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Bike Store Network High-Level Optimization Analysis

Focus Area 1: Store-Level Resource Optimization

  • Question 1a (Staff): How do total order volume and revenue compare to staff counts (managers vs. non-managers) across stores to identify which location is over-staffed?

  • Question 1b (Stock): Which stores hold high stock levels in product categories that they rarely sell, indicating inventory that should be reallocated to higher-demand stores?

Focus Area 2: Candidate City Selection for In-State Regional Expansion

Context & Scope Shift

The original goal was to identify which new state should receive an expansion store by measuring out-of-state customer demand and shipping times. However, preliminary analysis revealed that every store ships exclusively to customers within its own state (making store and state logically synonymous in our dataset). Because the dataset lacks out-of-state fulfillment data, we pivoted to in-state expansion: identifying which unserved city within each state represents the strongest candidate for a new local store.

  • Question 2a (Demand Ranking): After excluding each store’s home city, which non-home cities rank in the Top 5 for both total order volume and total revenue within their respective states? Thus identifying where customer demand is highest.

  • Question 2b (Market Share): What is the share of these top-demand candidate cities in terms of revenue and number of orders out of their states' total revenue and total orders (excluding home city)?

  • Question 2c (Shipping Time Profiles): What are the shipping time profiles, measured by average, 75th percentile, and maximum shipping duration, for these top-demand candidate cities? Thus determining which locations are furthest from the current store and would benefit most from a new local store.

Focus Area 3: Candidate Store for Closure

  • Question 3a (Demand & Market Density): Which store exhibits both low overall demand (total revenue and order count) and high shipping times (average, 75th percentile, and maximum), indicating it is located in a smaller, geographically sparse market?

  • Question 3b (Quarterly Performence Trajectory): Which store exhibits the weakest recurring growth profile based on quarter-over-quarter (QoQ) metrics across both total revenue and order volume (evaluated via average QoQ growth rate and % of positive growth quarters for each). Identifying which store suffers from the most persistent structural decline.

  • Question 3c (Labor Cost Reduction): Which store employs the most staff, therefore would maximize cost savings on payroll by closing it? Also taking into account the number of managers at each store, assuming managers are paid more than non-managers.

  • Question 3d (Inventory Relocation Effort): Which store holds the lowest overall inventory levels, therefore minimizing the logistical effort to reallocate stock, if closed.