Designing Near‑Guaranteed CTV Deals: The Sell‑Side Playbook for Dynamic Pricing and Predictable Delivery

Connected TV is a paradox for the sell side. Advertisers want TV-like certainty, quality, and storytelling. They also want digital-like flexibility, data, and price efficiency. Publishers and SSPs, meanwhile, need predictable revenue without boxing themselves into inflexible commitments, all while managing pod constraints, SSAI realities, and a fragmented app ecosystem. Near-guaranteed deals offer a pragmatic middle path. They aim to deliver forecastable impression volumes and pacing reliability without the operational rigidity of full programmatic guaranteed. Combined with dynamic pricing, pod-aware packaging, and strong supply-chain transparency, near-guaranteed deals can unlock premium budgets while preserving yield control. This article provides a sell-side blueprint. It explains the essential signals and standards, the packaging building blocks, the pricing logic, and the operational playbook needed to bring near-guaranteed CTV deals to life. It assumes you operate or partner with SSPs, publishers, or intermediaries working with long-form video and podded ad experiences, and that you prioritize privacy, transparency, and resource discipline. :cite[ekx,ib5,d0k,ajm,bfd,a2e]

What Do We Mean By Near‑Guaranteed?

Near-guaranteed is a sell-side construct that borrows the predictability and service-level intent of programmatic guaranteed while retaining degrees of flexibility typical of PMP or preferred deals. It is not a legal guarantee. Instead, it is a commercial framework with explicit reliability targets, smart buffers, and makegood logic, paired to dynamic pricing and pod-aware packaging. Think of it as a portfolio of commitments. You pre-commit delivery bands against precisely described CTV supply and content contexts, with dynamic price guardrails. You operate clear pacing SLAs, but you can expand or contract the delivery window, or switch to adjacent placements if conditions change. Near-guaranteed balances four forces:

Predictability: Buyers receive reliable delivery bands and forecastable reach across specific content, dayparts, and devices.
Quality: You protect pod experience, uphold competitive separation, and reduce invalid traffic via SSAI QA and supply chain transparency.
Flexibility: You dynamically optimize floors and reallocate supply across equivalent placements to stay on pace.
Profitability: You monetize premium contexts with slot-aware pricing while minimizing opportunity cost and makegood risk.

Why Now: The CTV Maturity Curve

As CTV programmatic spend grows, buyers are demanding both predictability and accountability from the supply side. OpenRTB 2.6 introduced pod bidding capabilities that finally reflect how TV breaks actually work. That, combined with improved CTV programmatic guidance, creates the conditions to operationalize near-guaranteed packaging that is understandable to buyers and technically sound for sellers. :cite[ekx,ib5] At the same time, standards like ads.txt and app-ads.txt, sellers.json, and SupplyChain object make provenance clear and enforceable, which is especially critical for multiscreen and app-based environments. :cite[d0k,ajm]

The Building Blocks: Signals, Standards, and Supply Hygiene

Near-guaranteed deals are only as good as the signals and the supply integrity behind them. Invest here first.

1) Pod-aware supply signaling

OpenRTB 2.6 enables structured, dynamic, and hybrid pod bidding. This lets media owners describe pod geometry and slot-level constraints rather than treating video like single-slot display. With pod features, you can set slot counts, duration flexibility, max ads per pod, and sequencing logic. This is foundational for consistent pacing and slot-specific pricing. :cite[ekx]

Structured pods: Fixed pod layout for breaks with known ad counts and positions.
Dynamic pods: Flexible pod length or composition where the seller retains optimization control.
Hybrid approaches: Mix fixed slots with flexible inventory for buffer management.

Pod-aware signaling reduces duplicate ads, supports competitive separation, and aligns trading to TV-like supply realities. :cite[ib5]

2) SSAI readiness and delivery integrity

Server-side ad insertion is common in CTV. Treat SSAI QA as table-stakes: ensure ad beacons, slot timings, and pod transitions are consistent. Maintain a clear incident playbook for live events, buffering, or server anomalies. While the details vary by tech stack, treating SSAI as a critical component of delivery integrity is mandatory for any reliability claim buyers will trust.

3) Supply chain transparency

Publish accurate ads.txt and app-ads.txt, with sellers.json alignment and consistent schain propagation. This reduces reselling confusion, eases SPO for buyers, and strengthens rate-card credibility. :cite[d0k,ajm]

ads.txt and app-ads.txt: Publicly declare authorized sellers. Keep it fresh, especially as apps move between stores or bundles change. :cite[d0k]
sellers.json: Identify the final seller and relevant intermediaries so buyers can validate provenance. :cite[ajm]
Supply chain object: Ensure consistent propagation across hops for transparent paths.

4) Pricing operability across systems

You will likely orchestrate floors across SSPs, Prebid, and ad servers like Google Ad Manager. Use Unified Pricing Rules to centralize floor strategy in GAM and reconcile them with Prebid’s Price Floors Module to avoid working crosswise. :cite[bfd,a2e]

Designing Near‑Guaranteed Deal Constructs

Near-guaranteed is not a single deal type. It is a packaging pattern built from well-understood constructs and standards.

Step 1: Start from a clear commercial intent

Define what you want near-guaranteed to achieve for the buyer and for your yield goals:

Intent: Predictable delivery bands with strong CTV context quality, at higher than open market CPMs.
Scope: Genres, tentpoles, or indexed title sets, with daypart and device filters.
Reliability target: For example, 95 percent of days within 5 percent of daily pacing target, with predefined makegood options.
Flex and buffer: Allow lateral movement across equivalent placements when a title slips or a live event runs short.

Step 2: Specify pod and slot realities

If you do not declare pod geometry and slot parameters, you cannot price slots correctly or pace predictably. Declare:

Pod length and slots: e.g., 3 to 5 ads per pod, 15 to 30 second durations.
Slot positions: A, mid, Z pricing tiers. Opening and closing slots often command premiums.
Competitive separation: Enforced via scheduling logic.
Podding type: Structured vs dynamic with clear fallback rules. :cite[ekx,ib5]

Step 3: Define dynamic pricing guardrails

Avoid a single hard CPM. Use guardrails and indexation:

Deal floor band: e.g., 28 to 36 USD CPM.
Slot multipliers: Opening slot +15 percent, closing slot +10 percent, mid slots baseline.
Daypart multipliers: Prime +20 percent, fringe minus 10 percent.
Context premium: High-attention sub-genres +15 percent, long-tail minus 10 percent.
Market index: Optional peg to recent bid landscape median for comparable pods.

Centralize guardrails in GAM UPR and align with Prebid floors to maintain coherence across pipes. :cite[bfd,a2e]

Step 4: Commit to pacing SLAs and makegood logic

Define reliability bands and what happens when you fall short:

Daily pacing SLA: Keep within a set band of daily target impressions, measured daily and aggregated weekly.
Makegood inventory: Pre-authorized adjacent pods, near-equivalent titles, or bonus spots at equal or greater quality.
Escalation windows: For live events, allow flexible windows on the night of event with next-day reconciliation.

Step 5: Agree on measurement and reporting

Specify log-level data needs, SSAI beaconing, and ad server records. Where possible, align on standard video metrics and reasonable latency constraints to avoid measurement drift.

Proof of delivery: Daily pacing reports with pod-slot breakdowns.
Log-level windows: Daily or 48-hour rollups for optimization and reconciliation.
Attribution pointers: Consistent content IDs, pod IDs, and device types.

The Dynamic Pricing Engine: How to Make It Real

Dynamic pricing is the heartbeat of near-guaranteed deals. It adapts floors and slot premiums based on real-time and historical signals to secure revenue while staying on track for delivery.

Inputs that matter

Bid density: Bids per request by pod and slot.
Win rate: Cleared deals vs bids for each slot type.
Content velocity: Expected impressions by title and daypart, including drop-offs and binge patterns.
Competition map: Overlapping deal obligations over the same supply windows.
SPO signals: Effective demand concentration by path to reduce channel conflict.

Optimization objective

Maximize revenue-per-available-minute subject to delivery reliability constraints and buyer experience constraints. That means floors move within bands to preserve pace targets without over-discounting premium moments or creating underdelivery risk in thinly traded slots.

Pseudocode: Dynamic floor suggestion

# Illustrative logic - adapt to your stack and data
def suggest_floor(pod_slot, daypart, genre, recent_bids, market_index, guardrails):
# Base floor from historical CPM for this context
base = historical_cpm(pod_slot, daypart, genre)
# Demand pressure factor
density = recent_bids['count'] / recent_bids['requests'] if recent_bids['requests'] else 0.0
pressure = 1.0 + min(0.3, max(-0.2, (density - 0.2)))  # clamp between -20% and +30%
# Market sanity check
indexed = 0.6 * base + 0.4 * market_index
# Slot multiplier
slot_mult = {'A':1.15, 'MID':1.0, 'Z':1.10}.get(pod_slot, 1.0)
# Daypart and genre multipliers
dp_mult = 1.2 if daypart == 'PRIME' else 0.9 if daypart == 'FRINGE' else 1.0
g_mult = 1.15 if genre in HIGH_ATTENTION_GENRES else 1.0
# Raw suggestion
raw = indexed * pressure * slot_mult * dp_mult * g_mult
# Guardrails
low, high = guardrails['floor_low'], guardrails['floor_high']
return round(max(low, min(high, raw)), 2)

This approach tolerates demand variability without causing pacing panic. It also forces you to encode multipliers you can defend in sales conversations.

Technical Integration: Practical Snippets

Below are illustrative snippets showing how to encode near-guaranteed logic into common sell-side tooling. Treat these as starting points.

Prebid: Price Floors Module configuration

Prebid’s module lets you define contextual floors and plug in external dynamic providers that return near-real-time floor updates. :cite[a2e]

// Example Prebid configuration
pbjs.setConfig({
floors: {
enforcement: {
enforceJS: true,
enforcePBS: true
},
data: {
currency: "USD",
skipRate: 0.05,  // allow 5% dynamic bypass if data stale
modelVersion: "rv-ng-2025-09",
schema: {
fields: ["adUnitCode", "adPodPosition", "deviceType", "daypart"]
},
values: [
{key: "*|A|ctv|PRIME", floor: 34.00},
{key: "*|MID|ctv|PRIME", floor: 30.00},
{key: "*|Z|ctv|PRIME", floor: 33.00},
{key: "*|*|ctv|FRINGE", floor: 26.00}
]
},
dynamic: {
enabled: true,
endpoint: "https://floors.redvolcano.ai/v1/floors",
timeout: 120
}
}
});

GAM: Unified Pricing Rules alignment

Centralize your guardrails in GAM so that direct, exchange, and header-bidding routes honor consistent floors. Use UPR to maintain a single source of truth for floor bands and multipliers. :cite[bfd] Suggested practice:

Create UPR by content group: e.g., Premium Series, Live Sports, Long-tail Movies.
Apply daypart and device splits: Prime vs off-prime, CTV vs mobile OTT.
Mirror Prebid keys: Keep naming consistent to avoid mismatches.

PMP deal object with pod-aware context

OpenRTB and SSP interfaces vary. The snippet below is illustrative only. It shows a PMP deal with a pod-aware extension and a slot multiplier hint.

{
"id": "bid-req-123",
"imp": [{
"id": "1",
"video": {
"mimes": ["video/mp4", "video/x-m4v"],
"w": 1920,
"h": 1080,
"minduration": 15,
"maxduration": 30,
"placement": 1,
"pos": 1
},
"pmp": {
"private_auction": 1,
"deals": [{
"id": "rv_ng_001",
"bidfloor": 30.00,
"bidfloorcur": "USD",
"at": 1,
"ext": {
"adpod": { "type": "dynamic", "minads": 3, "maxads": 5 },
"slot_hint": "A"   // Opening slot premium
}
}]
}
}],
"site": { "domain": "exampletv.com" },
"device": { "devicetype": 3 },
"tmax": 300
}

Note: Align with your SSP’s OpenRTB 2.6 implementation details for pod fields and ext semantics. :cite[ekx]

Inventory forecasting query sketch

Per-title and daypart forecasting improves pacing SLAs. A data warehouse friendly approach:

-- Illustrative forecasting
WITH hourly_impressions AS (
SELECT
title_id,
genre,
device_type,
daypart,
date_trunc('hour', event_time) AS hour,
COUNT(*) AS impressions
FROM ctv_impressions
WHERE event_date BETWEEN current_date - INTERVAL '28 days' AND current_date - INTERVAL '1 day'
GROUP BY 1,2,3,4,5
),
smoothed AS (
SELECT
title_id,
genre,
device_type,
daypart,
hour,
AVG(impressions) OVER (PARTITION BY title_id, daypart, EXTRACT(HOUR FROM hour)) AS expected_imps
FROM hourly_impressions
)
SELECT
genre,
device_type,
daypart,
DATE_TRUNC('day', hour) AS day,
SUM(expected_imps) AS forecast_imps
FROM smoothed
GROUP BY 1,2,3,4
ORDER BY day, genre, device_type, daypart;

Use this to pre-calculate capacity bands and inform deal sizing and pacing buffers.

Pacing For Predictability Without Overcommitting

Near-guaranteed pacing is a policy plus algorithm.

Policy: Commit a daily delivery band with explicit buffers and adjacency rights across equivalent supply.
Algorithm: Use hourly targets with recovery windows. If you undershoot for two consecutive hours, relax floors within guardrails and expand reach to adjacent contexts. If you overshoot, tighten floors in lower priority slots to preserve value.

A practical progression:

Start each day with a baseline floor and slot multipliers aligned to expected demand.
Monitor bid density and win rates. If density drops below threshold, unlock one adjacency at a time, not all at once.
Use micro-makegoods within the day first. Reserve next-day makegoods for genuine shortfalls beyond policy thresholds.
Publish a daily summary that shows how adjustments preserved reliability and value. The point is to author a transparent, predictable mechanism that buyers can trust and your team can run without heroics.
Sales Packaging That Buyers Will Actually Buy

Buyers want clarity and control. If you ask them to trust a new construct, give them a checklist and clear levers.
- Named packages: Near-Guaranteed Prime Drama, Near-Guaranteed Weekend Sports Highlights, Near-Guaranteed Family Co-View.
- Slot options: Opening only, Closing only, or Mixed with clear multipliers.
- Volume commitments: Weekly and monthly targets with daily pacing bands.
- Data overlays: Context and attention signals first. Complement with curated audiences where allowed and privacy-safe. :cite[ib5]
- Cancellation terms: Fair, with notice windows and in-flight reallocation rules.
Position near-guaranteed as programmatic certainty without the overhead of full PG. Highlight flexible buffers and dynamic optimization that work in the buyer’s favor when demand patterns shift.

Risk Controls Sellers Should Demand

Predictability is earned by how you manage known risks.
- Content schedule slippage: Maintain adjacency rules to equivalent titles and dayparts written into the IO.
- SSAI anomalies: Automate incident notifications with predefined makegood triggers.
- Floor miscalibration: Cap hourly floor volatility to avoid wild swings that harm pacing.
- DSP throttling and path quirks: Offer alternative routes for the same package to avoid a single point of failure.
- Pod duplication: Enforce dedupe and competitive separation at scheduling time, not just at auction.
Practical KPIs For Near‑Guaranteed Deals

You cannot manage what you do not measure. Focus on commercial reliability and user experience.
- Reliability: Percent of days within daily pacing band, on-time completion of weekly targets.
- Yield: eCPM delta vs open market, revenue-per-minute of pod time.
- Pod quality: Competitive separation violations per 1k pods, duplicate ad rate.
- SPO trust: Matched ads.txt and sellers.json coverage across inventory. :cite[d0k,ajm]
- Operational friction: Manual interventions per deal per week. Aim to drive this down via automation.
A Hypothetical Example

A mid-sized AVOD network wants to court CPG brand budgets ahead of a new series slate.
- They launch Near-Guaranteed Prime Drama with A-slot focus.
- Daily pacing band: 4 percent. Weekly buffer: 2 percent carryover.
- Floors: 32 to 40 USD CPM with A-slot +15 percent.
- Adjacency: If a new episode underdelivers, shift to the previous season’s best-performing episodes in the same hour, same device mix.
- Reporting: Daily pod-slot breakdown with log-level within 48 hours. Within two weeks, demand exceeds expectations in early episodes, but drops in week three. The dynamic pricing engine eases floors to the middle of the band for mid slots, maintains A-slot premiums for new release nights, and opens family co-view titles as an adjacency. The package ends the month 1.1 percent over weekly target, with a blended eCPM 17 percent above open auction benchmarks. The buyer renews with an increased slot commitment.
  How Red Volcano Can Help
  
  Red Volcano specializes in supply-side intelligence across web, app, and CTV. Near-guaranteed deals depend on accurate discovery, clean supply chains, and signal-level insights. Here is how our capabilities map to the playbook:
  - Publisher and App Discovery: Identify high-quality CTV apps, channels, and long-form contexts with robust taxonomy and pod potential.
  - ads.txt, app-ads.txt, sellers.json Monitoring: Track provenance drift and verify authorized sellers at scale for safer packaging. :cite[d0k,ajm]
  - Technology Stack Tracking: Detect SSAI providers, ad servers, and header bidding setups to anticipate signal capabilities and constraints.
  - CTV Data Platform: Enrich packages with content and device signals, daypart performance, and attention proxies to build compelling near-guaranteed proposals.
  - Sales Outreach Intelligence: Build target lists of buyers most likely to adopt near-guaranteed constructs based on historical PMP and PG behavior trends.
  We are not replacing your SSP or ad server. We are optimizing what you choose to trade by finding the right supply, verifying its integrity, and illuminating the levers that make near-guaranteed possible.
  
  Implementation Checklist
  
  A light but effective rollout plan:
  - Week 1 to 2: Readiness
    - Supply hygiene: Confirm ads.txt, app-ads.txt, and sellers.json alignment for target inventory. :cite[d0k,ajm]
    - Signal audit: Validate OpenRTB 2.6 pod features with partners and identify gaps. :cite[ekx]
    - UPR and Prebid floors mapping: Define a single floor source of truth. :cite[bfd,a2e]
  - Week 3 to 4: Pilot packaging
    - Package design: 2 to 3 near-guaranteed bundles by genre and daypart with clear slot pricing.
    - Operational SLAs: Codify pacing bands, adjacencies, and makegoods.
    - Reporting: Build daily pacing dashboard with pod-slot detail.
  - Week 5 to 8: Live pilots
    - Test diversification: One bundle per major DSP path to reduce single-path risk.
    - Observe volatility: Cap floor adjustments per hour and measure delivery stability.
    - Buyer feedback loop: Verify the value of pod-slot premiums and adjust multipliers.
  - Week 9 to 12: Scale or standardize
    - Expand SKUs: Add seasonal and tentpole packages with pre-set buffers.
    - Automate: Move dynamic floors from manual to ML-informed providers where feasible. :cite[a2e]
    - Document: Publish internal runbooks and external product one-pagers.
  Objections You Will Hear, And How To Answer
  - Is this just a PG rebrand? No. We maintain flexibility bands and dynamic pricing within agreed guardrails. You get predictability and we retain optimization agility.
  - How do I know you will not throttle to protect yield? We declare pacing SLAs and publish daily pod-slot performance. Adjacencies are pre-approved and logged.
  - Why pay a premium? You get slot-level quality, reduced waste from pod duplication, and fewer operational escalations. That is worth a premium over open auctions.
  - What if the content schedule slips? We define title equivalencies and daypart adjacencies in the IO. You are never stuck waiting for a single episode drop.
  Conclusion: Confidence Without Handcuffs
  
  The sell side does not need to choose between rigid guarantees and open-market chaos. Near-guaranteed CTV deals deliver confidence without handcuffs. They blend strong packaging discipline with pod-aware signals, dynamic pricing within clear guardrails, and transparent pacing SLAs. Start with supply integrity and standards. Use pod bidding and daypart-aware forecasting to describe inventory the way TV actually works. Introduce dynamic floors that move with real demand while honoring delivery commitments. Package in ways that buyers recognize and can measure. Then scale what works. The reward is predictable revenue and stronger relationships with buyers who value reliability and quality, along with the operational calm that comes from running a system you can explain, defend, and repeat. :cite[ekx,ib5,d0k,ajm,bfd,a2e]