Join us as we dive into the journey of an American multinational retail corporation, uncovering the secrets behind measuring long-term incremental sales revenue after the installation of their groundbreaking App. With a focus on identifying high-value media sources and creating a framework for causal analysis, this retail giant sought to harness the power of data to drive growth. Leveraging cutting-edge causal inference techniques and big data pipelines, a synthetic control group was constructed to establish statistical equivalence. Through comprehensive analysis and bootstrapping tests, incremental revenue was calculated for different time intervals. Witness the transformative impact of this analysis, including the estimation of billions of dollars in incremental revenue and the identification of high-value channels for future media strategies. Experience the power of data-driven decision-making and unlock the potential for unprecedented growth in the retail landscape.
One challenge was determining the product features that customers liked or didn't like based on customer online reviews. However, research has shown that customer reviews contain a significant amount of valuable information, with 97% of customer needs identified through these reviews compared to traditional surveys conducted over a 30-year period (Timoshenko and Hauser, 2019). To address this challenge, the client aimed to develop key product ranking criteria for their landing pages and customize the content for product detail pages by leveraging the key product benefits, pros, and cons identified from customer reviews. This approach would enable us to better understand customer preferences and tailor product offerings accordingly, enhancing the overall customer experience.
We implemented NLP models to analyze customer reviews and gain valuable insights. The team used techniques such as summarization, topic identification, attribute extraction through POS tagging, sentiment analysis, and meta-attribute clustering. To ensure accuracy, an app was developed for human validation, and feedback was incorporated into automated model training. This comprehensive solution empowered us to make data-driven decisions based on customer feedback.
We successfully deployed their learnings from customer reviews, leading to improved site experience and search engine optimization (SEO). These improvements resulted in a significant revenue increase of $8.3 million. Moreover, we attracted an additional 2.2 million high-value sessions, resulting in 81,000 new customers and 588,000 increased orders. We are now utilizing customer journey and path analysis to inform design decisions for the website and app, further enhancing the overall customer experience.