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Understanding Customer Segmentation in Grocery Shopping: Data-Driven Insights

This study explores customer segmentation for a local grocery store, utilizing data analysis to determine how average age, gender, and time of day influence shopping experiences. Conducting a random intercept survey across three times of day—9 AM, 5:30 PM, and midnight—yielded critical insights. Key metrics included demographics, item counts, and experience ratings, revealing distinct customer patterns. This analysis aids in shaping tailored shopping experiences based on comprehensive data interpretation, transitioning from raw data to actionable knowledge.

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Understanding Customer Segmentation in Grocery Shopping: Data-Driven Insights

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Presentation Transcript

  1. Data: symbols Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions Knowledge: application of data and information; answers "how" questions Understanding: appreciation of "why" Wisdom: evaluated understanding. From: Gene Bellinger, Durval Castro, Anthony Mills (http://www.systems-thinking.org/dikw/dikw.htm)

  2. Our Mission(should we choose to accept it) • Approached to help a local grocery story figure out whether they have segments in their customer base. • Whether the segments indicate different operations strategies • They think it’s about average age, sex and time of day. • Matters because they want to shape the shopping experience around average shoppers and they think time of day matters.

  3. Design • Random intercept survey of customers at three times of day (weekdays): • 9am • 5:30pm • Midnight • Short survey: • Sex (nominal) • # of items (ratio) • Age (ratio) • Rate shopping experience (ordinal – poor, good, excellent)

  4. Analysis Plan?

  5. The Basics • n = 120 • 9am: n = 40 • 5:30pm: n = 40 • Midnight: n = 40 • Women = 64 (53%) • Men = 56 (47%) • Rating of shopping experience: • Poor = 30% • Good = 37% • Excellent = 33% • Average # of items = 16 • Average Age = 35

  6. 5:30 pm • 60% female; 40% male • Average age: 35 • Average # of items: 12 • Rating: 45%/18%/40% 9 am • 70% female; 30% male • Average age: 37.6 • Average # of items: 30 • Rating: 30%/40%/30% Midnight • 30% female; 70% male • Average age: 31 • Average # of items: 5 • Rating: 22%/46%/30%

  7. What next?

  8. Data to Information to Knowledge?

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