Anthropic API – Claude Sonnet 4.6
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DeepSeek API – DeepSeek V4 Pro
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Step 1
Site & Real Estate
Lease economics, site fitness & location logic – qualify the site before modeling the case
Site identity
Site name / reference?A short reference for this specific site, used to distinguish it if more than one location is under evaluation for the same expansion decision.
Address?The street address of the site under evaluation.
Lease economics
Ownership structure?Whether the site is leased on the open market, leased from a related party on favorable terms (e.g. a group-owned property company, the same structure IKEA stores often have with Ingka Centres), or fully owned. This changes how the lease checks below should be read – a related-party or owned site carries materially lower lease risk even where the headline rent figure looks similar to a market lease.
Annual rent?Total annual rent for the site, in local currency. This is the numerator for the rent-to-revenue check, which is the single most diagnostic number in this section – it determines whether the site can ever be profitable regardless of how well the format is executed. Leave at 0 if owned outright.
Projected annual revenue?The Year-1 sales goal for a unit on this site. If IR Key Figures has already been completed for this site, this should match the sales goal entered there – it is repeated here so the rent-to-revenue check can run before IR is fully built out.
Lease term (years)?The length of the initial lease commitment, in years.
Renewal option?Whether the lease includes a defined right to renew. A lease with no renewal option leaves the business unable to protect a site that proves successful.
Exit clause?Whether the lease includes a defined way out before term expiry. A lease with no exit clause and no renewal option locks the business into a bad site with no way to correct course.
Escalation structure?How rent increases over the lease term. Uncapped or market-reset escalation introduces a structural cost risk that compounds over the life of the lease.
Site fitness
Building condition?The technical condition of the building as it stands today, before fit-out. Structural concerns at this stage are a different order of risk than cosmetic remediation.
Estimated remediation cost?Estimated cost to bring the building to a workable condition, in local currency. Enter 0 if turnkey.
Loading / back-of-house adequacy?Whether the site's loading access, storage, and back-of-house space can support the planned operating model – including omni-channel volume such as click-and-collect and delivery. A site with inadequate loading access for the planned format is a structural failure, not a risk to monitor.
Permitting status?Where required permits, licenses, and approvals currently stand. Permitting delays are one of the most common hidden timeline killers in an expansion project, and they are rarely visible until they have already cost time.
Construction / fit-out budget variance?The latest quoted or actual construction cost, as a percentage variance against the original construction budget. Positive values mean the build is running over budget.
Location logic
Visibility / accessibility score?1 = poor visibility and difficult access. 10 = excellent street visibility and easy access by the dominant transport mode for this catchment. Sits alongside the transport score already captured in IR Key Figures.
Distance to comparable / competing unit (km)?Distance to the nearest unit of the same brand or format, or the nearest significant direct competitor. Used as a cannibalization-adjacent check alongside the existing nearest-store distance captured in IR Key Figures.
Operating-model fit?Whether the physical site can actually run the planned operating model – not whether it is a generally good site. A small-format click-and-collect operation sited somewhere with no loading access for delivery vans does not support the model regardless of how strong the location otherwise is. This is treated as a standalone check, not averaged against the other site variables.
⚡ Site Readiness Check
This is a factual, rule-based check layer – not an AI narrative and not a phase-tracked score. It exists to qualify the site before the investment case is modeled, and a failed check here caps the overall BER score the same way a failed Investment Reality check does.
Step 2
IR Key Figures
Investment Request – Market Area & Business Case Validation
PMA – Primary Market Area
Country?The country in which the unit will operate. Determines regulatory environment, consumer spending benchmarks, and the macroeconomic context for this investment.
City / Metro area?The city or metropolitan area. For units in large cities, this is the metro-level anchor – population and macro benchmarks are drawn at this level. Specify the neighbourhood below for the precise catchment fetch.
Neighbourhood / District?The specific neighbourhood, district, or submarket where the unit will be located. This is the primary catchment anchor for the PMA auto-fill – the more precise this is, the more accurate the data fetch. Examples: Santa Fe, Polanco, Shoreditch, Le Marais, Midtown.
Postal / ZIP code?The postal or ZIP code of the site or planned location. Used to refine the catchment fetch to the correct sub-area, particularly important in large cities where neighbourhoods differ significantly in density, income, and demographics.
City / metro population?Total population of the city or metro area. This is the outer boundary of the potential customer universe. On its own it is meaningless – what matters is the realistic catchment population within the store's actual draw radius.
Catchment radius (km)?The realistic radius in kilometres from which this store will draw customers. In dense urban environments this is typically 1–3km. Overstating the catchment radius is one of the most common ways visitation goals get inflated in investment cases.
Catchment population (est.)?Estimated population living within the catchment radius. This is the denominator for the visitation penetration rate check. A common investment case error is using the full city population as the catchment – the actual draw for a new small-format unit is far smaller, which is why visit goals built on city-wide populations tend to produce penetration rates that are structurally impossible to achieve.
Trade area character?The character of the immediate trade area. Dense urban cores produce higher visit frequency but smaller baskets. Suburban formats produce larger baskets but lower frequency. The operational model, range, and staffing must be calibrated to this reality.
Public transport access (1–10)?Public transport access quality on a 1–10 scale. Urban small-format stores depend on transit access – a store customers cannot reach by foot, cycle, or public transport in under 15 minutes will not achieve the visit frequency the format requires.
Car share of visits (%)?Estimated percentage of customer visits arriving by car. A high public transport score is only meaningful if visitors actually use it. A store with a PT score of 9/10 in a catchment where 80% of visitors arrive by car has a fundamentally different access profile – parking capacity, drive-time radius, and car-park cost directly constrain visitation frequency. Enter the best available estimate from the investment case, traffic studies, or comparable store data. If unknown, leave blank.
Nearest store (km)?Distance in kilometres to the nearest full-format store in the same brand. Below 10km, cannibalization is a material risk – customers may redistribute rather than increase total spend, affecting the net new revenue contribution of this unit.
Key local competitors (names)?Key local competitors by name. Relevant for pricing strategy, market positioning, and the local capture plan. A store opening adjacent to a well-established category leader has a different commercial challenge than one in underserved territory.
PMA – Catchment Economics
Disposable income index?Catchment disposable income relative to the national average. Enter as an index where 100 = national average. A catchment at 85 has 15% below-average purchasing power; one at 120 is 20% above. Directly affects average ticket credibility – an investment case built on a high ticket assumption in a below-average income catchment requires explicit justification.
Home ownership rate (%)?Percentage of households in the catchment that own rather than rent their home. Tenure mix affects category demand, basket size, and visit mission. A catchment with a high renter share may support more portable, frequent-purchase, or lower-commitment ranges than one dominated by owner-occupiers.
Active consumer cohort 25–65 (%)?Percentage of catchment population aged 25–65 – the broadest active consumer demographic for most retail formats. The 25–45 sub-segment drives higher visit frequency and impulse purchasing; the 45–65 sub-segment drives higher average transaction value and considered purchases. A combined cohort above 50% indicates a structurally strong demand base. A catchment dominated by under-25 (lower disposable income) or over-65 (lower mobility, lower basket frequency) changes both the category mix and the revenue model assumptions.
Avg. household size?Average number of people per household in the catchment. Larger households often create more frequent replenishment needs and higher basket potential. Single-person households – common in dense urban cores – tend toward smaller, more frequent, convenience-led purchases. This affects range composition, visit frequency, and ticket assumptions.
PMA – Retail Environment & Site
Footfall / day?Estimated or counted daily pedestrian footfall at or immediately adjacent to the proposed store location. This is the most important demand-side input missing from most investment cases – transit access quality does not tell you how many people actually pass the door. A store on a 3,000 footfall street has a structurally different visit conversion opportunity than one on a 15,000 footfall street. Enter best available estimate from footfall data, municipality counts, or mall operator data.
Walk catchment pop. (10 min)?Population living within a 10-minute walk of the store entrance. For small-format retail, on-foot access is the primary driver of visit frequency – car-dependent locations produce larger baskets but lower visit rates. This figure is distinct from the broader catchment radius population and is the more relevant denominator for penetration rate in dense urban formats.
Competing retail sqm?Total retail floor area (sqm) operated by named competitors in the selected industry within the catchment. This converts the competitor name list into a supply-side pressure number. A catchment with 8,000 sqm of directly competing retail is structurally more contested than one with 1,500 sqm – and the conversion rate and basket assumptions must reflect that difference.
Co-tenancy / anchor?Distance in km to the nearest major competitor in the same category. Proximity to a dominant competitor affects catchment capture rate and marketing spend required to drive awareness. A location adjacent to a well-established player faces a higher conversion challenge in year one.
CMP – Customer Meeting Point
Store name?The commercial name of the store. Used to personalise the BER report and any external market research the AI reality check retrieves.
Store number / unit ID?Internal store or unit identifier. Used for cross-referencing with country organisation data and for tracking this unit's BER assessment history over time.
Retail industry?Primary retail industry for this unit. BER uses this to interpret competitors, store productivity, basket assumptions, footfall quality, and category overlap without assuming any one category model.
Format type?The store format type. Small-store, medium, and large formats have different footprints, range structures, cost bases, and operational models. BER benchmarks and scoring are calibrated to format type.
Store size (sqm)?Total gross retail area in square metres. Base for revenue-per-sqm benchmarking. Revenue productivity is currency-sensitive, so cross-market comparison should use FX/PPP-normalized comparable stores rather than a raw numeric threshold copied from another market.
Planned opening date?The planned opening date. This anchors the T–180 to T+90 timeline discipline. Every BER pillar variable is assessed relative to how many days remain before opening.
Planned annual worked hours?Total planned worked hours for year one. A well-optimised small-format unit on ~6,000sqm operates at approximately 100,000–110,000 hours annually. A new unit should target at or below that until the commercial model is proven. SPWH must be validated against the local break-even revenue per worked hour and, where available, FX/PPP-normalized comparable stores. The internal benchmark SPWH is a reference point only, not a portable threshold. Both overstaffing and understaffing in hours are risks.
Year-1 sales goal (local currency)?Year-1 total revenue target in local currency. This is the single most important number in the investment case – and the one most frequently set using big-box benchmarks rather than small-format reality. It is cross-checked against visits × conversion × ticket in the reality check engine.
Break-even revenue (local currency)?The minimum annual revenue at which this unit breaks even. The gap between this and the sales goal is the break-even buffer – the margin of safety if the store underperforms. A thin buffer combined with optimistic visitation assumptions is one of the most common structural vulnerabilities in small-format investment cases.
Investment budget (local currency)?Total capital investment in local currency. Used to calculate the payback period and to contextualise the risk of underperformance. An investment case with a thin break-even buffer and a high capital commitment is structurally fragile.
Payback period target (years)?Target number of years to recover the total investment. For small-format units, 7–10 years is a typical expectation. A payback target below 5 years requires a very aggressive revenue assumption – flag this if the sales goal also looks optimistic.
Profitable by (year from opening)?Year from opening by which the store is expected to reach operating profitability. Distinct from payback period – a store can be operationally profitable in year 2 while still recovering capital in year 8.
Year-1 visitation goal?Year-1 total customer visit target. The variable most likely to be wrong in the investment case. Must be built bottom-up from catchment population and realistic penetration rates – not scaled from a full-format store. Investment cases built on optimistic visit projections routinely produce year-one results 30–60% below target.
Average ticket (local currency)?Average transaction value per visit in local currency. Combined with conversion rate and visit volume, this drives the revenue model. Ticket values in small-format stores are typically lower than full-format – the range is narrower and the basket is more purposeful.
Conversion rate target (%)?Expected percentage of visitors who make a purchase. In small-format retail, conversion is typically lower than full-format – the range is significantly narrower, visits are shorter, more impulse-based, and usually made on foot rather than by car. Customers arrive with less intent to buy a specific item and less time to browse. If the investment case assumes conversion rates comparable to a big-box store, the revenue model is built on a false premise. The circular risk is the reverse: using a high conversion assumption to rescue an already optimistic visit goal.
Customer retention target (%)?Target percentage of year-1 customers who return in year 2. Retention is the commercial flywheel of a small-format store – repeat visit frequency is what makes the unit economically viable at lower basket values.
Currency?The currency used for all financial figures. Required for the AI reality check to contextualise numbers against local market benchmarks and present the BER report correctly.
Omni channel
Attribution scope?Defines what "online sales" means for this unit. Store-fulfilled: only orders this store picked, packed, or handled – the number that should drive this unit's P&L and staffing model. Catchment digital demand: all online revenue attributed to customers in this store's catchment regardless of fulfillment node. These are two materially different numbers. Conflating them makes break-even math wrong in a direction invisible until the store is open.
Online sales ratio (%)?Percentage of total Year-1 sales expected to complete online rather than at the physical till. This number resets what success looks like for the physical unit. A store can hit its catchment-derived demand target and still miss its physical sales goal – not because it failed, but because a predictable share of demand completes digitally. Young, urban, transit-dependent catchments will have structurally higher online completion regardless of execution quality.
Online visitation (annual sessions)?Annual digital visits attributable to this store's online presence – store-specific site traffic, app sessions with this location selected, or the nearest available proxy. This is footfall's digital twin. Without it, online conversion is uninterpretable: a weak online sales figure could reflect a conversion problem or simply a demand-awareness problem – a completely different fix.
Online conversion rate (%)?Share of digital sessions that complete a transaction. A low number is not automatically a problem – online traffic conflates genuine purchase intent with research and price-checking, behavior with no equivalent in footfall. Do not benchmark online conversion against in-store conversion; they are not measuring the same kind of visit.
Average online ticket (local currency)?Average value per digital transaction. The key signal is its divergence from the in-store average ticket: a materially higher online ticket suggests customers use the store to decide, then complete larger purchases at home. A materially lower one suggests online handles top-up and replenishment while bigger decisions remain on the floor. Either pattern has direct implications for the range plan and staffing model.
Click-and-collect share (%)?Share of online orders picked up in this store rather than shipped. Every click-and-collect order generates a real store visit the footfall-based visitation goal never counted, and it generates back-of-house pick, pack, and handover labor a sales-floor staffing model does not budget for. A high share against a headcount plan built around selling hours is a quiet way to open understaffed.
Ship-from-store share (%)?Share of online orders credited to this store that are actually fulfilled from this store's own stock, not a central warehouse. Two stores can show identical online sales ratios while one runs real fulfillment operations and the other receives an attribution credit for demand a warehouse elsewhere is servicing. Without this, the break-even cost-to-serve assumption can be materially wrong without anyone being able to see why.
Web-to-store rate / ROPO (%)?Share of in-store transactions preceded by online research on this store or its products. Evidence that the store's job is shifting from discovery-and-persuasion to confirming a decision already made on a screen. This changes what competence and leadership should be preparing co-workers to do – if most customers arrive pre-decided, the role is servicing intent rather than building it.
Store-influenced online rate / showrooming (%)?Share of online purchases preceded by a visit to this physical store. The store does the expensive, space- and labor-intensive part – discovery, comparison, decision – and the transaction completes elsewhere. If this happens on the company's own site and is tracked, the store is still serving the business. If it is not tracked or attributed, the investment case permanently understates what this unit is worth, in the same way legacy retailers funded stores on walk-in conversion math while those stores did unpaid showroom work for online transactions.
Cross-channel return rate (%)?Share of purchases returned through a different channel from which they were bought. Customers experience the brand as one entity and expect to return anything anywhere. That expectation places a real physical workload on the store unrelated to its own sales volume. A staffing model built purely around the unit's own transaction count will be structurally short on this task even if every other number in the plan is correct.
⚡ Investment Reality Check
EV Evidence Harvester
NO PACK
Step 3
International & Local Benchmarks
Comparable unit baseline
Normalization
Reference currency?Currency used after normalization. Comparable stores should provide fx_to_reference and ppp_factor so sales, ticket, and labor productivity are converted onto one baseline.
Target FX to reference?How many units of the REFERENCE currency one unit of the TARGET STORE'S currency buys. Direction check: this number should make your sales figure BIGGER when the reference currency is the weaker one, SMALLER when it's the stronger one. Example: target store in CAD, reference currency NOK – 1 CAD ≈ 7 NOK, so enter 7, not 0.14 (0.14 is the inverse, NOK into CAD, and will shrink your figures by ~50x). If unsure, use Fetch rate below rather than typing it by hand.
Target PPP factor?Optional purchasing-power or price-level adjustment for the target market. This is a multiplier CENTERED ON 1.0, not an index out of 100 or 130. Example: a market with prices 30% above the reference market is 1.30, not 130 – entering 130 will shrink your figures by roughly 100x. Leave as 1 unless you have a reliable PPP or price-index source; FX-only normalization is usually sufficient for this BER comparison.
Benchmark confidence floor?Minimum confidence score accepted for comparable stores. Stores below the floor are excluded from the baseline.
Comparable Stores
BM Benchmark Baseline
NO BASELINE
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