Commercial Analytics Intern (Promotions, Customer & DTC Analytics)

Commercial Analytics Intern (Promotions, Customer & DTC Analytics)

Overview
Nature’s Sunshine is seeking a highly analytical intern to support data-driven decision making across promotions, customer behavior, and direct-to-consumer performance.

This role focuses on understanding how promotions, pricing, and customer dynamics influence purchasing behavior. You will work with transaction-level data to evaluate promotion effectiveness, model customer behavior, and generate insights that improve targeting, retention, and overall commercial performance.

What You’ll Work On

• Analyze promotion performance (discounts, bundles, campaigns) to measure incremental lift, ROI, and impact on customer purchasing behavior
• Evaluate how promotions influence customer behavior over time (e.g., repeat purchase, stock-up, pull-forward effects)
• Build and analyze customer segments based on purchase behavior, frequency, lifecycle stage, and engagement
• Develop models to estimate customer behavior, including:
  • likelihood to purchase
  • response to promotions
  • repeat purchase / churn risk
• Analyze cohort behavior (e.g., customers acquired through promotions vs. non-promotional channels)
• Identify trends across products, categories, and channels to inform pricing, promotion, and merchandising decisions
• Work with transaction-level datasets to create features such as recency, frequency, monetary value, and promotion exposure
• Create dashboards and analyses that improve visibility into customer and commercial performance
• Partner with marketing and business teams to translate data into actionable recommendations

What We’re Looking For

• Pursuing a degree in Statistics, Data Science, Business Analytics, Applied Math, Economics (quantitative), or similar
• Strong foundation in applied statistics, including:
  • regression analysis (especially logistic regression)
  • probability and statistical inference
  • hypothesis testing and model evaluation
• Experience or familiarity with modeling customer behavior (e.g., propensity modeling, churn prediction, or similar)
• Ability to work with real-world datasets and translate business problems into analytical approaches
• Experience with data tools (Excel required; SQL, Python, or R strongly preferred)
• Ability to communicate insights clearly and tie analysis to business decisions
• Interest in consumer behavior, DTC, retail, or CPG business models

Nice to Have

• Experience working with transaction-level or customer-level data
• Exposure to customer segmentation techniques (RFM, clustering, cohort analysis)
• Familiarity with A/B testing or evaluating marketing/promotion effectiveness
• Experience with data visualization tools (Power BI, Tableau, etc.)