What is an explainable site selection score?

An explainable site selection score is a location score you can trace back to named components, documented data sources, and visible weights. You see how much each factor (reach, demand, competition, accessibility) added or subtracted, along with the data behind each figure and the date it was current. The total is the sum of parts you can inspect.

There is a working test for it. Ask the tool why a site scored what it did, and an explainable score gives you a real answer rather than a restated total. The model underneath can be as sophisticated as you like; if it cannot answer that question, the score does not count as explainable.

A black-box score hands you a number and asks for trust in the model that produced it. That holds up until you are sitting in a real estate or investment committee and someone asks why this site beat the one across the street. Surveys of executives consistently find that many flag explainability as a barrier to trusting AI, and a score nobody can decompose is the version of that problem you feel in the room. It stalls easily, because any single skeptic can park it.

A glass-box score is built to be challenged. Because the weights and contributions are visible, the committee argues with the model rather than the analyst. Someone who disagrees with a weight can change it and watch the score move. Adoption tends to run faster for this kind of scoring, and the reason is defensibility more than accuracy.

• The components are named up front. Geod scores on Reach, Demand, Competition, and Accessibility, not an undisclosed feature set.
• The weights are visible. Geod sets Reach at 30, Demand at 30, Competition at 25, and Accessibility at 15 before any site is scored, so the mix cannot be tuned later to flatter a favored location.
• Each component reports its own contribution: the points it added or subtracted for this specific site, so a low score has a named cause instead of a shrug.
• Every figure carries a source and a vintage date, because a number with no date cannot be audited.
• Confidence is stated, flagging thin or stale inputs rather than folding them into a clean-looking total.

What makes a score usable in committee is the exported brief more than the live dashboard. An explainable brief opens with the headline score, then breaks it into components and their contributions. It shows the trade area with the demand and competition that sit inside it, names the existing stores most exposed to cannibalization, and lists every data source next to its snapshot date.

Geod exports this as a committee-ready PDF, so the reasoning travels with the number instead of living only with the person who ran it. Anyone who reads the brief weeks later can follow how the score was reached.

SiteZeus offers letter-grade scorecards alongside an AI layer that explains model output after scoring. Buxton reports results as an index, where a score above 100 reads as performance above the benchmark average. SlantCRE pulls Esri, Placer, and Google data into editable reports. Geod takes the glass-box route: a transparent weighted score rather than a black box with an explanation bolted on afterward.

A SHAP-style explanation layer can make a complex model readable after the fact, and that is a legitimate second path. Geod differs in that the methodology is simple and visible from the start, which is easier to defend in front of capital approvers. The claim Geod makes is about adoptability rather than raw accuracy.

• "Can you show each component's contribution to a single site's score, not just the total?"
• "Are the weights visible and adjustable, and are they set before a site is scored?"
• "Does every figure carry a source and a vintage date?"
• "Can I export a committee-ready brief, or only a screenshot of a dashboard?"
• "If the model is AI-driven, is the explanation built into the method or added afterward?"

Black-box models are not worthless. A well-validated machine-learning forecast trained on a chain's own sales history can pick up patterns that a four-component score will miss, and for a large operator with the data and the internal trust to back it, the added accuracy can be worth a great deal.

Running both is usually the better call. An explainable score frames the decision and survives the committee, while a black-box forecast serves as a second opinion you reconcile against it. The forecast informs the call without becoming a verdict that nobody is allowed to question.