Challenge

Grove Collaborative faced several challenges with their home-grown product discovery solution. This system required creative workarounds to rank products attractively sitewide, leading to significant manual upkeep of ranking attributes and synonyms.

Manual intervention was also needed to add page elements for better navigation when search results were unhelpful, resulting in a less optimized customer experience.

The company’s focus on carrying a highly curated selection of earth-friendly products further complicated the situation. Their legacy search engine often led customers to null results pages for common items because they searched using brand names not carried by Grove Collaborative. Basic analytics highlighted the frequency of null results, revealing the inadequacy of their existing solution. Additionally, the engineering team was burdened with maintaining an outdated system, while merchandisers struggled with ineffective tools, all during a period of expansion.

Strategy

To address their challenges, Grove Collaborative strategically transitioned from their cumbersome in-house product discovery solution to a specialized platform with advanced AI capabilities. Recognizing the inefficiency of dedicating engineering resources to their flawed system, they sought a robust product discovery platform that could integrate seamlessly with their existing tech stack and offer powerful out-of-the-box functionalities.

The new platform's user-friendly APIs allowed for quick integration and required minimal configuration, providing the merchandising team with effective tools to optimize search results without extensive manual intervention. This strategic shift not only alleviated the burden on their engineering team but also improved the overall customer experience by delivering more accurate and personalized product searches.

Method

The method used by Grove Collaborative to overcome their product discovery challenges involved adopting MACH principles and leveraging the capabilities of a product discovery platform: Constructor. They implemented a microservices-based, API-first, cloud-native SaaS, and headless architecture, ensuring a scalable and flexible solution.

The chosen platform utilized AI to provide hyper-personalized customer experiences by analyzing clickstream data and other sources like loyalty programs, enabling real-time optimization of search results. This approach facilitated continuous learning from shopper behaviors, enhancing the relevance of product searches. The platform's user-friendly APIs allowed for quick integration and minimal configuration, empowering the merchandising team to execute their strategies effectively while improving overall efficiency and customer satisfaction.