Krunchbox aggregates weekly POS data across retailers, regions, and categories.

Krunchbox had evolved organically from its early days with a single tenant. New reports were added incrementally to address immediate needs, without a broader structural framework.

As additional tenants came on board, the number of reports grew rapidly. Over time, this led to two clear issues:

What initially provided flexibility gradually introduced complexity, making it harder for brand tenants to quickly interpret data and focus on what mattered most.

Every tenant question seemed to result in a new report.

Tenant requests resulted in overlapping reports with similar intent.
Finding simple answers often required opening multiple reports.

The system had data in abundance—but no clear starting point.

Filters, tables, and metrics were all visible by default.
Instead of guiding users, the interface asked them to figure out where to begin.

Before redesigning anything, I spoke directly with tenants to understand their real goals. Rather than asking for features, I focused on daily usage and decision-making needs.

Across conversations, the same questions kept coming up:

Most tenant needs were common and predictable.
One-off requirements existed—but shouldn’t define the system.

The solution focused on:

Reports were reorganized into two layers:

To reduce overload:

A new dashboard became the primary entry point.

Each section answered one key question around sales, stock, or performance.

AI translated complex data into clear, plain-language insights.
It surfaced risks, trends, and opportunities—without replacing reports.

Instead of raw metrics, tenants saw:

Krunchbox evolved into a more focused and scalable platform: