Chain Store Network Planner
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About this skill
Problem
Chain-store site selection often relies on gut feeling: candidate sites are hard to quantify, while rent, competition, foot traffic, and payback periods sit in separate spreadsheets. Multi-store planning can also miss cannibalization and coverage gaps. This skill turns those questions into a repeatable analysis workflow.
How It Works
It routes user intent through modules. A six-dimension model scores district value, location value, property conditions, and rent-to-sales ratio. Profit logic estimates rent burden and payback period. Competitive analysis covers substitutes, complementary formats, and cannibalization. Prediction uses analogy or regression to estimate daily foot traffic and revenue. The geo layer supports AMap, Baidu, Tencent, or OSM data sources and can generate site maps, distance matrices, battle maps, population coverage, and gap scans. Default outputs include PNG, Excel, or PPT, with optional heatmaps, VI checks, image compression, weather factors, and PDF reports.
Boundaries
It is best when candidate coordinates, store data, or POI information are available, and it does not replace field visits. AI image generation, weather, and map APIs depend on configured keys. With fewer than 30 stores, analogy is usually more practical; regression needs richer store feature data.
Use Cases
- Compare candidate store sites in a trade area using a six-dimension score and location map.
- Scan city coverage gaps for an annual opening plan and produce a battle map plus shortlist.
- Check whether rent is affordable by estimating revenue, break-even, and payback period.
- Review supervisor photos for VI compliance, compress images, and compile an audit report.
Best For
- Chain expansion leads who need explainable scoring for city entry, density, and closure.
- Regional operations managers who use maps and competitor data to spot gaps and cannibalization.
- Store supervisors who check VI consistency, organize patrol photos, and output standard reports.
- Data analysts who fit analogy or regression models from store CSV datasets.
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