This project explores customer purchasing behavior in the retail chip category and evaluates the impact of targeted store interventions. It was completed in two phases:
Task One: Segment customers by lifestage and premium tier to identify high-value groups and behavioral patterns.
Task Two: Analyze trial store performance using key retail KPIs to assess the effectiveness of promotional strategies.
The goal was to generate actionable insights that support data-driven decision-making in customer targeting, promotional planning, and strategic scaling.
Identify which customer segments contribute most to chip sales.
Understand purchasing behavior across lifestages and premium tiers.
Evaluate the success of trial store strategies using comparative KPI analysis.
Recommend scalable actions based on performance data.
Retailers often struggle to translate raw sales data into strategic action. This project was designed to bridge that gap by:
Segmenting customers based on demographic and value tiers.
Visualizing sales trends and behavioral patterns.
Comparing trial and control stores to assess promotional impact.
By combining segmentation analysis with store-level performance evaluation, the project delivers a holistic view of customer behavior and strategic opportunity.
Data Exploration: Reviewed transactional data segmented by customer lifestage and premium tier.
Visualization: Created pie charts, bar graphs, and stacked percentage charts to illustrate sales volume, customer distribution, and quantity sold.
Insight Generation: Identified Mainstream Older Families and Retirees as the most valuable segments, and highlighted behavioral gaps in younger demographics.
Strategic Framing: Delivered recommendations on promotional targeting and assortment optimization based on segment performance.
KPI Selection: Focused on three core metrics — total sales, unique customers, and transactions per customer.
Control Store Matching: Control stores were selected based on similarity in customer profile, sales volume, and historical performance trends to ensure a fair comparison.
Comparative Analysis: Used line charts to compare trial stores (77, 86, 88) against their matched control counterparts over time.
Intervention Context: Store 77 received a new store layout during the trial period, which likely influenced traffic patterns and shopper behavior.
Interpretation: Found that Store 88 showed consistent uplift across all KPIs, while Store 77’s growth was driven by increased traffic but not basket size.
Recommendation: Proposed scaling Store 88’s strategy to similar profiles and reassessing execution in Stores 77 and 86.
Mainstream customers drive the highest sales across all lifestages, especially among Older Families and Retirees.
Young Singles/Couples, despite being the largest customer group, contribute less to total sales — indicating untapped potential.
Premium customers represent a smaller share of volume but offer margin opportunities.
Store 88 demonstrated strong uplift across all KPIs, making it a clear candidate for strategy rollout.
Store 77 showed increased traffic likely driven by the new store layout, but declining basket size suggests limited engagement.
Store 86 showed moderate improvement in customer volume, but flat performance in other metrics.
Visuals from analysis
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Scale Store 88’s strategy to stores with similar customer behavior and sales profiles.
Reassess execution in Stores 77 and 86 before further rollout.
Use total sales, customer volume, and transactions per customer as a framework for identifying high-potential stores in future trials.
Store 88 delivered consistent growth across all KPIs.
Store 86 showed moderate improvement in customer volume but flat performance elsewhere.
Store 77’s uplift was driven by increased traffic, likely due to the new store layout, but did not translate into larger baskets or deeper engagement.
This project demonstrates how retail analytics can uncover hidden value in customer behavior and store performance. By segmenting customers and evaluating trial strategies, we gain clarity on where to invest, how to target, and what to scale.
The insights are not just descriptive — they’re strategic. They empower retailers to move from reactive decisions to proactive planning, grounded in data and driven by results.