Optimizing Marketing Strategy for Online Skincare Brand

The global skincare market is a competitive landscape, with consumers increasingly looking for personalized and effective solutions to meet their needs. For our client, a rising online skincare brand, this meant standing out through data-driven marketing strategies. Despite a loyal customer base, the brand faced challenges in identifying patterns that could inform its promotional efforts. With over 250,000 transactions in 2023, Our client had amassed a wealth of data but lacked actionable insights. Questions like "Which products perform best together?", "When should campaigns be launched for maximum impact?", and "Which locations yield the highest ROI?" remained unanswered. To address these challenges, the company sought help in analyzing its SKU-level transaction data. The goal was to leverage advanced analytics to identify high-performing product combinations, pinpoint ideal campaign timing, and improve geographic targeting. By utilizing statistical techniques such as correlation, regression, and chi-square tests, our client uncovered valuable insights that reshaped its marketing approach. This transformation not only improved the efficiency of marketing spend but also enhanced customer engagement and loyalty.

Identifying Product Combinations: Customers often purchased multiple items together, but the brand lacked insights into which combinations were most popular. This hindered cross-selling opportunities and the creation of effective bundled offers. Seasonality and Timing: The brand struggled to identify optimal periods for launching marketing campaigns, leading to inconsistent performance. Promotions launched at the wrong time failed to capture peak customer interest. Geographic Targeting: While Radiant Glow had a nationwide presence, it lacked clarity on how regional preferences affected sales. Marketing spend was distributed uniformly, missing opportunities to target high-performing areas and optimize underperforming ones. SKU-Level Analysis: The company had a diverse product catalog but lacked clarity on which SKUs performed best under various promotional conditions, making it challenging to allocate marketing resources effectively.

To address these challenges, the following data sources were analyzed: SKU-Level Transaction Data: Covering product sales, quantities purchased, and cart compositions for over 250,000 orders. Customer Demographics: Including age, gender, and location, providing insights into buyer behavior. Sales Trends: Historical data segmented by time of year, days of the week, and specific months to identify patterns in purchasing behavior. Marketing Campaign Data: Data on past promotions, including discount rates, advertising spends, and performance metrics such as click-through rates and conversions. By integrating these datasets, a comprehensive view of the brand's sales and customer interactions was achieved, setting the foundation for deeper analysis.

To extract actionable insights, a three-pronged analytical approach was implemented: Correlation Analysis: Purpose: To identify relationships between products frequently purchased together. Example: Vitamin C Serum and Hyaluronic Acid Moisturizer showed a correlation coefficient of 0.87, indicating strong complementarity. Regression Analysis: Purpose: To predict sales performance based on marketing variables such as campaign timing, discount rates, and seasonal factors.Insights: A regression model revealed that campaigns launched during weekends had a 25% higher success rate than weekday promotions. Chi-Square Tests: Purpose: To assess the relationship between categorical variables, such as customer demographics and product categories. Insights: Tier-2 cities like Pune and Jaipur displayed a significant preference for budget-friendly skincare bundles, with a p-value < 0.05, driving region-specific marketing efforts. Visualization and Reporting: Advanced dashboards were created to visualize key metrics, enabling the marketing team to track campaign performance in real time and adjust strategies accordingly.

Product Combinations High-performing bundles like the Vitamin C Serum + Hyaluronic Acid Moisturizer and SPF 50 Sunscreen + Face Cleanser were identified. Discounts on these combos boosted sales by 20% and increased customer satisfaction due to perceived value. Optimal Campaign Periods Sales peaked during March to May and October to December. Weekend campaigns, particularly on Saturdays, consistently outperformed, generating a 25% higher engagement rate compared to weekday promotions. Geographic Targeting 1. Mumbai, Delhi, and Bengaluru emerged as top-performing regions, contributing to 80% of total sales. 2. Emerging Tier-2 markets like Pune and Jaipur demonstrated a 12% year-over-year sales growth, leading to region-specific ad campaigns and a 15% higher ROI in these areas.

Higher ROI: The brand's ROI on marketing campaigns increased by 38%. Increased Sales: Bundling popular products boosted revenue by 20% and increased the average cart value from ₹1,800 to ₹2,200. Enhanced Regional Focus: Tier-2 cities, previously untapped, now contribute 15% of the total sales, up from 10% the year before. Efficient Resource Allocation: By reallocating ad spend from underperforming areas to high-performing regions, the brand optimized its marketing resources, cutting waste by 25%.