Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

Market Share Analyzer — Competitive Positioning for Investment Analysis

Convert MarketCheck sold transaction data into real-time market share analytics for investment decisions. Track brand share, segment conquest patterns, EV penetration curves, and competitive positioning — all mapped to stock tickers with BULLISH / BEARISH / NEUTRAL / CAUTION signals.

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_makes, tracked_states, benchmark_period_months, country. If missing, ask for geographic scope and focus. US-only. Confirm profile.

Built-in Ticker → Makes Mapping

OEM TICKERS:
F     → Ford, Lincoln
GM    → Chevrolet, GMC, Buick, Cadillac
TM    → Toyota, Lexus
HMC   → Honda, Acura
STLA  → Chrysler, Dodge, Jeep, Ram, Fiat, Alfa Romeo, Maserati
TSLA  → Tesla
RIVN  → Rivian
LCID  → Lucid
HYMTF → Hyundai, Kia, Genesis
NSANY → Nissan, Infiniti
MBGAF → Mercedes-Benz
BMWYY → BMW, MINI, Rolls-Royce
VWAGY → Volkswagen, Audi, Porsche, Lamborghini, Bentley

DEALER GROUP TICKERS:
AN    → AutoNation
LAD   → Lithia Motors
PAG   → Penske Automotive
SAH   → Sonic Automotive
GPI   → Group 1 Automotive
ABG   → Asbury Automotive
KMX   → CarMax
CVNA  → Carvana

User Context

Financial analyst or sector strategist needing competitive positioning data for stock-level investment decisions. Market share changes are leading indicators of revenue trajectory. Every output maps brands to tickers with investment signals.

Workflow: Brand Market Share (Ticker-Level View)

Calculate market share by make, aggregate by ticker, and compare against a prior period.

1. Call mcp__marketcheck__get_sold_summary for the current period:

   • date_from / date_to: target month first-to-last day
   • state: user's state filter (omit for national)
   • inventory_type: as specified (or omit for both)
   • ranking_dimensions: make
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 20 → Extract only: per make — sold_count, total sold_count. Discard full response.

2. Repeat for the prior period with identical filters but adjusted dates. → Extract only: per make — sold_count, total sold_count. Discard full response.

3. Calculate for each make, then aggregate by ticker:

   • Current Share % = Make Sold Count / Total Sold Count × 100
   • Prior Share % = same for prior period
   • Share Change (bps) = (Current Share % - Prior Share %) × 100
   • Volume Change % = (Current Sold - Prior Sold) / Prior Sold × 100

4. Present as a ranked table at the TICKER level:

   • Columns: Rank, Ticker, Makes, Current Sold, Current Share %, Prior Share %, Share Change (bps), Volume Change %, Signal
   • Signal: BULLISH if gaining > +30 bps AND volume up; BEARISH if losing > -30 bps AND volume down; CAUTION if share gaining but volume down; NEUTRAL if within ±30 bps

5. Investment summary: "The top share gainers this period were [Ticker1] (+XX bps), [Ticker2] (+XX bps). The biggest losers were [Ticker3] (-XX bps). For tracked tickers: [Ticker] moved from #X to #Y with [+/-N] bps — [BULLISH/BEARISH] for revenue trajectory."

Workflow: Segment Conquest Analysis (Investment Positioning)

Determine which OEM tickers are winning within specific vehicle segments.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target period
   • state: user's state filter (omit for national)
   • body_type: target segment (e.g. SUV)
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 15 → Extract only: per make/model — sold_count. Discard full response.

2. Repeat for comparison period. → Extract only: per make/model — sold_count. Discard full response.

3. For each segment, calculate and map to tickers:

   • Segment leader and their ticker
   • Tracked tickers' rank within the segment
   • Gap to leader in bps
   • Signal per ticker: GAINING / LOSING / STABLE within segment

4. Investment insight: "In the SUV segment, TM gained 120 bps through RAV4 and Highlander. F lost share as Explorer volume declined. STLA SUVs (Jeep) saw the steepest share loss at -85 bps — BEARISH for STLA's North American revenue mix."

Workflow: EV Market Share Tracking (Ticker View)

Monitor EV share by brand with investment signals.

1. Call mcp__marketcheck__get_sold_summary for EV sales:

   • date_from / date_to: target period
   • state: user's state filter (omit for national)
   • fuel_type_category: EV
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 15 → Extract only: per make/model — sold_count; plus total EV sold_count. Discard full response.

2. Repeat for total market (no fuel_type_category) and prior periods. → Extract only: per make/model — sold_count. Discard full response.

3. Calculate and map to tickers:

   • EV Penetration Rate with trend — sector-level adoption signal
   • Brand EV Share — who is winning the EV race (TSLA vs legacy)
   • EV % of Each OEM's Total Sales — transition progress by ticker
   • Signal: For TSLA: share loss rate and absolute volume. For legacy OEMs: transition pace vs peers.

4. Investment implications: "TSLA's EV share dropped from XX% to XX% as HYMTF and GM gained. However, total EV market grew XX%, so TSLA absolute volume is still up. For legacy OEMs: F at 4.2% EV penetration vs GM at 6.1% — GM has stronger transition momentum."

Workflow: Dealer Group Market Share (Retail Stock Signal)

Rank publicly traded dealer groups by market share.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target period
   • ranking_dimensions: dealership_group_name
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 20 → Extract only: per group — sold_count. Discard full response.

2. Same filters but ranking_measure: average_days_on_market, ranking_order: asc. Also pull average_sale_price. → Extract only: per group — average_days_on_market, average_sale_price. Discard full response.

3. Build a Dealer Group Stock Signal table:

   • Columns: Rank, Group, Ticker, Volume, Share %, MoM Change, ASP, DOM, Efficiency Score
   • Map to tickers: AN, LAD, PAG, SAH, GPI, ABG, KMX, CVNA
   • Signal per group based on volume trend and efficiency

4. Investment summary: "AN leads in volume but LAD has the best efficiency score. KMX and CVNA are gaining share in used-only segment — different business model dynamics."

Output

Present: competitive headline with tickers and share change in bps, ranked data tables with ticker mapping, comparison period trends, and investment implications by ticker (beneficiaries, at-risk, earnings impact).

Important Notes

• US-only: Requires get_sold_summary.
• 100 bps of national market share translates to approximately 15,000-17,000 annual units — always contextualize share changes in volume terms.
• A 50+ bps decline sustained over 2 quarters signals a structural issue for the OEM ticker.
• Always aggregate makes to the ticker level. An analyst evaluating GM needs Chevrolet + GMC + Buick + Cadillac combined, not individual brand views.
• For EV share analysis, contextualize TSLA's share loss against total EV market growth — TSLA can lose share while growing volume if the market expands faster.