Dealer Targeting
Build buyer lists for upcoming auctions — score dealers by aging pressure, lot size, and inventory mix gaps
Purpose
Auction buyer attendance determines sell-through rate — and the best buyers are dealers who need the types of vehicles on the run list. Inviting every dealer in a state is noise; targeting dealers with aging lots, large inventory, and mismatches between what they currently stock and what the local market is buying is signal.
Dealer Targeting scores dealers in a DMA on three dimensions: how much inventory they have (volume = more originations), how aged their current lot is (higher DOM = more likely to need fresh inventory from auction), and how well their current inventory mix matches local demand (larger gap = more motivated to buy specific segments at auction). The output is a HOT / WARM / WATCH ranked list with specific buying needs per dealer.
How It Works
Execution flow. MCP tool calls are shown inline on each step.
search_active_carsCalls search_active_cars for the state with dealer_id facets to identify the top 50 dealers by unit count and their avg DOM.
get_sold_summaryCalls get_sold_summary by body_type for the state to understand what segments the market is buying — used to calculate each dealer's mix gap.
search_active_carsFor top 20 dealers, calls search_active_cars per dealer to get body_type distribution, avg DOM, and top 3 aged units.
Scores each dealer: DOM Score (0–40) = percentile rank by avg DOM × 40. Volume Score (0–30) = percentile rank by total units × 30. Mix Gap Score (0–30) = sum of |dealer_share - market_share| per body_type / 2 × 30.
HOT PROSPECT (70–100): high aging + large lot + mix mismatch. WARM (50–69): moderate signals. WATCH (30–49): low urgency but potential regular buyer.
MCP Tool Calls
| Tool | Calls | Purpose |
|---|---|---|
search_active_cars | 1 + up to 20 | Market-wide dealer scan, then per-dealer profiling |
get_sold_summary | 1 | Local demand by segment for mix gap calculation |
Example Output
DEALER TARGETING — Texas | March 2026 Building buyer list for upcoming auction ════════════════════════════════════════════ HOT PROSPECTS (Score 70+) Rank Dealer Name City Units Avg DOM Buyer Score Buying Needs ──── ────────────────────── ───────── ───── ─────── ─────────── ──────────────────────── 1 Premier Auto Group Dallas 342 61 days 94 Needs SUV (+18% gap) 2 Capital City Motors Austin 198 58 days 89 Needs Trucks (+14% gap) 3 Gulf Coast Pre-Owned Houston 287 54 days 84 Needs SUV, Sedan 4 Texas Select Cars San Ant. 176 52 days 79 Needs Trucks, SUV 5 Hill Country Autos Kerrville 143 56 days 74 Needs SUV (+21% gap) WARM PROSPECTS (Score 50–69) 6 Lone Star Auto Dallas 241 48 days 67 Regular buyer candidate 7 Alamo Used Cars San Ant. 132 46 days 61 Sedan inventory light [6 more warm prospects] DMA DEMAND SNAPSHOT (Texas, February) Top demand: SUV (38%), Truck (27%), Sedan (22%), Hatchback (8%), Other (5%) Hot segments: SUV and Trucks — feature prominently in run list OUTREACH RECOMMENDATIONS 1. Premier Auto Group — 342 units, 61-day DOM, needs SUVs → invite to SUV-heavy lane 2. Capital City Motors — needs trucks → invite if run list has F-150s/Tacomas 3. All 5 HOT prospects → personal outreach with segment-specific incentive SUMMARY 5 hot prospects | 11 warm | 8 watch list Top buying need across all prospects: SUV (4 of 5 hot prospects)
Cost Estimate
10 targeting sessions/month ≈ $0.50–$1.30
Limitations
- US (full) and UK (search_uk_active_cars — no demand data, mix gap skipped in UK).
- Mix gap calculation requires both dealer body_type distribution and market sold body_type data — skipped if state sold data is unavailable.
- Top 50 dealers by facet count — large states may have many more dealers; use ZIP + radius to narrow geographic targeting.
- Buyer score predicts likelihood to need inventory, not certainty — historical auction attendance history is not available in this data set.
More in the Auction House Plugin
Same Capability, Different Plugin
These skills share the same underlying methodology but are tuned for a different audience.