Where Should the Next Wegmans Open? 7 Checks for a Bayesian Site-Selection Forecast
A new-store forecast should separate observed evidence from assumptions before a retail real estate team commits build-out and lease capital or accepts the risk that a new location will divert demand from nearby stores.
The historical grocery example behind this guide used the open-source PyMC package to combine road access, local demographics, analyst input, and probabilistic sales estimates. At the time described, the chain had operated for over 100 years, had 100+ stores across the East Coast, and opened at most 2-3 new locations per year. Those figures describe the original planning context, not current operating claims or delivery benchmarks.
Use these seven checks to interrogate any comparable site forecast. Each check identifies an input, modeling choice, or judgment that a buyer should be able to inspect.
1. Define the trade area by travel time
A simple radius treats every direction as equally accessible. A road-network catchment instead reflects the routes people can use, including street layout and physical barriers. Travel-time isochrones estimate how far a customer can travel within a chosen interval, which is why they matter for catchment analysis at retail sites (TravelTime).
The team constructed a driving graph around each store from a roughly 20-mile buffer. Road classifications supplied edge speeds and travel-time values. A shortest-path calculation produced nested catchments from 2 minutes out to 24 minutes. When catchments overlapped, the location with the shorter trip received the contested area. A buyer should ask whether the network data, travel assumptions, and overlap rule match local shopping behavior.
2. Allocate demographics within partial boundaries
Census geography and a store catchment rarely share the same edges. Weighting a partially included block group only by land area can misrepresent where households actually sit, especially when settlement is concentrated in one corner.
The example addressed that mismatch with a quadkey tile grid and geocoded delivery-point density. The density inside each clipped block group determined how much of its population and household data contributed to the store profile. The buyer's check is straightforward: confirm that demographic allocation follows inhabited places rather than empty acreage.
3. How are store and ecommerce demand modeled separately?
The historical model estimated in-store and ecommerce sales through two distinct channels while sampling them together, so total demand carried uncertainty from both outcomes. It also let the model constrain coefficient direction where domain knowledge justified it and incorporate an analyst's ordered visibility assessment without pretending that judgment was a precise physical measurement.
Both sales outcomes used the trailing 52 weeks of data in the source setup. That was a historical modeling choice intended to reduce seasonal distortion, not a universal prescription. A buyer should inspect whether channel definitions, averaging windows, and judgment variables fit the decision now being made.
4. How is each store tested as a true holdout?
With roughly 100 stores in the fitting data, the example used full-refit leave-one-out validation. One store was removed, the model was fitted again, and the omitted store became the prediction target. This checks whether the approach generalizes beyond the observations used for fitting.
Each store contributed two observed outcomes, one per sales channel. The evaluation combined the held-out evidence from both. That prevents a strong result for one channel from concealing a weak result for the other. Buyers should ask to see performance across sites and channels, not only an aggregate score.
5. How is uncertainty preserved when combining models?
The example compared several model variants and assigned weights according to predictive fit. It then combined posterior samples using those weights rather than blending only the central forecasts. Keeping the samples preserves distribution shape and supports probability questions, such as whether sales clear a planning threshold.
This matters because a single expected value can hide asymmetric or multi-peaked risk. The useful buyer artifact is the range of plausible outcomes and the assumptions behind its shape, not merely the headline estimate.
6. Recalculate neighboring-store inputs
Adding a proposed site changes the territory assigned to nearby stores. The historical workflow first partitioned the map without the candidate and then partitioned it again with the candidate included. Demographic covariates were aggregated for both versions. Applying paired posterior samples to those two input sets produced a distribution of estimated sales displacement for each affected store and channel.
The sequence is important: proposed site, revised trade areas, recomputed covariates, updated sales forecast, then estimated displacement. A buyer can trace that chain to identify where measured evidence ends and analyst judgment begins.
7. Keep the analyst in the decision loop
An analyst supplied a candidate address, square footage, parking type, visibility score, and operating zone. The interface returned expected weekly sales with an uncertainty interval, affected-store estimates by channel, and a map of the revised trade areas.
Those outputs informed a broader decision that still included market visits, competitive intelligence, and real estate judgment. The interface is useful when it exposes assumptions and uncertainty. It is dangerous when polished output encourages the team to treat the forecast as an automatic site decision.
The question that remains after the shortlist
These seven checks can improve confidence in how a site forecast was assembled. They do not establish which concept, assortment, or message will change behavior among shoppers in the selected trade area.
That is a separate causal decision. Once spatial analysis has narrowed the candidates, Subconscious can run a controlled experiment comparing defined actions for the shopper population in question. The causal test should retain the same decision and population if the team later moves to real-human validation. This handoff adds behavioral evidence without recasting the forecast as proof of market performance. Read more about the research approach and its application to CPG decisions.
Keep the boundary visible
Subconscious does not replace spatial demographic analysis, travel-time network modeling, real estate judgment, market visits, or competitive research. It also does not produce the neighboring-store displacement estimates or automated location forecasts described here. Those methods estimate where a store may work and how it may affect the network. A controlled causal experiment tests which defined action changes a shopper decision.
If the unresolved choice concerns the concept, assortment, or message rather than the physical site, scope that behavioral test against the target population.