advanced analytics and proprietary algorithms, Antuit mined all relevant shopper data to define price elasticities and demand curves to better understand shopping behavior, seasonality, and pricing on known value items (KVIs). Antuit’s AD-IN solution also delivered an intuitive visual interface that allowed users to easily connect insights to decisions and accelerate adoption. Planners provided business rules and constraints, such as price min/max, promotion time frames, and inventory thresholds. The solution’s decision engine then determined the optimal price points for the entire product portfolio based on store traffic, sales, and margins.
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