TL;DR

• What Retailers gain from using tapestry: Between A$37k and A$226k extra profit per store, every year — depending on how quickly you move and how well your data is set up. Based on a typical A$30m store with 27% gross margin.
• Where the money comes from: Mostly by protecting and growing your margin (even small basis-point wins add up fast across A$30m) and recovering lost sales through faster, smarter execution. You'll also free up team time and reduce costly mistakes. If you choose to, you can earn extra margin by giving suppliers controlled access to performance data.
• Why this works in retail: Right now, by the time you spot a problem — a promo that's not set up, stock that's missing, a category drifting — you've already lost sales and margin. Retail+ lets you see it, understand it, fix it, and confirm it worked — all in real time. Tasks and lists make sure nothing slips through. Hank (your AI assistant) answers the questions your analyst would, so decisions happen faster.
• How to prove it: Run a pilot in a handful of stores with matched control stores. Track how fast you spot and fix issues, how many actions get closed, and how much performance you recover. The math will speak for itself.

1. Executive Summary

Retail+ concentrates value in five retailer levers: (1) sales recapture/uplift, (2) gross-margin basis point gains, (3) hours returned (capacity unlocked), (4) execution/compliance risk avoidance, and (5) optional supplier contribution margin via data access/monetisation.

Using your required A$30m revenue per store example, the model estimates annual per-store value of ~A$36.7k (Conservative), ~A$105.0k (Moderate), ~A$225.6k (Aggressive).

Retail+ matters because it compresses the loop from “spot” → “diagnose” → “act” → “confirm” using live analytics plus execution tooling (tasks/lists), with Hank enabling bounded, analyst-like Q&A against live performance patterns.

Scenario range (per store, annual): ~A$37k to ~A$226k, dominated by margin bps and execution-driven sales recapture.

2. Report Metadata

• Role: Retailer decision‑makers (Executive team: CEO/Owner, CFO, COO, Commercial/Category leaders)
• Version: v1.0 (scenario model + substantiation-ready structure; designed to be replaced with your actual store inputs)
• Feature List Version: “Features Homepage” + Retail+ feature descriptions provided
• Date: 18 Jan 2026
• Confidence Score (0–100): 63 / 100
• Evidence Sources Used (this report):
  • Feature definitions and positioning (what Retail+ does): Features Homepage on tapestry website.
  • Role-specific economic models used as triangulation anchors and to bound assumptions/ranges (CEO/Owner, CFO, COO, Buying Manager, Category Manager).
  • Benchmark library embedded inside those role reports (e.g., on-shelf availability/out-of-stocks research, data-driven decision-making performance evidence, shelf/space elasticity, wage/on-cost context, gross margin context, data monetisation margin proxies).

Confidence score rationale (63/100):

• Upward drivers: feature capability statements are explicit; multiple reputable benchmark “headroom” sources are cited across the role reports (availability/OOS, execution/inventory accuracy, analytics-performance linkage, shelf/space response).
• Downward drivers: only revenue/store is given as a baseline figure; baseline GM%, fully-loaded labour, supplier monetisation participation, overlap between levers, and solution cost/pricing are assumptions (clearly exposed and sensitivity-tested).

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3. Methodology Overview (Scientific Approach)

Feature → Benefit → Economics mapping

1. Start with verifiable features from the supplied feature list (e.g., Real-time Analytics, Search & Scan, Task Management, Lists Management, Basket Analytics, Spaces, Data Trading, multi-device access, Hank).
2. Convert each feature into an operational benefit with a measurable proxy (examples: time-to-insight, promo readiness error rate, task closure time, attach rate, sales/GM variance recovery rate).
3. Convert each benefit into a P&L mechanism:
   • Sales uplift → incremental gross profit = ΔSales × GM%
   • Margin gain → incremental gross profit = Sales × ΔGM (bps)
   • Hours returned → hours × cost/hr × capture% (capacity vs cash)
   • Compliance/risk avoided → expected value reduction
   • Supplier contribution margin → supplier subscription/data access contribution

Scenario modelling

We model three scenarios (Conservative / Moderate / Aggressive) that represent differences in:

• data readiness,
• adoption and operating cadence,
• execution discipline,
• supplier participation (for data monetisation).

Sensitivity modelling

We publish:

• unit economics (“value per +10 bps margin”, etc.),
• a moderate-case one-at-a-time sensitivity table,
• GM% sensitivity (affects sales-uplift GP component).

Benchmark triangulation

We treat benchmarks as plausibility bounds (headroom and mechanism support), not promises - drawing from the benchmark libraries compiled inside the role reports (availability/OOS, execution/inventory accuracy, analytics-performance evidence, shelf/space response, wage context, gross margin context, data monetisation margin proxies).

Conservative bias and validation

• Overlap haircut: we apply a 15% overlap reduction to combined sales-uplift + margin-gain GP to reduce double-counting risk (a conservative modelling guardrail).
• Hours captured: we treat “hours returned” as partially captured (not all time becomes cash savings).
• Validation plan: outcome claims should be validated via a store pilot using matched controls and difference‑in‑differences (outlined in Appendix models).

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4. Role Context: Operational Realities & Challenges

Across retailers, the recurring constraints are remarkably consistent:

1. Decision latency cost: when insight arrives weekly/monthly, issues have already leaked sales and margin. Retail+ positions “live, store-level insight you can act on” to shorten that latency.
2. Execution gap: good decisions don’t pay if store execution is inconsistent (promo setup, ticketing, ranging compliance, gap closure). Tasks + lists explicitly target “nothing slips through the cracks.”
3. Margin pressure (bps matter): small basis-point improvements applied to a large sales base become material profit. Multiple role models converge on margin bps as the dominant sensitivity.
4. Labour constraints: the opportunity is often capacity unlock (better prioritisation, faster “truth checks”, less ad-hoc reporting), not immediate headcount reduction.
5. Supplier relationships and data governance: retailers want clearer, data-backed joint business planning and (optionally) to convert reporting burden into governed supplier-paid access—while keeping permissions/ownership under retailer control.

5. Feature → Benefit → Economic Mechanism Mapping

5.1 Feature Summary

Retail+ includes:

• Mobile, tablet and desktop access
• Search and scan (including barcode scan)
• Home Screen (performance overview + priority tasks)
• Real-time Analytics, including: compare, export (CSV), store & group views, department insights, category drill-down, supplier performance, manufacturer insights, basket analysis, comprehensive analysis report
• Task management
• Lists management
• Spaces (Retail+ Pro) (aisle-to-bay performance)
• Basket analytics (basket value, margin, profit)
• Data trading (package and sell data to chosen suppliers; retailer controls permissions)
• Hank – your Intelligent Assistant (analyst-like Q&A; carries context into workflows)
• Department Workflow Recommendations (weekly recommendations; task conversion)
• Trend Analysis and Insights (plain-language summaries + next steps)
• Data Marketplace (permissioned sharing at chosen granularity)
• Retailer–Supplier Task Collaboration (coming soon)
• Conversations (coming soon)

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5.2 Benefits Extracted

Hank capability scope constraint: This report assumes the current capabilities of Hank (department declines/rankings, product margin/profit queries with thresholds, growth products/categories, top/bottom SKUs, rolling totals, most profitable departments) - i.e., analyst-like retrieval and ranking on POS-derived structures, not “open-ended magic”. This aligns with how the role reports constrain scope to explicit analytics patterns.

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5.3 Economic Mechanisms

6. Financial & Operational Assumptions

Baseline assumptions (inputs)

Scenario input ranges (used in modelling)

7. Economic Impact Modelling (Per Store)

7.1 Impact Areas

We quantify five impact areas (annual, per store):

1. Sales uplift → incremental gross profit from incremental sales
2. Margin gain → incremental gross profit from basis-point improvement
3. Hours returned → monetised at labour cost/hr × capture% (capacity vs cash)
4. Compliance / risk avoidance → expected value reduction
5. Supplier contribution margin → optional contribution from governed supplier-paid access

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Core formulas (per store):

• Incremental GP from sales uplift = R × su × GM
• Incremental GP from margin gain = R × mg
• Overlap haircut (conservative) = k × (GP_sales + GP_margin)
• Captured hours value = hours_saved × 52 × labour_cost × capture
• Supplier contribution = supplier_CM (modelled directly as contribution)
• Total annual value = GP_sales + GP_margin − overlap + hours_value + risk_EV + supplier_CM

7.2 Per-Store Annual Impact Table (A$, per store, per year)

Assumptions used: R = A$30,000,000; GM = 27%; labour = A$35/hr; overlap haircut k = 15%.

Interpretation:

• The model is intentionally “small percentage” economics: even the Moderate case is ~0.35% of annual sales in profit-equivalent value—consistent with the idea that Retail+ primarily reduces leakage and improves decision cadence rather than creating a one-off transformation event.
• Most value comes from margin bps + recaptured sales, with hours returned and supplier CM as secondary (supplier CM can become material if actively commercialised).

7.3 Sensitivity Analysis

7.3.1 “Value per unit change” (per store, annual)

Using R = 30m, GM = 27%, and overlap haircut k = 15%:

• +10 bps margin gain (0.10%)
  • Gross: 30,000,000 × 0.001 = A$30,000
  • Net-of-overlap (if sales/margin are the only changing pieces): A$25,500
• +10 bps sales uplift (0.10%)
  • Gross GP: 30,000,000 × 0.001 × 0.27 = A$8,100
  • Net-of-overlap: A$6,885
• +1 hour/week saved
  • Gross economic value: 52 × 35 = A$1,820
  • Captured value depends on capture% (e.g., 50% → A$910)
• +A$1,000/year risk avoided → +A$1,000
• +A$1,000/year supplier contribution margin → +A$1,000

Key takeaway: the model is most sensitive to margin basis points, then sales uplift, then supplier CM, then hours returned (unless you can truly cash out labour). This ordering matches the role-report sensitivity conclusions.

7.3.2 Moderate-scenario one-at-a-time sensitivity (illustrative)

Base Moderate total = A$104,955 / store / year.

7.3.3 GM% sensitivity (affects sales-uplift component only)

Example (Moderate sales uplift = 0.50%):

• At GM = 25%, GP_sales = 30m × 0.005 × 0.25 = A$37,500 (vs A$40,500 at 27%).
• This shifts total modestly relative to margin bps sensitivity—again consistent with role reports.

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8. Multi-Store & Long-Term Value

8.1 Annual Impact at Scale (A$/year)

(Linear scaling shown for transparency; in reality, some components may show economies of scale while execution variance may reduce linearity.)

8.2 10-Year Cumulative Impact

Adoption ramp used: Yr1 40%, Yr2 70%, Yr3 90%, Yr4–10 100% (sum factor = 9.0×).

Per store (10-year):

• Undiscounted:
  • Conservative: A$330,336
  • Moderate: A$944,595
  • Aggressive: A$2,030,742
• NPV @ 8% (illustrative):
  • Conservative: A$213,542
  • Moderate: A$610,622
  • Aggressive: A$1,312,749

At 100 stores (NPV @ 8%, illustrative):

• Conservative: A$21.35m
• Moderate: A$61.06m
• Aggressive: A$131.27m

8.3 Payback & ROI Profile

Because solution pricing and rollout cost were not provided, the defensible approach is threshold and formula outputs.

1. Break-even all-in annual cost per store (value-based ceiling):
   • Conservative: A$36.7k/year
   • Moderate: A$105.0k/year
   • Aggressive: A$225.6k/year
2. Payback (months) if annual cost per store is known:
   • Payback months ≈ 12 × (Annual Cost / Annual Benefit)
   • Example (illustrative only): if annual all-in cost = A$30,000/store:
   • Conservative: 12 × 30,000 / 36,704 ≈ 9.8 months
   • Moderate: 12 × 30,000 / 104,955 ≈ 3.4 months
   • Aggressive: 12 × 30,000 / 225,638 ≈ 1.6 months

9. Dependencies, Risks & Conditions

Preconditions (needed to realise value)

1. Data readiness: clean POS ingestion, stable product hierarchy (dept/category), supplier mapping, consistent promo identifiers.
2. Operating cadence: weekly exceptions review + monthly commercial review using the same views; decisions must be routinised.
3. Execution loop: tasks/lists must be used and closed with evidence/verification, not just created.
4. Governance for supplier access: permissions, aggregation rules, contracts, and audit trails before data trading/marketplace concepts are activated.

Risks (and why they matter)

• Double-counting risk: sales uplift and margin gain can overlap (mix, availability, promo effects). Mitigation: overlap haircut + benefits ledger + pilot attribution.
• Adoption risk: dashboards without behaviour change yield little. Mitigation: embed into rituals; make “Home Screen → drilldown → task → closure” the operating rhythm.
• Labour capture risk: hours returned may not convert to cash. Mitigation: track “capacity unlocked” separately from “cash released”; redeploy time to availability, promo readiness, range discipline.
• Supplier monetisation risk: commercial upside depends on supplier appetite and governance. Mitigation: treat supplier CM as optional; stage-gate with legal/privacy and clear packaging.
• Feature delivery risk: supplied feature pages are marked Backlog, and some are explicitly “coming soon”; realised value depends on what is delivered and adopted.

10. Citations & Evidence Library

Below is the benchmark set referenced across the role reports to support mechanisms and bound assumptions (used for plausibility, not as guarantees).

1. Data-driven decision-making and performance (“Strength in Numbers”)
   • Authors: Brynjolfsson, Hitt, Kim
   • Year: 2011
   • Sample: 179 large firms (multi-industry)
   • Geography: US / large-firm context
   • Relevance: supports “faster insight → higher productivity/output” directionality
2. On-shelf availability / out-of-stocks benchmark (ECR “Blue Book”)
   • Org: ECR Europe
   • Year: 2003 (republished/used widely)
   • Sample: large observational base (reported in role report)
   • Geography: Europe
   • Relevance: anchors that OOS is material and process-driven; supports sales-recapture plausibility
3. Out-of-stocks consumer response synthesis (Corsten & Gruen research stream)
   • Authors: Corsten & Gruen
   • Year: early 2000s stream (cited in role reports)
   • Geography: global/meta
   • Relevance: supports that some OOS becomes truly lost sales; informs conservative recapture assumptions
4. Execution and inventory inaccuracy evidence
   • Raman, DeHoratius, Ton (Execution in retail ops)
   • DeHoratius & Raman (inventory record inaccuracy empirical analysis; large sample across stores/records)
   • Relevance: supports that execution failures are common and profit-damaging; justifies tooling that closes the loop
5. Shelf/space response (space elasticity meta-analysis + field experiments)
   • Eisend (meta-analysis; many elasticity estimates)
   • Drèze, Hoch, Purk (field experiments)
   • Relevance: supports that space/layout interventions can move sales, informing the “Spaces” mechanism
6. Gross margin benchmark context (Australian grocery)
   • Sources: public reporting referenced within role reports (e.g., Coles/Woolworths margin anchors)
   • Relevance: calibrates GM% for scaling uplift to GP$
7. Wage and on-cost context (Australia)
   • Sources: award wage references + statutory super context (as compiled in role reports)
   • Relevance: informs labour value of hours returned
8. Data monetisation / retail media margin proxies
   • Sources: industry margin proxies cited in role reports (e.g., Reuters/Deloitte-style high-margin ranges)
   • Relevance: supports that supplier-paid data access can be structurally high contribution, but is governance-dependent

11. Appendices

Appendix A — Raw formula list

Let:

• R = annual revenue per store
• GM = baseline gross margin rate
• su = sales uplift rate
• mg = margin gain rate (bps as decimal)
• k = overlap haircut factor (conservative)
• h = hours saved per week
• w = labour cost per hour
• cap = labour capture factor
• risk = compliance/risk avoided expected value per year
• scm = supplier contribution margin per year

Then:

1. GP_sales = R × su × GM
2. GP_margin = R × mg
3. Overlap = k × (GP_sales + GP_margin)
4. Hours_value = h × 52 × w × cap
5. Total annual value = GP_sales + GP_margin − Overlap + Hours_value + risk + scm

10-year:

• Undiscounted: Total_10y = Total_annual × Σ Adoption_t
• NPV: NPV = Σ (Total_annual × Adoption_t / (1+r)^t)

Appendix B — Extra models to tighten precision (recommended validation approach)

1. Availability model (OOS-based): measure baseline OOS proxies; estimate reduction; convert to recovered sales using observed substitution behaviours.
2. Promo incrementality model: use basket analytics to separate incremental GP from cannibalised discounting.
3. Execution model: link task/list completion to measurable outcomes (promo readiness, gap closure time, repeat issue rate) and quantify sales/GM recovery.
4. Supplier monetisation model: package tiers × supplier uptake × price × contribution margin; stage-gate with governance.

Appendix C — Full feature mapping matrix (condensed)

(Feature definitions and “coming soon” flags are taken from the feature list.)

Appendix D — Cross-check: role-model outputs vs this consolidated retailer model

The role reports supplied produce the following per-store annual ranges (each uses slightly different baseline GM%, labour treatment, and supplier assumptions). This consolidated model is designed to sit within the envelope and uses an explicit overlap haircut + hours capture factor for conservatism.