The Trade Desk + Agency-Managed Reach Buying
Direct CompetitorUse TTD's demand-side platform for programmatic CTV with traditional agency or in-house team managing reach curves, frequency, and creative
Limitations
- Reach curves don't answer 'what did this sell?' — no direct ROAS measurement
- Outcome attribution requires stitching third-party data sources (MMP + CRM + pixel)
- Weekly optimization cycles, not real-time bidding on business outcomes
- Shared ML models across all advertisers — not advertiser-specific
- ACR/cookie-based targeting becoming regulated (Kentucky, Texas lawsuits)
MNTN's Template-Driven Performance TV
Direct CompetitorRun performance TV through MNTN's simplified self-serve platform with pre-built templates for TV campaigns, targeting rules, and KPI tracking
Limitations
- Template-based optimization, not per-advertiser ML — one-size-fits-most approach
- Limited to MNTN's own inventory relationships (not true open CTV)
- 100+ KPIs but limited cross-screen attribution (TV to mobile/web weak)
- Best for mid-market; performance plateaus at scale
- Higher fees due to managed service model
Amazon DSP with First-Party Shopper Data
Direct CompetitorAdvertise on CTV inventory using Amazon's first-party retail data (purchase history, browsing behavior) for targeting and attribution
Limitations
- Locked to Amazon ecosystem — limited access to open CTV inventory (Netflix, Roku, Peacock)
- Amazon first-party data only works for retail/ecommerce; B2B/SaaS limited value
- Outpacing TTD in CTV growth (23% vs 14%) due to first-party advantage, but only for Amazon-scale advertisers
- Black box optimization — limited customization of bidding strategy
- Inventory bias toward Amazon properties
Google Display & Video 360 (DV360)
Adjacent SolutionManage CTV campaigns within Google's ecosystem via DV360, leveraging Google first-party data (GA, Gmail, Chrome) for targeting and measurement
Limitations
- Primarily a Google ecosystem play — limited to YouTube CTV and Google-connected inventory
- Depends on Google third-party cookies (sunsetting) and consent signals (SKAdNetwork-like limitations in Chrome)
- Shared ML models, not advertiser-specific optimization
- Attribution tied to Google conversion pixel — incomplete cross-platform view
- Not designed for true open CTV (Roku, MNTN, independent publishers)
Roku OneView Native Platform
Direct CompetitorBuy CTV directly through Roku OneView, leveraging Roku platform first-party data and native inventory prioritization
Limitations
- Roku-only inventory — no true open CTV access
- Recently pivoting to 'commerce engine' narrative (Roku Shop), diluting focus on performance measurement
- Limited cross-device attribution (Roku data only)
- Self-serve UX less mature than TTD or DV360
- Smaller user base means fewer case studies and slower product innovation
Manual IO Buying with Publishers Directly
Diy ApproachNegotiate insertion orders directly with publishers (Netflix, Amazon Prime Video, Peacock, Paramount, Disney+) for CTV inventory
Limitations
- Highly manual — weeks to negotiate IOs, no programmatic agility
- Limited targeting and data activation (publishers restrict data usage)
- No unified measurement across multiple publisher deals
- Minimum spend commitments, inflexible pricing
- Doesn't scale for growth companies needing to shift budgets weekly
DIY: Stitch Point Solutions (MMP + DSP + Measurement)
Diy ApproachCombine best-of-breed tools: Adjust/Branch for mobile, Trade Desk or DV360 for programmatic, Mixpanel/Amplitude for analytics, Rockerbox for attribution
Limitations
- High operational overhead — 4-5 vendors to manage, integrate, reconcile
- Attribution data lag (daily reconciliation, not real-time)
- Vendor finger-pointing on data discrepancies (no single source of truth)
- Requires senior data engineer / analyst headcount
- CTV-specific measurement gaps (cross-device attribution weak)
- $7.4B CTV fraud/inaccuracy problem — no single vendor owns fraud detection across all sources
Stay on Linear TV (Status Quo)
Status QuoContinue buying linear TV through traditional media buyers, avoiding CTV complexity entirely or treating CTV as experimental/testing channel
Limitations
- Linear TV inventory declining (cord-cutting accelerates) — reach erosion YoY
- No programmatic agility — yearly contracts, quarterly adjustments
- Limited outcome measurement — reach + frequency metrics only
- 45% of CTV budgets now coming from linear TV migration (IAB 2026) — missing growth
- Competitors adopting CTV gaining competitive advantage in targeting efficiency
Per-Advertiser Operational ML
Custom ML models trained on each advertiser's unique business outcomes (ROAS, CAC, LTV, conversion rate) rather than shared templates or cross-advertiser learning. Models compound with spend — the more data, the smarter the bidding.
Alternatives Lacking This
- MNTN (template-driven optimization)
- Roku OneView (platform-level, not advertiser-specific)
- TTD (shared ML across customer base)
- DV360 (shared Google ML models)
Evidence
Roadmap: CTV ROAS & Purchase Optimization Models (8.75/10, BUILD, Critical). CTV Signal Engine: PMP deals grew from 41% to 59%, favoring ML-driven optimization. Battlecard insight: 'Per-advertiser ML vs template optimization' — Moloco is what you move to when you outgrow MNTN.
Outcome-Based Bidding (ROAS/CPA, Not Impressions)
Bid on measurable business outcomes (revenue per impression, CAC, ROAS) instead of impression volume or reach curves. Automatically optimizes spend allocation toward highest-return inventory and audiences.
Alternatives Lacking This
- TTD (reach curves, not outcome curves)
- MNTN (KPI tracking, but not outcome-driven bidding)
- Google DV360 (conversion-based bidding, but limited cross-platform context)
- Direct IO (manual IO placement, no algorithmic optimization)
- Roku OneView (limited to Roku SKU/attribution context)
Evidence
Battlecard: 'Outcome-based bidding vs reach curves — TTD can't answer what did this sell?' Core elevator pitch: 'For growth marketers tired of defending CTV spend with reach curves, Moloco turns every impression into a measurable business outcome.'
First-Party Signal Ingestion (Not Dependent on Shared Identity Graphs)
Ingest advertiser-owned first-party data (CRM, CDP, purchase history, pixel events) directly into bidding without relying on third-party cookies or shared identity resolution. Native support for hashed emails, device IDs, and customer IDs.
Alternatives Lacking This
- TTD (relies on third-party cookies, ID5, Liveramp — increasingly regulated)
- DV360 (Google cookie-dependent, SKAdNetwork limitations)
- DIY approaches (need 3-4 vendors for signal stitching)
- Manual IO buying (no data activation at scale)
Evidence
CTV Signal Engine: ACR data regulations tightening (Texas AG lawsuits, Kentucky classifying ACR as sensitive data). Capability directly addresses $7.4B CTV fraud/inaccuracy problem by using first-party, auditable signals.
Cross-Screen Attribution (CTV → Mobile → Web → Offline)
Unified measurement framework that ties CTV impressions to downstream user behavior across mobile apps, web, and offline (ecommerce, app installs). Single source of truth for attribution, not multi-vendor reconciliation.
Alternatives Lacking This
- Roku OneView (Roku-only context)
- Amazon DSP (Amazon ecosystem bias)
- TTD (requires stitching third-party data)
- MNTN (TV→mobile weak, TV→web not native)
- DIY point solutions (vendor finger-pointing on attribution)
Evidence
Roadmap: Unified Cross-Screen Attribution Dashboard (8.5/10, BUILD, Critical). Battlecard: Moloco's ability to prove CTV drove app installs, web conversions, and offline purchases differentiates vs TTD (reach curves only).
Privacy-Resilient Targeting (No ACR/Cookie Dependency)
Bidding and targeting strategies that don't degrade when third-party cookies sunset or ACR data becomes regulated. Designed for a post-cookie future using contextual signals, content affinity, and first-party data.
Alternatives Lacking This
- TTD (cookie-heavy, cookie sunset = immediate impact)
- DV360 (Google 1P + 3P mix, sunsetting 3P)
- Roku OneView (ACR-based, exposed to regulatory risk)
- Manual IO (no data activation, so no ACR risk, but limited optimization)
Evidence
CTV Signal Engine: Texas AG lawsuits, Kentucky ACR classification as sensitive data. $7.4B fraud/inaccuracy problem exacerbated by low-quality ACR. Moloco's first-party signal + contextual approach future-proofs against regulation.
Biddable Open CTV Supply (Not Locked to One Walled Garden)
Access to 90%+ programmatically-transacted CTV inventory across independent publishers (Roku, Pluto, Tubi, The Roku Channel), AVOD platforms (Netflix, Amazon Prime Video, Peacock, Paramount+, Disney+), and direct publisher deals. Not dependent on any single platform's inventory.
Alternatives Lacking This
- Amazon DSP (Amazon ecosystem bias; limited Netflix, Roku, Peacock access)
- Roku OneView (Roku inventory + relationships only)
- Google DV360 (YouTube CTV + Google partners only)
- Manual IO (requires separate negotiations per publisher)
Evidence
CTV Signal Engine: 90%+ CTV transacted programmatically. Netflix opening 80% inventory to programmatic ($3B ad revenue). Amazon DSP growing 23% vs TTD 14% due to first-party data, but limited to Amazon ecosystem. Moloco's open supply advantage enables scale without vendor lock-in.
Self-Serve Performance UX (Not Agency-Gated)
Intuitive, performance-marketing-native dashboard where growth teams can launch, optimize, and measure campaigns in hours, not weeks. No agency dependency, direct control over bidding strategy, real-time performance dashboards.
Alternatives Lacking This
- MNTN (managed service model, higher fees, slower time-to-launch)
- TTD (enterprise-first UX, requires agency or senior technical operator)
- Manual IO (requires media buyer or agency management)
- DV360 (enterprise platform, steep learning curve for performance teams)
Evidence
Roadmap: Self-Serve CTV Campaign Builder (8.0/10, BUILD, Critical). Battlecard: Best-fit buyer profile is 'Head of Growth at scaling consumer brand, came up running mobile performance, fluent in ROAS/CAC' — they want to self-serve like they do mobile.
Measurable ROAS on CTV — Prove It Works
cap-outcome-biddingProof Points
- Roadmap: CTV ROAS & Purchase Optimization Models (8.75/10, BUILD, Critical) enables per-impression ROAS tracking
- Battlecard: 'For growth marketers tired of defending CTV spend with reach curves, Moloco turns every impression into a measurable business outcome'
- CTV Signal Engine: $38B US market, 70% of advertisers increasing spend 17% avg — measurement is the unlock
Compounding Performance (Models Improve with Spend)
cap-per-advertiser-mlProof Points
- Per-advertiser ML models improve with data — more spend = smarter bidding
- CTV Signal Engine: PMP deals grew from 41% to 59%, indicating advertiser preference for ML-driven optimization
- Battlecard: 'Per-advertiser ML vs template optimization — Moloco is what you move to when you outgrow MNTN'
Independence from Walled Gardens
cap-open-supplyProof Points
- CTV Signal Engine: 90%+ CTV transacted programmatically
- Amazon DSP growing 23% vs TTD 14%, but at cost of ecosystem lock-in. Moloco's open supply prevents single-vendor dependency.
- Netflix opening 80% inventory to programmatic ($3B ad revenue) — open platforms creating competitive advantage
Privacy-Resilient Future-Proofing (Regulate-Ready)
cap-privacy-resilientProof Points
- CTV Signal Engine: $7.4B CTV fraud/inaccuracy due to low-quality ACR data. Texas AG lawsuits, Kentucky classifying ACR as sensitive.
- TTD, DV360, Roku OneView all exposed to cookie sunsetting or ACR regulation. Moloco's first-party + contextual approach immune.
- Regulatory Watch pillar shows tightening ACR regulations (FCC, FTC, COPPA impacts)
Speed to Value (Hours, Not Weeks)
cap-self-serve-uxProof Points
- Roadmap: Self-Serve CTV Campaign Builder (8.0/10, BUILD, Critical) enables non-technical growth teams
- Battlecard: Target buyer is performance marketer (mobile-trained), expects self-serve UX like they know from mobile
- vs MNTN: Template model slower; vs TTD: enterprise UX requires expertise
Reduced Waste (ML Identifies & Avoids Fraud)
cap-first-party-signalProof Points
- CTV Signal Engine: $7.4B wasted on CTV fraud/inaccurate data — ML fraud detection is a differentiator
- First-party signal ingestion (auditable, known-quality) vs third-party ACR (low-quality, fraud-prone)
- Outcome-based bidding automatically deprioritizes high-fraud, low-ROAS inventory
Single Source of Truth for Attribution (No Vendor Finger-Pointing)
cap-cross-screen-attributionProof Points
- Roadmap: Unified Cross-Screen Attribution Dashboard (8.5/10, BUILD, Critical)
- DIY point solutions require 4-5 vendors + engineering overhead. Single source of truth reduces complexity.
- Battlecard: CTV measurability is the differentiator — unified attribution proves ROI vs reach curves
DTC / App-First Brands Scaling from Mobile to CTV
Characteristics
- Revenue: $5M-$250M+
- Primary KPI: Unit economics (ROAS, CAC, LTV)
- Marketing: Performance-led (came up on mobile / Facebook / UA)
- Growth stage: Series B-D or profitable SaaS
- Geographic: US, UK, expanding Australia/Canada
Why They Care
They speak ROAS/CAC natively. They outgrew MNTN (templates too constraining). They avoid TTD (agency dependency, reach curves instead of outcomes). They want self-serve mobile-like UX for CTV — Moloco is the CTV equivalent of Adjust/Branch for mobile.
Observable Signals
- High mobile app spend ($50K-$500K/month)
- Recent CTV experiments with MNTN or TTD (not satisfied)
- Team includes Head of Growth or VP Performance Marketing
- Using MMPs (Adjust, Branch) + cohort analytics (Amplitude, Mixpanel)
- Quarterly budget reviews with ROAS targets
Growth-Stage Companies with Performance Accountability
Characteristics
- Revenue: $10M-$500M
- Structure: CFO/board scrutinizes every channel's ROAS
- Budget: $5M-$50M annual marketing spend
- Team: CMO/VP Marketing + Performance team
- Industry: B2C SaaS, ecommerce, consumer apps, fintech
Why They Care
Every media dollar must be defensible in board meetings. CTV reach curves are indefensible. Moloco's outcome-based bidding + unified attribution = board-ready story ('CTV drove $X revenue').
Observable Signals
- Monthly / weekly performance reviews (not quarterly)
- CFO or board rep attends marketing meetings
- Recently reallocated budget away from low-ROAS channels
- Using marketing analytics platforms (Rockerbox, Contentsquare, etc.)
- Questions like 'What's the ROAS on CTV?' in early sales conversations
Linear-to-CTV Migrators Needing Measurement
Characteristics
- Revenue: $100M+
- Marketing: Media buyer team managing linear TV budgets ($5M-$100M+)
- Challenge: Cord-cutting eroding linear reach; board wants CTV allocation
- Geography: Primarily US, CPG, QSR, automotive, financial services
Why They Care
Linear TV is declining (cord-cutting accelerates). CTV is 45% of new budget growth (IAB 2026), but media buyers are trained on reach/frequency, not outcomes. Moloco bridges: gives media teams a performance framework they can defend to brands.
Observable Signals
- Large legacy media buyer team ($2M-$10M headcount)
- Recent linear TV budget cuts or shift-to-CTV directive from brand
- Using Nielsen/comScore for measurement (looking to modernize)
- Questions about 'how to measure CTV' in discovery calls
- Brand side: CPG, QSR, auto with $50M+ linear budgets
Commerce Media / Retail Brands with Owned Audiences
Characteristics
- Revenue: $500M+
- Competitive advantage: First-party customer data (CRM, purchase history, browsing)
- Marketing: Retail marketing director managing owned customer spend
- Channel: Direct-to-consumer (DTC brands with scale)
Why They Care
Moloco's first-party signal ingestion enables them to activate owned audiences on CTV — drive repurchase, upsell, LTV. They have the data asset (Amazon, Shopify), but can't activate it at scale on CTV. Moloco unlocks owned-audience CTV strategy.
Observable Signals
- CRM with 1M+ customers
- Allocation to 'owned audience' or 'loyalty' marketing channel
- Exploring third-party CTV (not exclusive to Amazon DSP/Roku if they're already using it)
- Data team (or CDO) in organization
- Questions about 'how to use our customer data for CTV' in conversations
Performance Agencies Managing Multi-Channel Budgets
Characteristics
- AUM: $50M-$1B+
- Specialization: Performance marketing (not brand/creative focused)
- Client base: DTC, ecommerce, mobile-app, fintech brands
- Team: Performance strategists, paid media specialists, data analysts
Why They Care
Agencies can differentiate from competitors by offering CTV as a performance channel (not reach/frequency). Moloco enables white-label or Moloco-powered CTV services; agencies charge premium for outcome-based optimization vs TTD reach-buying.
Observable Signals
- Already managing mobile/paid social/search for clients (ROAS-driven)
- Client request: 'Can you do CTV?' or 'Add CTV to our media mix'
- Frustration with TTD (requires agency support, less self-serve)
- Interest in proprietary optimization (differentiation from other agencies)
- Team capacity to build CTV expertise
Performance CTV
GrowthPositioning Statement
Moloco is the performance CTV platform that turns connected TV from a reach play into a measurable, outcome-driven business outcome channel. Unlike reach-based alternatives (TTD, Roku, manual IO), Moloco optimizes CTV for ROAS, CAC, and LTV using per-advertiser ML and unified cross-screen attribution — giving growth teams and performance agencies the measurement certainty they need to defend CTV spend.
Performance CTV is the emerging segment of connected TV advertising focused on measurable business outcomes (ROAS, CAC, LTV, incrementality) rather than traditional TV metrics (reach, frequency, impressions). It combines operational ML with cross-device attribution and first-party signal activation to enable performance marketing teams to treat CTV like mobile/paid social/search — where every dollar spent is tied to a measurable business outcome.
Adjacent Categories
- Programmatic Advertising
- Marketing Attribution & Measurement
- Cross-Device Advertising
- ML-Powered Bidding
- Privacy-Resilient Advertising