LibraryCapSource OpenCasesBuilding a Data-Driven Model to Predict E-Bike Demand for Solé Bicycles in Key Cities

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Building a Data-Driven Model to Predict E-Bike Demand for Solé Bicycles in Key Cities

Last Updated: 03/27/2026

Case Organization

Solé Bicycles

https://www.solebicycles.com

Venice Beach, CA, USA

1-10

Case Contributors

Case Disciplines

Customer Service & Account Management Data Management Digital Marketing Growth Strategy Market Research Purchasing, Logistics, Supply Chain

Skills & Expertise

Behavioral Analysis Customer Segmentation Data Analysis Data Collection Data Sourcing Demand Forecasting Demographic Analysis Digital Marketing Distribution Strategy Environmental Analysis Geographic Analysis Go-To-Market Strategy Growth Strategy Insight Generation Inventory Planning KPI Development Local Partnership Strategy Machine Learning Modeling Market Prioritization Market Research Marketing Strategy Model Validation Predictive Modeling Pricing Strategy Regression Analysis Risk Assessment Sales Strategy Scenario Analysis Statistical Analysis Transportation Trend Analysis
Background & Objective

The challenge or opportunity you are trying to address for the organization.

As the popularity of e-bikes continues to surge, Solé Bicycles seeks to identify and prioritize high-demand markets for expanding its e-bike offerings. E-bikes have seen rapid adoption worldwide, driven by urbanization, environmental awareness, and shifts in transportation preferences.
Learning Objectives

This is what students will learn as they complete the case.

This case focuses on applying data analytics, predictive modeling, and market strategy to inform business expansion decisions. Students will explore how to translate complex datasets into actionable insights that guide market entry, marketing investment, and operational planning. 

  •  Identify and analyze key variables that influence e-bike demand, including demographic, socioeconomic, environmental, and behavioral factors 
  •  Source, evaluate, and synthesize diverse datasets (e.g., census data, transportation trends, urban density metrics) to inform decision-making 
  •  Develop predictive models (e.g., regression, machine learning) to estimate demand across different geographic markets 
  •  Interpret model outputs to prioritize high-potential cities for business expansion 
  •  Assess how urban trends such as sustainability, commuting patterns, and infrastructure impact product adoption 
  •  Translate data insights into strategic recommendations for marketing, inventory planning, and distribution 
  •  Design city-specific go-to-market strategies tailored to local consumer preferences and conditions 
  •  Evaluate risks and barriers to entry (e.g., regulation, competition, infrastructure) and propose mitigation strategies 
  •  Develop performance metrics to track the success of market entry and marketing initiatives 
  •  Bridge technical analysis with business strategy, ensuring insights are both data-driven and actionable for decision-makers
Key Action Items

These are activities and action items you might want to complete in order to achieve the expected outcomes.

Milestones
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Milestone #3
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Milestone #4
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