Like-for-Like comparisons are the backbone of honest retail analytics, and most tools still get them wrong. Building a Like-for-Like solution in Power BI using a Semantic model shows exactly why this matters, and why an AI-native approach can do it better.
The problem is deceptively simple. When you compare store performance year over year, you need to exclude stores that opened or closed mid-period. A traditional spreadsheet forces you to manually flag these stores, build separate lookup tables, and hope you didn't miss a transition date. The Semantic model addresses this by treating time and store attributes as structured relationships, not flat columns. It lets the data model itself enforce the logic: only stores active in both periods get compared. That's not a workflow improvement; it's a fundamental shift in how the question gets asked.
What this means for you is that the friction of maintaining L4L reporting disappears. Instead of spending hours each month verifying which stores qualify, you define the rule once. The model handles the rest. And because the logic lives in the data layer rather than in formulas scattered across sheets, it scales across hundreds of stores without breaking. For analysts who have been patching together workarounds in Excel or standard BI tools, this is the difference between managing data and being managed by it.
The real insight here is that L4L isn't a niche requirement, it's a universal pattern. Every retailer, every multi-location business, every organization that tracks performance over time faces this same structural challenge. The Semantic model approach doesn't just solve it for stores; it solves it for any entity that needs fair, period-over-period comparison. That's the promise of an AI-native spreadsheet: not automating the old way of working, but rethinking the logic so the comparison is accurate by design, not by manual effort. The next step is to ensure your tool can ingest that model and let you ask the question in plain language. That's where the real productivity gain lives.