Building a Modern eCommerce Analytics Stack | Webinar Recap

TL;DR: Most brands have invested heavily in dashboards, reporting tools, and data warehouses. Yet diagnostic speed remains slow, automation minimal, and AI underutilized. Rather than more data, brands need a redesigned analytics stack that enables proactive anomaly detection, connected cross-channel insights, and automated recommendations embedded directly into workflows.

2 minute read

Our benchmarking across 103 brand-side respondents revealed very few brands are leveraging what’s possible today from an eCommerce analytics perspective:

• 64% require a week-plus to know why performance changed.
• 87% rely on manual or ad hoc analysis to identify root cause solutions.
• 93% of analytics effort is spent on reporting, reconciliation, and reactive explanation.
• 95% don’t allow AI to materially influence decisions.

In other words, most teams are still operating in a descriptive, manual diagnostic model, rather than building systems that surface prioritized action in near real time.

We hosted Sreenath Reddy, founder of Intentwise, to share what good looks like from an eCommerce analytics capabilities now that first-party and retailer data is more accessible than ever, and AI capabilities offer completely new ways of working.

The session included what the ideal end state for brands looks like:

• Understanding of cross-channel halo effects
• Easy mechanisms to answer ad hoc questions
• Proactive identification of anomalies and root causes
• Accessible answers to strategic questions like customer lifetime value and incrementality

The session showed where the goal posts are today for leaders, why so few of brands are there, and where to get started.