Attitude and usage segmentation on fast food consumption
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Not mentioned
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Food & Beverage
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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