Enhanced in-store experience for a leading CPG brand

Case Studies:Mu Sigma
Published On: 20 November 2015
Views: 1149

What We Did: A behavioral tracking and measurement tool was created to understand and measure customer behavior/preferences for effective decision making. This was done by analyzing survey data across various customer segments.


The Impact We Made: Insights from the behavioral tracking and measurement tool led to multiple recommendations around store remodeling, space allocations, adjacencies of departments, and shopping preferences. This lead to overall improvement in customer experience, resulting in a 17% increase in overall sales through significant growth across all major product categories.


Summary: Enhancing the customer experience

Marketing and branding team of a leading CPG organization wanted to study consumer behavior and preferences to enhance their customers’ shopping experience. Multiple surveys were conducted to gauge customers’ preferences. The client approached Mu Sigma to analyze the survey results and suggest solutions that could work towards enhancing customer experience w.r.t each customer segment.


About The Client: Leading CPG player

The client is a leading CPG in the US. It has 20+ brands that generate retail sales of more than $1 billion individually. The company's products are distributed across 200 countries.


The Challenge: Inability to make sense of disparate data and insights

The client had plethora of information available in the form of surveys filled by customers. However, they lacked a structured framework to analyze it. Business decisions were made in ignorance of customer’s brand affinity, preferences/ likings, etc.


The Approach: Deciphering consumer preferences and behaviors

Mu Sigma analyzed customer behavior across various survey results:

  • Different dimensions affecting consumer behavior were identified and care was taken to measure variables without any bias.

  • Against each customer segment, scores were calculated using data mining techniques such as Simple Indexing and Cohen’s techniques. This process highlighted the most significant attributes.

  • Each score was then mapped with customer demographics and a perceptual map was constructed to understand consumer preferences and behavior.


The Outcome: An analytical tool and framework for more effective decision making

The Mu Sigma team was able to create a tool to analyze customer surveys and provide recommendations for the management team on an ongoing basis to support effective decision making:

  • Recommendations about macro space allocation, adjacencies for departments and shopping preferences for both products and retailer helped increase the overall sales by 17%.

  • Also, helped managers and leadership team analyze these surveys independently to expedite strategic decision making.

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