C5i Case Studies Attitude and usage segmentation on fast food consumption

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	Attitude and usage segmentation on fast food consumption C5i

Attitude and usage segmentation on fast food consumption

C5i

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Food & Beverage
Applicable Functions	Sales & Marketing
Use Cases	Predictive Replenishment
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Food & Beverage

Applicable Functions

Sales & Marketing

Use Cases

Predictive Replenishment

Services

Data Science Services

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.

About 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.

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.

Impact #1	Customized offerings based on segmentation
Impact #2	Proactive customer targeting
Impact #3	The segments such as “healthy obsessed,” “food advocates” became primary segments for targeting and messaging

Impact #1

Customized offerings based on segmentation

Impact #2

Proactive customer targeting

Impact #3

The segments such as “healthy obsessed,” “food advocates” became primary segments for targeting and messaging

Benefit #1	The predictive model was able to predict 95% correctly on test and training datasets

Benefit #1

The predictive model was able to predict 95% correctly on test and training datasets

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Overview

Attitude and usage segmentation on fast food consumption

Operational Impact

Quantitative Benefit