The retail organization faced challenges with data trust, siloed teams, and reactive analytics. To address these issues, they shifted their perspective to treating data as a product, which meant defining clear ownership, user requirements, and performance metrics. This shift led to expanding analytics teams and embedding analysts within operational pods focused on different customer experience areas.
They rebuilt their intelligence stack by unifying transactional data, behavioral analytics, and loyalty interactions under a common taxonomy aligned by customer journey intent rather than channels. Platform tools were treated as data pipes rather than end solutions, emphasizing signal quality and governance. The SIGNAL framework was introduced to guide transformation by standardizing taxonomy, integrating data, governing ownership, normalizing signals, aligning teams for activation, and learning through closed-loop testing.
This cultural and infrastructural change resulted in a 7%-8% lift in digital sales influenced by personalization, improved A/B testing velocity, shortened insight-to-activation cycles, and stronger collaboration across departments. Looking forward, the retail intelligence foundation supports emerging AI-powered commerce applications, reinforcing the importance of trustworthy, structured data as a strategic asset for future innovation and customer experience enhancements.
👉 Pročitaj original: CIO Magazine
