Cowboy Boots Marketing Campaign
Project Overview
The goal was to create an effective marketing strategy for boots in Minnesota and Wisconsin. Priced between $100 and $150, the boots cater to individuals aged 24-54 and are designed for both work and fashion. Using the provided data, I analyzed key metrics—including population, income, sales, and other demographics—to identify the zip codes most likely to generate sales. This allowed for targeted marketing efforts to maximize ROI. Lastly, I recommended advertising platforms suited to the target age group and proposed future evaluations, such as comparing marketed and non-marketed zip codes to assess ROI effectiveness. I cleaned the data and performed statistical analysis in Excel, then created a PowerPoint presentation for the marketing strategy. Additionally, I visualized the target zip codes using Tableau, all of which are linked below.
Tools & Technologies
- Excel (XLOOKUP(), Index(), Match(), Filter(), Regression Analysis, Charts)
- Tableau
- Data Analysis
- Data Cleaning
- Statistical Analysis (Regression testing, correlation analysis, VIF test)
- PowerPoint
- Marketing Analytics
Methodology
This project involved analyzing market and demographic data in different zip codes to identify performance patterns and optimize ROI.
- Data collection and cleaning
- Relationship analysis for sales with income, population, and other key metrics
- Multiple regression analysis to identify the key factors influencing sales
- Results visualization
- Campaign planning and development
Results & Impact
A summary of the results and marketing strategy is below:
According to media platform insights, YouTube is the leading platform for all age groups, followed by Facebook and Instagram, with TikTok ranking highly among younger generations within the 24-54 age group. For marketing, it’s recommended to prioritize YouTube, Facebook, and Instagram for broad reach, while TikTok can be used to target younger segments drawn to interactive content. To maximize sales, marketing efforts should focus on high-population areas, particularly those with a median income of $40,000-$50,000, as these regions are more likely to afford higher-priced products. Additionally, marketing should be concentrated in zip codes closer to the store, minimizing efforts in areas with greater distances. Avoid targeting areas with a high number of farm employees, as this has shown a negative sales correlation. Lastly, focus on zip codes with projected population growth to ensure long-term demand. Future efforts should include investigating outliers, comparing ROI from marketed versus unmarketed areas, and gathering more customer demographic data to fine-tune marketing strategies and improve targeting.
The statistical analysis revealed:
- Population: Strongest predictor of sales with a positive correlation (R² = 0.94). Marketing efforts should be concentrated on high-population areas to maximize sales.
- Income: Moderate correlation (R² = 0.2911). Target areas with median incomes of $40,000-$50,000, though income is a secondary factor.
- Distance: Weak correlation (R² = 0.24). Marketing efforts should focus on zip codes closer to the store to optimize accessibility and sales.
- Farm Employees: Initially showed a positive relationship but had a negative regression coefficient (-220), indicating that farm employees may not be a strong predictor for marketing success.
- 5-Year Growth: Focus on zip codes with projected population growth over the next 5 years, as these areas are likely to experience higher demand. Keep an eye on any areas with negative growth.
Marketing Action Plan
Media Platform Insights:
Marketing Implications:
Population
Income
Distance
Farm Employees
5-Year Growth
Future Efforts: