Prove Your Marketing Works
From Impression to Purchase

Scenario

OEM spends $400M+ on digital ads but sees only 8% conversion via last-click attribution. Dealers track sales in isolated DMS systems with no visibility into which ad exposures preceded showroom visits. The true path from impression to test drive to purchase remains a black box, forcing budget allocation guesswork. Without proof of digital's real impact, half the budget languishes on unmeasured channels while competitors optimize aggressively.

How It Works

OEM sends hashed ad impression data (ad ID, timestamp, user hash via envelope encryption) and dealers upload anonymized DMS records (test drive date, purchase date, vehicle) to Placino. Using cryptographic matching and deterministic hashing (SHA-256 on identifiers), Placino correlates ad exposures to downstream test drives and purchases. A Private Set Intersection (PSI) query identifies which dealers' customers saw specific campaigns. The federated architecture keeps all raw data on-premises; only encrypted aggregates flow to the clean room. Merkle-chain audit trails document every query, proving attribution provenance to auditors.

Scenario

OEM allocates $75M in annual co-op funds evenly across 500+ dealerships—the fairest approach, but strategically blind. High-performing dealers get same funding as poor performers; weak campaigns persist because nobody sees the data. The OEM has zero visibility into which dealer marketing actually moves sales, creating a 80/20 waste problem where underperforming dealers burn co-op dollars on ineffective local campaigns.

How It Works

Each dealer hashes customer purchase records (VIN, purchase date, sale amount) and uploads with campaign spend metadata (campaign ID, cost, channel). Placino applies differential privacy with epsilon-budget controls (epsilon=1.5) to prevent dealer-level data leakage while preserving trends. Federated queries compute ROI by dealership and campaign type without centralizing PII. K-anonymity thresholds (k=5) ensure insights on campaigns with sufficient volume. Results are securely shared back to each dealer, showing where co-op spend generated sales and where it vanished.

Scenario

OEM has generic prospect lists from third parties but lacks real purchase-intent signals. Competitors' service chains hold golden data—recent service visits indicate active vehicle owners mid-lifecycle, likely considering upgrades—but sharing raw service records violates privacy and antitrust concerns. Without access to these intent signals, conquest campaigns target broad audiences with 12% engagement; the high-value competitive users remain invisible.

How It Works

Competitive service chains (Toyota, Honda, Ford dealers) hash customer service records (service date, vehicle model, service type) and upload to Placino without exposing customer identity. OEM initiates a Private Set Intersection (PSI) query: "Show me anonymized cohorts of recent service customers aged 35-55 who serviced vehicles 5+ years old in urban markets." Placino matches OEM's CRM (hashed customer profiles) against service cohorts via cryptographic matching, returning only aggregate segment statistics (e.g., "23K matched customers, avg service cost $450, 8x more likely to upgrade within 12 months"). No raw service records leave the service chains; zero customer names cross company boundaries. Envelope encryption (AES-256-GCM) protects query payloads in transit.

Scenario

OEM's connected vehicles generate 50M daily events (location, speed, acceleration, EV charging, braking patterns), capturing customer behavior worth tens of millions to insurers and media buyers. But sharing raw telematics violates privacy obligations; customers never consented to insurance underwriting or behavioral profiling. Today the data sits dormant—a $30M+ annual monetization opportunity lost. Insurance partners need cohort-level risk signals; media buyers need demographic + location segments. Without a privacy-safe mechanism, OEM can't unlock this market.

How It Works

OEM ingests connected vehicle telemetry into Placino's clean room (hashed vehicle ID, location, speed, charging pattern, NOT linked to customer name/email). Differential privacy (epsilon=2.0) adds statistical noise to preserve individual privacy while allowing aggregate analytics. Insurance queries: "Show me % of vehicles with hard-braking patterns in urban zip codes + age range" return only privacy-safe counts and aggregates. Media buyers query for "highway commuters 30-45, 50K+ income, suburbs of top-10 metros"—answered via federated computation using k-anonymity thresholds (k=10K minimum segment size). All queries audit-logged on Merkle-chain. Results are packaged as segments (no individual records) and licensed to data buyers. OEM never reveals which vehicles belong to whom.

Scenario

OEM and parts manufacturers send generic "your vehicle is due for service" emails to all owners quarterly—wasting spend on customers not yet at maintenance intervals. OEM knows vehicle age/mileage; parts manufacturers know service history and inventory. But sharing customer databases violates both privacy policies and competitive concerns. Without combined visibility, marketing hits 8% conversion—below profitability. Service intervals slip, warranty claims rise, and $12M in preventive-maintenance revenue vanishes.

How It Works

OEM uploads hashed vehicle IDs with age, mileage, and purchase-date data to Placino. Parts manufacturer contributes hashed service records (vehicle ID, last oil change, last brake service, recommended next intervals). Federated machine-learning model—operating entirely within the clean room—predicts which vehicles will need brake pads, tires, or oil changes within 30 days. Privacy guardrails: k-anonymity (k=50) ensures predictions apply to cohorts, not individuals; zero-data-movement architecture keeps raw records at source. OEM receives only encrypted segment IDs ("cohort-2847: needs brake pads in 7 days"). Marketing campaigns target segments by encrypted ID, never exposing which customers are actually in them. Conversion attribution flows back securely, showing parts manufacturer which service recommendations drove sales.