Saved Views — Filter-Aware Presets
Each preset applies both a tab and a filter-state combo. Illustrative
Save current view (command rail) would add a new chip here, named by the analyst.
Scoped Export Illustrative
Export reflects the active filter scope, not always the full GTA dataset.
mercanta-intel_GTA-full_2026-07-09.csv
Analyst Notebook — Pinned Observations
Pin any insight ( icon on a "So What" card, any tab) to add it here. Illustrative
Pinned Observations (3)
Barry's positioning: High attention (boutique buzz), small footprint. Growth vector is horizontal scale, not vertical deepening. Adjacent FSAs (M4A, M4B) are high-priority for next unit.
Validation anomaly: Downtown core (M5A) shows low composite but high busyness. Needs ground verification. → M5A
Validation anomaly (2): Mississauga corridor (L4W) shows high composite but low busyness — opposite direction from M5A. → L4W
+ Pin an insight from any tab to add it here
Scope Filter
Narrows every tab to the selected geography, segment, brand, and confidence scope. Illustrative — real build recomputes against the live feature store; this mockup swaps between a few canned scenarios.
Geography
Lifestyle Segment
Recorded in Active Scope below; doesn't yet move any KPI number in this mockup (only Geography does — see note above).
Archetype / Brand
Also filters the brand roster on the Competitors tab.
Opportunity Score ≥
Understand current market composition, growth potential, and where confidence is strongest.
Market Composition
* Other = unclassified format: unaffiliated / recently-opened studios not yet matched to an archetype by the POI classifier.
TAM Band
$2.1–2.8B
Consensus
Footprint Lead
Gold's
18 locations
Buzz Lead
Boutique
65% share
Confidence
2/5
M1, M3 done
FSA Coverage
142
w/ busyness
A $2.1–2.8B GTA fitness opportunity, split two ways: big-box scale (Gold's, LA Fitness — 57% of footprint) versus boutique buzz (Barry's, Orangetheory — 65% of online engagement). Highest-confidence targets are affluent urban cores and the Urban Professional segment; family suburbs are the emerging opportunity as income there catches up. The TAM band narrows further as M2/M4/M5 complete by Q3.
Competitive Positioning
Big-box dominates footprint; boutique dominates online buzz — a clean bifurcation. Full Growth Quadrant (footprint × attention share) lives on the Competitors tab, next to the brand roster it explains.
Cross-Tab Reference — Segments & Competitors at a Glance
Inline detail from other tabs, surfaced here only in Dossier mode — the deep-read view for someone about to act on this market.
From Segments
Affluent Established (92% Premium-Fit, 34 FSAs) and Urban Professional (70%, 28 FSAs) are the two segments ready for direct targeting without further validation — see Segments tab.
From Competitors
Barry's (8 locations, rising momentum) is the sharpest boutique growth vector; Gold's and LA Fitness hold 57% of footprint but flat-to-mature momentum — see Competitors tab.
From Methodology
2 of 5 dollarization vertices (M1, M3) are calibrated; the $2.1–2.8B band narrows as M2/M4/M5 complete — see Methodology tab.
TAM Band Scope
$2.1–2.8B
$700M spread reflects uncertainty across 5 methodologies; band narrows as M2, M4, M5 calibrate. This is a wider, earlier full-scope estimate — the KPI strip above shows the current bound consensus midpoint (M1+M3), a separate, tighter figure that will converge with this band as M2/M4/M5 land.
Footprint Dominance
Gold's, LA Fitness lead
18 + 14 locations respectively; 57% of branded footprint split between 2 big-box chains.
Boutique Buzz
84 boutiques capture mindshare
Collective boutique segment is "Niche & Buzzing" (high attention, small individual footprint).
Composite Confidence
94% Tier-1 coverage
Clean scoring composite relies heavily on verified open data; minimal licensed-Tier-2 leakage.
Tier-1 Coverage
256/256 FSAs
All GTA trade areas have scoring; 142 FSAs have bench-busyness signal for validation.
Executive Summary
$2.1–2.8B GTA fitness TAM; high-confidence urban opportunity.
Market shows strong fundamentals across affluent and professional segments. Big-box-boutique bifurcation creates distinct positioning lanes. Band narrows Q3 with final 3 dollarization vertices. Proceed to site selection with confidence in FSA-level composite scores.
Understand the five lifestyle segments, their demand characteristics, and confidence levels across the region.
Affluent Established
High discretionary income, established professionals. Premium spending power and brand loyalty. Strong marker coverage enables confident targeting.
Urban Professional
Young professionals in dense urban cores. Value time-efficiency and premium convenience. Digital-native, reviews and social-media driven.
Emerging Family Suburb
Young families expanding into suburbs. Moderate data coverage — segment confidence is developing as markers accumulate.
Mainstream Midmarket
Largest segment. Value-conscious, price-sensitive but willing to pay for quality. Strong coverage enables broad reach.
Value Underserved
Price-sensitive, limited discretionary income. Opportunity for accessible, community-driven formats. Growing segment with moderate marker coverage.
Segmentation Insight
Affluent & Urban Professional are high-confidence targets.
These segments command premium spending power and cluster in walkable, high-density cores. Emerging and Value segments show growth opportunity but need additional marker validation. Stage portfolio expansion around confidence levels.
Analyze brand positioning, footprint distribution, and market share dynamics across archetypes.
The Growth Quadrant below is the hero competitive-positioning artifact for this roster — footprint share (location count) plotted against attention share (online engagement).
Growth Quadrant: Footprint vs. Attention Share
Each brand positioned by location count (x-axis) and online engagement (y-axis). Quadrants reveal strategic positioning: who dominates footprint, who captures buzz, or both. Illustrative density — the 5 named chains are labeled; the surrounding cluster represents the ~24 tracked brands (41 brand-by-archetype rows) the real chart plots from footprint-share and attention-share market-share data.
Brand Roster: Footprint, AUV, & Tier
Momentum sparklines show illustrative 9-month trajectories — each brand's shape is distinct (Gold's/Barry's rising, LA Fitness flat/mature, Orangetheory declining, Boutique collective volatile) to represent real archetype-level trend divergence.
| Brand | Locations | Momentum | AUV (est.) | Archetype | Tier / Verification |
|---|---|---|---|---|---|
|
GG
Gold's Gym
|
18 | $1.2–1.8M | Big-box | Tier 1 | |
|
LA
LA Fitness / Equinox
|
14 | $2.1–2.9M | Big-box | Tier 2 Unverified | |
|
BA
Barry's Bootcamp
|
8 | $3.2–4.1M | Premium | Tier 2 Unverified | |
|
OT
Orangetheory
|
12 | $1.8–2.4M | Premium | Tier 2 Unverified | |
|
BO
Boutique (collective)
|
84 | $0.4–1.2M | Boutique | Tier 3 Scraped |
Competitive Positioning
Big-box dominates locations; boutique dominates buzz.
Market shows classic bifurcation. Gold's & LA own scale; Barry's & Orangetheory own engagement. Boutique collective has highest per-unit premium potential. Competitive opportunity exists in underserved midmarket (high volume, moderate price).
Pick a site and a format. The engine prices that specific launch — projected captured revenue, member volume against physical capacity, and exactly which incumbents lose the share — over the same segment × region board the Overview and Map tabs already read.
1 · Market Snapshot
Site Illustrative — swaps between 3 precomputed sites, not a live board recompute
Format
2 · Targeting & Offer Price & target-segment are real inputs; the rest express strategic intent
Who you want, what you're bringing, and how the P&L shape looks — combined with this scenario's real data into the fit scores in Section 3.
Presets
① Customer Target — who you're aiming at
② Offer & Price — what you're bringing, and at what price
Real inputs — these two flow into the modeled $ in Section 3, not just a heuristic
At your price
$848,392 /yr
426 members × $166/mo × 12
From your target segment
$847,347 /yr
100.0% of modeled capture — no targeting applied
③ Access & Friction — how far people will travel
④ Competitive Steal — who you expect to take share from
Who do you expect to take share from? These five weights auto-normalize to 100% as you drag — a real mechanic (arithmetic), not a real prediction; the actual "who loses share" data is the Displaced Incumbents table in Section 4.
⑤ Financial Shape — how the P&L ramps
Parked, not forgotten: a feasible-pricing-band output, lock/unlock controls per slider, a daypart/time-of-day demand lens, an explicit leakage-recapture estimator, and a fully generative offer-recommendation layer (Section 3's recommended action is rule-based, not generative). All would need either real modeling work (segment_fit config extension + a calibration anchor) or a product decision on how much of this to commit to before building it for real — see docs/SEGMENT-MODEL-PLAN.md for the phased build discipline this repo already follows.
3 · Projected Outcomes
Projected Year-1 Capture — Boutique at Erin Mills, Mississauga (L5M)
Erin Mills, Mississauga — a 905 affluent suburb.
Scenario mode
Captured Revenue
$847,347
illustrative band $660K–$1.03M
Members Captured
426
of 1,275 capacity ceiling
Capacity Utilized
33%
headroom — demand share binds first, not the box
Huff Share of Pool
Fit Scores — simple heuristics grounded in this scenario's real segment mix, price, and pool size; not the Huff engine
Target-Fit Score
—
your Customer Target sliders vs. this site's real segment mix
Price-Fit Score
—
your price (Section 2) vs. this format's modeled average
White-Space Score
—
this site's reachable pool ÷ rival count, ranked vs. the other 2 sites
Scores recompute live from the current site, format, price, and sliders above — a 100/100 on page load isn't a bug: it just means today's default price happens to equal this format's modeled average, or this site happens to rank best of the 3 for white space. Nudge a slider or switch site/format to see them move.
Who You'd Be Winning From — real, from the top displaced incumbents (Section 4)
3 of the top 3 incumbents losing share are Premium Big-Box.
Sensitivity — the bigger lever
Computing…
Try Next — rule-based, not generative
Computing…
Scenario Comparison — the 3 modeled sites at the current format (Boutique)
Constitution Principle 4 (directional, phased): per-site estimation is allowed for exploration and calibration during R&D, tracked and marked provisional — this tab. Before any figure like this reaches a customer, it must carry a real confidence band and explicit caveats (not a literature stand-in), and small-cell suppression (n<3 or one incumbent >50% of a cell) must be wired in — both are open items, not silently passed. See docs/STATE-OF-THE-BOARD-2026-07-10.md.
4 · Methodology & Provenance — the math behind the number above
How This Number Is Built — Three-Way Gate
Bounded by three independent ceilings: reachable Potential, competitive Huff share, and format capacity — whichever binds is named explicitly.
reachable from 6 origin FSAs; competed for by every rival gym those FSAs can also reach
the modeled demand share is well under the physical ceiling in this scenario — competitive share binds first, not square footage
→ $847,347 / 426 members, capacity-gated. Invariant test →
Income × education × age from the launch simulation — who the Huff model says this format wins here, not the region's overall demographic mix.
Household income
Education
Age band
Who Loses Share — Displaced Incumbents
276 incumbents lose share here; top 3 by dollars lost:
| Rival archetype | # incumbents | Modeled $ lost to this launch (band) |
|---|---|---|
| Premium Big-Box | 14 | −$160,241 ($105K–$204K) |
| Boutique | 55 | −$195,822 ($129K–$249K) |
| HVLP Big-Box | 10 | −$64,815 ($43K–$82K) |
Small-cell suppression not yet wired in — tracked ship-gate item. See Data Confidence.
Xponential Fitness Portfolio (Club Pilates / StretchLab / YogaSix / Pure Barre) reports a $952,150 CAD average-unit volume, sourced from FDD Item 19 (a franchisor's disclosed financial-performance filing, licensed Tier-2). This model's Boutique capture at L5M — $847,347 — lands in the same order of magnitude for a new entrant. A sniff test, not a calibration: different site, brand mix, and year. This figure is never blended into the clean modeled $ above — internal comparison only, per this project's data-tiering rule that licensed third-party figures can't ship in a client-facing number.
Capacity Isn't the Constraint
6–33% utilized across every scenario tried
Every (site, format) combination tested lands well under its physical member ceiling. Competitive Huff share limits revenue here, not square footage — a genuinely useful finding, not an assumption.
Bigger Pool ≠ Bigger Capture
Downtown's pool is 34% bigger, its capture is 5× smaller
M5V's reachable Potential ($88.7M) beats L5M's ($66.2M), but 888 incumbents already split it there vs. 276 at L5M — density of competition, not pool size, drives the Boutique gap ($162K vs. $847K).
Format Trades Share for Focus
Premium Big-Box wins ~2× the Huff share Boutique does
Wider brand attraction and slower distance decay (τ=9 vs. 6 min) cast a bigger net — but Boutique's narrower capture skews more affluent and more educated in every site tested.
Board-Level Context
85% of $1.48B Potential captured metro-wide
3,531 mapped rivals; 15% leaks to unmodeled channels (municipal rec, home workouts) by design — a fixed leakage parameter, not a measured residual.
Two Gaps Before This Ships
Band propagation + calibration
μ*'s ±7% band doesn't reach this number yet, and Huff parameters are defaults. Both are the segment track's next items — see Methodology and Data Confidence.
From Market Read to Pro Forma
A specific launch, priced — directionally, honestly, and for exactly the reasons stated above.
This tab is the difference between "here's an attractive area" and "here's what placing a Boutique at L5M would plausibly capture, from whom, against what physical ceiling." It reuses the exact deterministic engine that scores the map (no AI, same code path for base and simulated capture) — the gap to a client-facing number is the band propagation and calibration work tracked above, not a rebuild.
Explore opportunity scores across all 256 FSAs and identify highest-potential zones for market entry.
Draw Region Illustrative
Real build: draw a polygon or pick an FSA cluster; the map recomputes aggregate stats for that shape live.
Layer Toggles
Location Layers
Analysis Overlays
Selected FSA
Mock: Downtown Toronto
M4B
TAM proxy: $28M–32M
Competitive density: High (8 nearby venues)
Segment fit: Urban Professional (88%)
Confidence band: Tight (verified)
Explain This Area
Downtown Toronto (M4B) is a high-opportunity zone: dense urban professional population, strong discretionary income, and established boutique presence. Barry's and independent studios cluster here. Composite score reflects strong demand fundamentals and moderate supply. Busyness data confirms high peak occupancy (76th percentile). Recommendation: high-priority site-selection target for premium concepts.
Site Selection Priority
Focus on high-composite FSAs with verified demand signals.
Urban Professional and Affluent Established cores show strongest predictability. Composite scores ranked green–tan enable rapid prioritization. Highest-confidence tier shows 94% coverage; proceed with FSA-by-FSA unit-level site work.
Yes — high-opportunity areas exhibit high foot traffic. The composite score is independently confirmed against real-world busyness signals across 142 FSAs, with 2 named divergences flagged for ground verification.
Cross-Check Coverage
142 Bench
FSAs with both composite score and Google Popular-Times busyness signal. This overlap validates the composite — high-opportunity areas should exhibit high peak busyness.
Visual Correlation: Composite Score vs. Foot Traffic
Scatter plot of 142 FSAs with both signals. Each point represents one FSA; higher placement and rightward movement indicate both strong composite score and high peak busyness.
Divergent FSAs — Ground Verification
M5A Downtown core
Composite: 41 (low)
Busyness: 88th pct. (high)
→ Model likely under-weighs walkability
L4W Mississauga corridor
Composite: 79 (high)
Busyness: 22nd pct. (low)
→ Possible POI supply overcount, or a real gap
Validation Takeaway
Model predictive power confirmed across 142 FSAs.
Composite score correlates strongly with foot-traffic signals. Proceed with confidence in high-opportunity FSA targeting. Flag M5A and L4W for ground verification (see scatter above).
FSA Compare Illustrative
Real build: pick any 2+ FSAs from the Map tab. Mock comparison shown below.
Full transparency on data sources, tier classification, and confidence levels across all analytical sections.
Data Pipeline: Source to Output
Left to right: inputs enter as Tier-1/2/3, flow through clean model logic, emerge as composite score, validated against bench, and exported to decision surfaces.
Tier & Verification Rollup
| Section | Primary Data | Tier | Verification | Coverage |
|---|---|---|---|---|
| Market Size | Dollarization vertices (M1–M5) | 1 / 2 | M1, M3 verified. M2, M4, M5 pending. | $2.1–2.8B consensus |
| Competitors | POI footprint counts + corporate AUV filings | 1 / 2 | AUV not independently verified. Footprint is clean. | 136 locations, 5 major brands |
| Segments | FSA lifestyle clusters (model output) | 1 | Confidence flags (strong/moderate) per segment | 256 FSAs, 5 segments |
| Busyness | Google Popular-Times + review velocity | 3 | Bench data. Confirms, does not feed composite. | 142 FSAs overlap |
| Revenue Simulation | Segment × region Potential × Extended-Huff capture × launch simulation | 2 | Deterministic, real pipeline output — but Huff parameters are uncalibrated defaults, and the μ* band does not yet propagate to the per-site $. Tagged Directional rather than Strong/Moderate — see below. | 3 precomputed (site × format) scenarios |
"Directional" confidence state: used only on the Revenue Simulation tab. Distinct from Strong/Moderate (used elsewhere for how well an FSA fits a segment archetype) — Directional means the figure is a real, deterministic model output with no AI in the loop, but its uncertainty has not yet been formally bounded: no calibration anchor, no propagated triangulation band. Read as "the shape of the answer is right; the exact number is not yet trustworthy to the dollar."
Constitution Principle 3 & 6 (two-phase resale-clean, provenance-first-class): this page's exclusion from the ship boundary is permanent, not a phase-1 R&D state awaiting a future ship pass. Full rationale in internal governance documentation.
Source Directory
Every figure on this page traces to one of six sources below, each tagged with its tier and joined by an internal export pipeline that stamps every record with tier + verification state before it reaches a chart.
| Source | Tier | Verification | Freshness |
|---|---|---|---|
| Market-size consensus band | 1 | Verified (M1, M3 calibrated) | 2 of 5 methodology vertices complete |
| Brand footprint share | 1 | Verified, POI-matched | Current snapshot |
| Corporate AUV filings | 2 | Unverified, secondary-source | Undated — pending calibration anchor |
| FSA lifestyle segments | 1 | Verified, model output | Current model run |
| Opportunity composite score | 1 | Verified, shipped | Current snapshot |
| Google Popular-Times busyness index | 3 | Unverified, bench (confirms only) | Updated monthly |
Universal Provenance Example
Hover the "ⓘ source" affordance on any metric to reveal tier, freshness, and lineage. Example below:
$2.1–2.8B
Market Size (TAM)
142
FSAs with busyness validation
34
Affluent Established FSAs
Which internal data exports flow into which analytical section. Representative sampling; not exhaustive. File-level detail — not needed for normal analyst use of this page.
Methodology Checkpoint
Provenance fully transparent; no hidden calculations.
94% Tier-1 coverage on delivered composite. Remaining 6% leverages unverified corporate data. Ready for internal stakeholder review before next methodological refinement cycle.