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Food Retailer Deploys Offer Optimization to Increase Customer Loyalty & Sales
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Retail
- E-Commerce
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Replenishment
Services
- Data Science Services
- System Integration
The Challenge
A large Canadian food retailer sought to advance their loyalty program by adjusting their direct marketing plan. The successful retailer aimed to be market leaders in personalized offers, however the marketing plan did not deliver the anticipated benefits, resulting in weak response rates and decreased vendor interest. To course correct, they pursued a more sophisticated offer matching engine to assemble relevant offers for their grocery shoppers. This entails the ability to match buyer persona and product by category and store, which dictates the specific offers buyers will receive, how to win back customers and identify upsell possibilities. One of the goals was to improve market positioning using personalization as a key means to growth. Additionally, the retailer realized after unsuccessful attempts that they did not have the in-house capability to do direct marketing at the scale and sophistication of a proven provider, so they engaged Antuit from previous proven programs such as streamlining the company’s input data, building a flexible targeting engine and increasing customer engagement. Antuit’s depth of knowledge in data modeling through SAS also had improved previous results.
About The Customer
The customer is a large Canadian food retailer aiming to be market leaders in personalized offers. They have a successful track record but faced challenges in their direct marketing plan, which did not deliver the anticipated benefits. The retailer sought to improve their loyalty program and market positioning through personalization. They realized they lacked the in-house capability to execute direct marketing at the required scale and sophistication, leading them to engage Antuit, a global analytics solutions provider. Antuit has a history of helping companies predict, shape, and meet demand through deep domain expertise and proprietary solutions, including machine learning and AI.
The Solution
The food retailer turned to Antuit to design and build an insights-driven solution. The companies established a foundation of the problem and business needs, resulting in a recommendation for an offer optimization strategy. Antuit consultants analyzed performance of previous campaigns to understand which aspects of direct marketing are important for achieving customer retention and sales uplift. The overall results were further scrutinized based on customer’s profiles such as price sensitivity, lifestyle, overall loyalty along with product attributes such as seasonality, price and richness of offer. Primary conclusions for using an offer optimization approach included addressing these issues from previous campaigns: Products selected for promotion were unable to satisfy the marketing intent, Products and offers were not relevant to potential shoppers, The promotions failed to drive measurable traffic growth or sales growth, Expensive basket-level offers did not deliver a measurable increase in basket size. Antuit provided the skill set and tools to make the marketing communications relevant to the complete universe of shoppers. Specifically, Antuit had the retail knowledge and the ability to work within the client’s IT infrastructure, enabling a quick customized solution development in a cost-effective manner. The solution addressed the most important need in the client’s equation: the relevancy of offers for the shopper. To accomplish this, Antuit scored each offer for each customer to establish relevancy. The offer targeted shoppers only when the shopper matched the targeting intent and exceeded the relevancy threshold. Further, Anutit filtered the offers to prevent private label cannibalization, to avoid offer repetition and to minimize the overlap of value between offers. The solution also made basket builders more tactical and cost-effective by dynamically allocating the spend threshold to ensure stretch in customer spending. The retailer faced vendor budget constraints for each offer and needed to maximize spend on promotional communications. Given these challenges, Antuit developed an optimal allocation algorithm for each shopper that maximized relevance while keeping business constraints and objectives such as vendor budget, revenue, profitability within recommended limits.
Operational Impact
Quantitative Benefit
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