Target Corporation (NYSE: TGT) — CIK 0000027419. General merchandise retailer. Fiscal year ends late January/early February (e.g., FY2024 ended Feb 1, 2025). ~1,960 stores across the US.
Practice Exercises
1. Revenue Forecasting — Build a top-line growth model using one or more approaches Not Started
Choose a model type below, pull the last 5 years of data, and forecast FY2026–2028. Try multiple approaches and compare outputs.
1a. Top-Down Growth Rate Model
Method: Apply historical YoY growth rates to total revenue. Calculate CAGR, moving averages, and trend inflection points.
When to use: Quick directional estimate, sanity-checking other models, communicating the "simple story" to leadership.
Target context: Revenue surged FY2020–2021 (COVID stay-at-home demand), then flattened/declined FY2022–2025. Is this mean-reversion or structural decline?
Step-by-step using the Annual Data export:
1. Open "Income Statement" sheet. Find "Revenue from Contract with Customer" — this is total net sales.
2. Create a new sheet. Row 1 = Revenue ($M) for FY2020–FY2025. Copy the values.
3. Row 2 = YoY Growth. Formula: =(B1-A1)/A1. You'll see: +20%, +13%, +3%, -2%, -1%, -2%.
4. Row 3 = 3-year moving average. Post-COVID, the average is ~0% — flat business.
5. Key question: Is FY2019's $78B the "true" base, meaning FY2020–21 was a temporary COVID bump that's now reverting? Or is $105B the new normal?
6. Build scenarios: Bear (continues declining 1-2%/year), Base (flat at ~$105B), Bull (modest recovery 2-3% as comps normalize).
7. Forecast FY2026–2028 using =Prior Year × (1 + Rate).
8. Sanity check: compare to total US retail market growth (~3-4%/year). If your bull case exceeds market growth, Target must be GAINING share — is that realistic?
When to use: Quick directional estimate, sanity-checking other models, communicating the "simple story" to leadership.
Target context: Revenue surged FY2020–2021 (COVID stay-at-home demand), then flattened/declined FY2022–2025. Is this mean-reversion or structural decline?
Step-by-step using the Annual Data export:
1. Open "Income Statement" sheet. Find "Revenue from Contract with Customer" — this is total net sales.
2. Create a new sheet. Row 1 = Revenue ($M) for FY2020–FY2025. Copy the values.
3. Row 2 = YoY Growth. Formula: =(B1-A1)/A1. You'll see: +20%, +13%, +3%, -2%, -1%, -2%.
4. Row 3 = 3-year moving average. Post-COVID, the average is ~0% — flat business.
5. Key question: Is FY2019's $78B the "true" base, meaning FY2020–21 was a temporary COVID bump that's now reverting? Or is $105B the new normal?
6. Build scenarios: Bear (continues declining 1-2%/year), Base (flat at ~$105B), Bull (modest recovery 2-3% as comps normalize).
7. Forecast FY2026–2028 using =Prior Year × (1 + Rate).
8. Sanity check: compare to total US retail market growth (~3-4%/year). If your bull case exceeds market growth, Target must be GAINING share — is that realistic?
1b. Comp-Store Sales + New Store Model
Method: Total Revenue = (Existing Stores × Prior Year Rev/Store × (1 + Comp Growth)) + (Net New Stores × Avg New Store Revenue).
When to use: THE standard retail forecasting model. Separates organic growth from expansion.
Target context: ~1,960 stores. Comp-store growth was +20% in FY2021, then -1% to -4% in FY2023-24. Net new stores ~20/year.
Step-by-step using the Annual Data export:
1. From the data export, pull total revenue. Store count comes from the 10-K (search "number of stores" in MD&A section) — Target had 1,956 stores at end of FY2024.
2. Calculate Revenue per Store: Total Revenue / Store Count. Should be ~$54M/store.
3. Build assumptions table: Beginning store count, new stores opened (+20-25/yr), stores closed (-2-5/yr), ending store count.
4. Comp-store growth assumption: this is the HARDEST input. Pull historical comps from earnings releases. Model: -1% (bear), +1% (base), +3% (bull).
5. Formula: Revenue = (Prior Store Count × Rev/Store × (1+Comp%)) + (Net New Stores × 0.8 × Rev/Store). New stores ramp to 80% of mature store volume in year 1.
6. Extend for FY2026–2028. Note: new stores add <1% to growth — comp growth is what matters.
7. Sensitivity: show revenue at -2%, 0%, +2%, +4% comp — the range is wide ($100B to $113B by FY2028).
When to use: THE standard retail forecasting model. Separates organic growth from expansion.
Target context: ~1,960 stores. Comp-store growth was +20% in FY2021, then -1% to -4% in FY2023-24. Net new stores ~20/year.
Step-by-step using the Annual Data export:
1. From the data export, pull total revenue. Store count comes from the 10-K (search "number of stores" in MD&A section) — Target had 1,956 stores at end of FY2024.
2. Calculate Revenue per Store: Total Revenue / Store Count. Should be ~$54M/store.
3. Build assumptions table: Beginning store count, new stores opened (+20-25/yr), stores closed (-2-5/yr), ending store count.
4. Comp-store growth assumption: this is the HARDEST input. Pull historical comps from earnings releases. Model: -1% (bear), +1% (base), +3% (bull).
5. Formula: Revenue = (Prior Store Count × Rev/Store × (1+Comp%)) + (Net New Stores × 0.8 × Rev/Store). New stores ramp to 80% of mature store volume in year 1.
6. Extend for FY2026–2028. Note: new stores add <1% to growth — comp growth is what matters.
7. Sensitivity: show revenue at -2%, 0%, +2%, +4% comp — the range is wide ($100B to $113B by FY2028).
1c. Traffic × Basket Size Model
Method: Revenue = Transactions × Average Transaction Value (ATV). Decompose comp growth into volume vs. price.
When to use: When you need to understand WHETHER growth comes from more customers or higher spend per visit.
Target context: Post-COVID traffic declined while basket size grew (inflation + mix). Which is more sustainable?
Step-by-step using the Annual Data export:
1. Target discloses traffic and ATV trends on earnings calls (not in XBRL). From recent calls: traffic -2% to +1%, ATV +2% to +4%.
2. From your data export, pull total revenue. Estimate transactions: Revenue / ATV. Target's ATV is ~$40-45 per trip.
3. Calculate implied transactions: ~$105B / $42 = ~2.5B transactions/year across all stores.
4. Per-store: 2.5B / 1,960 = ~1.3M transactions/store/year, or ~3,500/store/day.
5. Model traffic growth: is it recovering post-COVID? Target has invested heavily in Drive Up and same-day services to win back trips.
6. Model ATV growth: inflation drives ATV up, but consumers trading down to private label offsets. Assume +2%/year.
7. Forecast: Revenue[t] = Transactions[t-1] × (1+Traffic Growth) × ATV[t-1] × (1+ATV Growth).
8. Key insight: if traffic is flat and ATV grows 2%, you get 2% revenue growth. That's barely above inflation — is this a "growth" company?
When to use: When you need to understand WHETHER growth comes from more customers or higher spend per visit.
Target context: Post-COVID traffic declined while basket size grew (inflation + mix). Which is more sustainable?
Step-by-step using the Annual Data export:
1. Target discloses traffic and ATV trends on earnings calls (not in XBRL). From recent calls: traffic -2% to +1%, ATV +2% to +4%.
2. From your data export, pull total revenue. Estimate transactions: Revenue / ATV. Target's ATV is ~$40-45 per trip.
3. Calculate implied transactions: ~$105B / $42 = ~2.5B transactions/year across all stores.
4. Per-store: 2.5B / 1,960 = ~1.3M transactions/store/year, or ~3,500/store/day.
5. Model traffic growth: is it recovering post-COVID? Target has invested heavily in Drive Up and same-day services to win back trips.
6. Model ATV growth: inflation drives ATV up, but consumers trading down to private label offsets. Assume +2%/year.
7. Forecast: Revenue[t] = Transactions[t-1] × (1+Traffic Growth) × ATV[t-1] × (1+ATV Growth).
8. Key insight: if traffic is flat and ATV grows 2%, you get 2% revenue growth. That's barely above inflation — is this a "growth" company?
1d. Category Mix Model
Method: Forecast revenue by merchandise category, each with its own growth rate and margin profile.
When to use: When category mix is shifting and different categories have very different margins.
Target context: Discretionary (Home, Apparel) crushed post-COVID. Essentials/Food steady. Beauty outperforming.
Step-by-step using the Annual Data export:
1. Category mix isn't in XBRL. Pull from the 10-K segment discussion. Target's categories: Apparel & Accessories (~17%), Beauty (~6%), Food & Beverage (~23%), Hardlines (~15%), Home (~18%), Essentials & Household (~21%).
2. Calculate each category's revenue: Total Revenue × Category Mix %.
3. Research category trends: Beauty growing +8-10%/year (market tailwind). Food flat (low margin but traffic driver). Discretionary categories (Home, Hardlines) declining -3-5%.
4. Set growth rates per category for FY2026–2028. Consider: are consumers still pulling back on discretionary? Is the "nesting" trend over?
5. Forecast each category, then sum → Total Revenue.
6. CRITICAL: also model gross margin by category. Food is ~20% margin, Apparel is ~40%, Beauty is ~35%. As mix shifts toward Food/Essentials, TOTAL gross margin compresses even if revenue grows.
7. Build a "mix shift impact" row: show how margin changes at different category-growth assumptions.
When to use: When category mix is shifting and different categories have very different margins.
Target context: Discretionary (Home, Apparel) crushed post-COVID. Essentials/Food steady. Beauty outperforming.
Step-by-step using the Annual Data export:
1. Category mix isn't in XBRL. Pull from the 10-K segment discussion. Target's categories: Apparel & Accessories (~17%), Beauty (~6%), Food & Beverage (~23%), Hardlines (~15%), Home (~18%), Essentials & Household (~21%).
2. Calculate each category's revenue: Total Revenue × Category Mix %.
3. Research category trends: Beauty growing +8-10%/year (market tailwind). Food flat (low margin but traffic driver). Discretionary categories (Home, Hardlines) declining -3-5%.
4. Set growth rates per category for FY2026–2028. Consider: are consumers still pulling back on discretionary? Is the "nesting" trend over?
5. Forecast each category, then sum → Total Revenue.
6. CRITICAL: also model gross margin by category. Food is ~20% margin, Apparel is ~40%, Beauty is ~35%. As mix shifts toward Food/Essentials, TOTAL gross margin compresses even if revenue grows.
7. Build a "mix shift impact" row: show how margin changes at different category-growth assumptions.
1e. Digital vs. In-Store Channel Model
Method: Total Revenue = In-Store Revenue + Digital Revenue. Model digital penetration over time.
When to use: Omnichannel retailers where digital has different economics.
Target context: Digital ~18% of sales. Same-day services (Drive Up, Order Pickup, Shipt) are Target's edge vs. Amazon.
Step-by-step using the Annual Data export:
1. Digital penetration data is on earnings calls: ~18% of sales in FY2024, up from ~9% pre-COVID.
2. Calculate: Digital Revenue = $105B × 18% = ~$19B. In-Store = $105B × 82% = ~$86B.
3. Model digital growth: was growing 20-30%/year during COVID, now ~5-8%. Assume digital penetration reaches 22-25% by FY2028.
4. Model in-store: flat to slightly declining as digital cannibalizes some trips.
5. Formula: Total Rev = In-Store[t-1] × (1+In-Store Growth) + Digital[t-1] × (1+Digital Growth).
6. Key question: does digital growth ADD revenue (new occasions) or SHIFT revenue (same sale, different channel)? If it's mostly shift, total growth is still flat.
7. Model the margin impact: same-day fulfillment from stores is cheaper than shipping from a DC, but requires labor. Does higher digital = higher or lower EBIT margin? (Target claims it's accretive — verify with the numbers.)
When to use: Omnichannel retailers where digital has different economics.
Target context: Digital ~18% of sales. Same-day services (Drive Up, Order Pickup, Shipt) are Target's edge vs. Amazon.
Step-by-step using the Annual Data export:
1. Digital penetration data is on earnings calls: ~18% of sales in FY2024, up from ~9% pre-COVID.
2. Calculate: Digital Revenue = $105B × 18% = ~$19B. In-Store = $105B × 82% = ~$86B.
3. Model digital growth: was growing 20-30%/year during COVID, now ~5-8%. Assume digital penetration reaches 22-25% by FY2028.
4. Model in-store: flat to slightly declining as digital cannibalizes some trips.
5. Formula: Total Rev = In-Store[t-1] × (1+In-Store Growth) + Digital[t-1] × (1+Digital Growth).
6. Key question: does digital growth ADD revenue (new occasions) or SHIFT revenue (same sale, different channel)? If it's mostly shift, total growth is still flat.
7. Model the margin impact: same-day fulfillment from stores is cheaper than shipping from a DC, but requires labor. Does higher digital = higher or lower EBIT margin? (Target claims it's accretive — verify with the numbers.)
1f. Macro-Linked Consumer Spending Model
Method: Regress Target revenue growth against macro indicators to scenario-plan with external data.
When to use: When the business is highly correlated with macro cycles (mass retail is).
Target context: Target's core customer is middle-income. They trade down in recessions, trade up in expansions.
Step-by-step using the Annual Data export:
1. Pull Target's YoY revenue growth from your data export for FY2018–FY2025.
2. Pull macro data (free from FRED — fred.stlouisfed.org): Real Personal Consumption Expenditures growth, Consumer Confidence Index, Unemployment Rate.
3. In Excel, create a scatter plot: X = Real PCE growth, Y = Target revenue growth. Add a trendline. What's the R²?
4. If R² > 0.5, you have a useful relationship. The slope tells you Target's "beta" to consumer spending.
5. For your forecast, use economist consensus for PCE/GDP growth (available from Fed dot plot, Conference Board).
6. Scenario: Recession (PCE -1%) → plug into your regression → implied Target growth. Expansion (PCE +3%) → implied growth.
7. This gives EXTERNALLY-justified scenarios rather than arbitrary growth rates. In an FP&A presentation, leadership trusts "based on consensus GDP" more than "I think 3% feels right."
8. Caveat: COVID broke the relationship (stimulus checks + stay-at-home = spike). Consider using pre-COVID data (FY2015–2019) for the regression and treating FY2020-21 as outliers.
When to use: When the business is highly correlated with macro cycles (mass retail is).
Target context: Target's core customer is middle-income. They trade down in recessions, trade up in expansions.
Step-by-step using the Annual Data export:
1. Pull Target's YoY revenue growth from your data export for FY2018–FY2025.
2. Pull macro data (free from FRED — fred.stlouisfed.org): Real Personal Consumption Expenditures growth, Consumer Confidence Index, Unemployment Rate.
3. In Excel, create a scatter plot: X = Real PCE growth, Y = Target revenue growth. Add a trendline. What's the R²?
4. If R² > 0.5, you have a useful relationship. The slope tells you Target's "beta" to consumer spending.
5. For your forecast, use economist consensus for PCE/GDP growth (available from Fed dot plot, Conference Board).
6. Scenario: Recession (PCE -1%) → plug into your regression → implied Target growth. Expansion (PCE +3%) → implied growth.
7. This gives EXTERNALLY-justified scenarios rather than arbitrary growth rates. In an FP&A presentation, leadership trusts "based on consensus GDP" more than "I think 3% feels right."
8. Caveat: COVID broke the relationship (stimulus checks + stay-at-home = spike). Consider using pre-COVID data (FY2015–2019) for the regression and treating FY2020-21 as outliers.
Learnings: —
2. Build a 3-statement model linking income statement → balance sheet → cash flow Not Started
Target is capital-intensive (stores, DCs, supply chain). Focus on how CapEx flows to PP&E on the balance sheet, and how inventory changes hit working capital in the cash flow statement. Inventory management is critical for retail margins.
Learnings: —
3. Take Q1–Q3 actuals and forecast Q4 + full year — then compare to the actual 10-K Not Started
Target's Q4 (Nov–Jan) includes Black Friday, Cyber Monday, and the full holiday season — it's ~30% of annual revenue. Use historical Q4/full-year ratios and holiday retail trends to forecast. Compare to actual.
Learnings: —
4. Do a variance analysis: compare your forecast to actuals and explain the gaps Not Started
Key retail drivers to decompose: traffic (transactions), average basket size, digital vs. in-store mix, gross margin (COGS pressure from theft/shrink, freight, markdowns). Target's margin compression in FY2022 is a great case study.
Learnings: —
5. Build a waterfall chart showing revenue bridges (traffic × basket × digital mix) Not Started
Decompose YoY revenue change into: transaction count change, average transaction amount change, digital fulfillment growth (same-day services, Drive Up, Shipt). Which is the primary growth lever now?
Learnings: —
6. Calculate key metrics: comp-store sales, gross margin %, inventory turns, ROIC, dividend payout ratio Not Started
Retail-specific metrics: inventory turnover (COGS / avg inventory), days sales of inventory, ROIC (NOPAT / invested capital), shrink as % of revenue, SG&A leverage (SG&A growth vs. revenue growth). Compare to Walmart and Costco benchmarks.
Learnings: —
Full annual financial statements. Target's FY ends in late January/early February (FY2024 = Feb 2024 – Feb 2025).
| Year | Filing | Interactive | Filed |
|---|---|---|---|
| FY 2025 | SEC Filing | Investor Page | Mar 2026 |
| FY 2024 | SEC Filing | Investor Page | Mar 2025 |
| FY 2023 | SEC Filing | Investor Page | Mar 2024 |
| FY 2022 | SEC Filing | Investor Page | Mar 2023 |
| FY 2021 | SEC Filing | Investor Page | Mar 2022 |
| FY 2020 | SEC Filing | Investor Page | Mar 2021 |
Quarterly financials. Target's quarters end in May (Q1), Aug (Q2), Nov (Q3). Q4 is in the 10-K (holiday season).
| Period | Filing | Interactive | Filed |
|---|---|---|---|
| Q1 FY2025 (May 2025) | SEC Filing | Investor Page | Jun 2025 |
| Q3 FY2024 (Nov 2024) | SEC Filing | Investor Page | Dec 2024 |
| Q2 FY2024 (Aug 2024) | SEC Filing | Investor Page | Sep 2024 |
| Q1 FY2024 (May 2024) | SEC Filing | Investor Page | Jun 2024 |
Key Resources