Attitude and usage segmentation on fast food consumption
公司规模
1,000+
地区
- America
国家
- United States
产品
- Not mentioned
技术栈
- Not mentioned
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Revenue Growth
技术
- 分析与建模 - 预测分析
适用行业
- 食品与饮料
适用功能
- 销售与市场营销
用例
- 补货预测
服务
- 数据科学服务
关于客户
The customer is a leading fast food restaurant chain operating in the United States. They are part of the consumer goods and services industry, specifically in the food and beverage sector. The client has been experiencing a decline in sales in certain metropolitan areas over a period of time. They are interested in understanding these markets more thoroughly and are looking to quantify the target opportunity through an attitudinal lens. They aim to profile customers by their fast food consumption behavior and develop specific offerings keeping past usage/behaviors in mind.
挑战
The client, a leading fast food restaurant chain, had been experiencing a decline in sales in few US metropolitan areas over a period of time and was interested in understanding these markets more thoroughly. They wanted to quantify the target opportunity through an attitudinal lens, and profile customers by their fast food consumption behavior. The attitude/ lifestyle-based segments also needed to cross over with client ready behavioral segments in order to develop specific offerings keeping past usage/behaviors in mind. They were also interested in targeting “value” segments more strategically so they were looking to build a predictive model to classify prospect customers into attitudinal segments.
解决方案
Blueocean Market Intelligence was asked to conduct a segmentation analysis and predictive model to enable a better understanding of customer attitude and usage, with respect to fast food. They ran the segmentation using several approaches such as agglomerative, divisive, pattern detection etc. and then the results were cross-compared. The common emerging segments were further investigated for validity and mathematical properties. The segments derived were used as target variables and a mathematical model was developed using discriminant analysis model to predict segment classification based on usage, behavioral and demographic variables.
运营影响
数量效益
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