Regional Grocery Chain Optimizes Promotion Pricing to Compete More Effectively
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- AD-IN
Tech Stack
- Predictive Analytics
- Machine Learning
- Proprietary Algorithms
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Revenue Growth
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Retail
- E-Commerce
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Demand Planning & Forecasting
Services
- Data Science Services
- System Integration
About The Customer
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.
The Challenge
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.'
The Solution
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.
Operational Impact
Quantitative Benefit
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