Tommy Hilfiger has emerged as one of the leading fashion brands in the world by effectively leveraging data analytics across various aspects of its marketing approach. Some of the key ways in which the company uses data analytics include:
Customer profiling and segmentation: Tommy Hilfiger gathers extensive customer data from various online and offline touchpoints. This includes transaction data, website behavior data, social media engagement data, loyalty program data, and more. The company analyzes this wealth of customer data to develop rich customer profiles and segment customers based on attributes like demographics, purchase history, lifestyle patterns, engagement preferences, and more. This helps the brand develop highly targeted and personalized marketing campaigns for different customer segments.
Predictive analysis of customer behavior: Tommy Hilfiger combines its customer profiles and segmentation with predictive modeling techniques to analyze historical customer data and identify patterns in customer behaviors. This helps the company predict future customer behaviors like likelihood of purchase, priority product categories, engagement preferences, loyalty patterns, churn risk, and so on for individual customers or segments. Such predictive insights enable Tommy Hilfiger to implement highly customized and predictive marketing campaigns.
Personalized communication and offers: Leveraging its customer profiling, segmentation, and predictive analysis capabilities, Tommy Hilfiger sends hyper-personalized communications including catalogs, emails, push notifications, and offers to its customers. For example, it may promote new arrivals specifically catering to the past purchase history of a high value customer and offer them additional discounts. Such personalization has significantly boosted customer engagement and spending for the brand.
Cross-selling and upselling: Data analytics helps Tommy Hilfiger identify related and complementary product categories that an individual customer may be interested based on their past purchases. It employs this to dynamically send targeted cross-selling and upselling recommendations. For instance, it can detect customers who frequently purchase jeans and actively promote shirts and accessories that will complement the jeans. This has noticeably increased its average order value over time.
Omnichannel attribution modeling: With customers engaging via multiple channels today, it is important to analyze the impact of each touchpoint. Tommy Hilfiger uses advanced attribution modeling to recognize the actual impact and value of each marketing channel toward final online and offline conversions. This provides valuable insights into optimizing spending across online and offline channels for maximum ROI.
Real-time personalized webpage experiences: Tommy Hilfiger leverages customer data to deliver hyper-personalized webpage experiences to its customers. For example, when a customer visits the website, they are prominently displayed products from their past viewed/wishlisted categories to optimize engagement. Product recommendations are also dynamically updated based on their real-time behavior like adding products to cart. This has increased conversion rates on the website significantly.
Location-based and contextual marketing: It analyzes location check-ins of customers on its app to identify high engagement areas. It then promotes relevant offers and campaigns to customers visiting such preferred locations. For example, discounts on footwear if customers are detected at a hobby store. Contextual triggers like weather, events, and seasonality are also integrated to further boost messaging relevance.
Inventory and demand forecasting: Tommy Hilfiger uses its rich historical sales data combined with external demand drivers to forecast demand and sales volumes for individual SKUs with a high degree of accuracy. Using these fine-grained demand forecasts, it optimally plans production runs and inventory levels to reduce markdown risk and ensure adequate stock levels. This has enhanced operational efficiency.
Promotions and pricing optimization: Data analytics enables Tommy Hilfiger to test and learn which combination of products, offers, campaigns, and prices are most effective at stimulating demand and maximizing revenues/profits for the company as well as value for customers. For example, A/B testing of home page designs or discount levels. It then routes the top performing strategies to full rollout.
Performance measurement and optimization: At every step, Tommy Hilfiger measures key metrics like viewership, engagement, conversion, repeat rates, NPS etc. to evaluate strategy effectiveness. It uses these data-driven insights to continually enhance its algorithms, models and approach over time – establishing a virtuous cycle of continuous performance improvement.
Tommy Hilfiger has transformed into a fully digital-driven business by taking extensive advantage of data analytics across the customer lifecycle right from engagement and personalization to predictive strategy optimization. This has enabled memorable customer experiences driving brand love and loyalty, fueling the company’s consistent growth. Data-led decision making is now at the core of Tommy Hilfiger’s entire operations globally.