Since its founding in 2013 as a flower delivery service, Bloom & Wild has expanded its offerings and regions, leveraging rich data and analytics to understand and optimize the customer journey.
- Bloom & Wild operates across UK, Netherlands, and France with multiple digital touchpoints.
- Contentsquare provides core behavioral analytics, enhanced with AI summaries and session replays.
- Data integration via warehouse and AI querying enables comprehensive downstream business insights.
What happened
Bloom & Wild has evolved into a multi-category gifting platform with presence in the UK, Netherlands, and France. Since 2021, Lottie Linter has led product analytics, harnessing extensive data from apps, websites, and customer behaviors to refine the user experience.
The company uses Contentsquare's analytics technology extensively, allowing teams across disciplines to analyze session funnels, replays, and customer behaviors. Recently, AI-powered summaries of session replays have become crucial for distilling large volumes of behavioral data into actionable insights.
Why it matters
The integration of behavioral data with broader business metrics enables Bloom & Wild to make data-driven decisions that enhance customer satisfaction and operational efficiency. This multi-layered approach supports teams ranging from product designers to commercial and leadership executives.
Bloom & Wild’s use of Contentsquare’s Data Connect facilitates bringing diverse datasets—including order, stock, and engagement data—into a unified warehouse environment. This comprehensive view allows for advanced modelling, testing, and AI-driven querying, creating a seamless analytics pipeline that empowers decision-makers across the organization.
What to watch next
Bloom & Wild is currently expanding its data integration capabilities with new tools such as Statsig for A/B testing and leveraging Tableau’s Multi-Context Protocol to enable AI-driven exploration of analytics dashboards. These initiatives signal a move toward more scalable and AI-enhanced data operations.
Further refinement of event tracking and metadata management, as emphasized by Linter, will be critical. Ensuring that metrics and event definitions are clearly named and documented will help non-analyst teams engage with the data more effectively, driving wider adoption and better-informed customer experience improvements.