Mary Gabrielyan
October 6, 2025
18
minutes read
Think of OLV advertising as the connective tissue in a modern video plan. It complements OTT and CTV, fills in daytime and mobile viewing, and gives you the data to refine quickly. We’ll show the how.
Online video advertising (OLV) represents far more than moving images on screens. It's become the primary vehicle for brand storytelling, customer engagement, and measurable business outcomes across every device consumers use.
Unlike traditional TV commercials that broadcast to passive audiences, OLV advertising creates interactive experiences that follow viewers from their morning smartphone scroll to their evening tablet browsing, collecting performance data and optimizing delivery at every step.
The format's rapid ascent stems from a fundamental shift in how people consume content. Viewers now expect video everywhere: embedded in articles, auto-playing in social feeds, streaming on connected TVs. This omnipresence gives advertisers unprecedented opportunities to reach audiences with relevant messages at precisely the right moments.
As programmatic technology advances and first-party data strategies mature, OLV advertising has evolved from experimental budget line item to essential marketing channel.
This article explains how OLV marketing fits with OTT and CTV, the formats that matter, how campaigns are set up and optimized, and the practices that consistently drive results in the U.S. market.
Online video (OLV) advertising is the placement of video ads across internet-delivered environments such as websites, mobile apps, social platforms, and streaming services when viewed off the television screen. In practice, OLV covers in-stream ads that run before, during, or after a piece of video content; outstream units that play within articles or feeds; social video ads; and newer interactive or shoppable video formats.
OLV is often discussed alongside OTT and CTV, but they are not the same thing. OTT describes internet-delivered video content, while CTV refers to the connected television device on which that content is watched. OLV typically describes digital video ads on desktop and mobile, and it complements CTV in cross-screen plans.
💡 For a deeper dive on CTV, see Connected TV advertising.
Marketers pick OLV because it scales across screens, supplies granular controls (targeting, frequency, creative), and proves impact with near-real-time metrics. U.S. investment has followed that logic: industry reports show digital video capturing a majority share of TV/video budgets in 2025, while streaming usage set new highs—expanding the pool of ad-supported viewing that online video can reach.
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OLV reaches people on phones, tablets, and computers, and complements CTV to build unduplicated reach across devices.
Research shows that campaigns using multiple screens see 41% better recall and 13% higher purchase intent compared to single-platform approaches.
In May 2025, streaming surpassed the combined share of broadcast and cable for the first time, underscoring where viewing time is shifting. Cross-media measurement initiatives (e.g., Nielsen ONE) are designed to de-duplicate audiences across publishers and platforms so planners can manage frequency and incremental reach with confidence.
📍 What this means for plans: pair OLV (desktop/mobile/app) with CTV to extend reach beyond the living-room screen, manage frequency across touchpoints, and pick up audiences that stream on mobile at different dayparts.
Video commands attention in ways static formats cannot match. Eye-tracking studies reveal that YouTube mobile ads capture 83% of viewers' visual attention, compared to just 45% for television ads. This heightened focus translates directly into memorability, with approximately 80% of users able to recall a video ad they've seen in the past 30 days.
The engagement advantage extends beyond mere viewing. Social videos generate 1,200% more shares than text and image posts combined, turning viewers into voluntary brand ambassadors. This organic amplification occurs because video content triggers emotional responses more effectively than other formats. Motion, sound, and narrative combine to create experiences that viewers remember and discuss.
📍 Practical takeaway: use short, front-loaded cuts for mobile and test interactive elements (end cards, overlays, product modules) where inventory allows to lift view-through and engagement rates.
OLV supports sequencing—showing people a planned series of short videos that build a narrative across exposures. YouTube and DV360 document sequence campaigns that let brands introduce, reinforce, and then prompt action with creative steps tailored to each stage. Advertisers use this to adapt the arc (e.g., teaser → product proof → offer) while controlling order, pacing, and audience rules.
📍 Tip: build modular assets (6s, 10–15s, and 30s) that can be arranged into sequences for prospecting, education, and retargeting without reshoots.
Video buyers now rank business outcomes (e.g., sales, store visits) as the top KPI—pushing OLV toward biddable, test-and-learn execution with clear success criteria.
IAB’s 2024–2025 work documents this KPI shift and continued double-digit growth for digital video, reflecting confidence in measurable performance.
Verification and measurement guidelines from IAB/MRC standardize viewability and completion reporting across web, app, and OTT.
💡 Use Elevate for outcome-based setup and optimization: AI planning builds the plan, predictive forecasting sets budgets, Impact Score and custom-KPI optimization shift spend to what works, and Ask Elevate explains the why.
📍 How to act on it: define primary outcomes (conversions, lift) plus early signals (VTR quartiles, CTR), set learning budgets for week-one experiments, and shift spend quickly to winners.
OLV makes sequential messaging straightforward. Official Google documentation confirms you can build remarketing lists from YouTube viewers, past site visitors, and Customer Match files, then serve tailored video ads to those audiences. This supports flows such as “watched but didn’t visit,” “visited but didn’t add to cart,” or “abandoned cart.”
📍 Execution notes: maintain recency windows (e.g., 7/14/30 days), cap frequency by segment, and rotate creatives so repeat viewers see the next message in the sequence.
Well-designed OLV programs can move both brand and sales outcomes. In one large meta-analysis of nearly 30 Nielsen Catalina Solutions sales-lift studies, L’Oréal USA reported an average 6% sales lift tied to YouTube activity, alongside a 6:1 ROAS on YouTube CTV campaigns.
Separate Ipsos work with Google shows YouTube CTV driving recall, consideration, and offline action—evidence that cross-screen video can influence upper- and lower-funnel metrics.
📍 Bottom line: treat OLV as a full-funnel lever—optimize early to attention and completion, then validate with incrementality studies (brand lift and conversion lift) as the campaign scales. business.google.com
A single OLV plan can use several formats, each built for a different job. In-stream rides alongside video content for scale and completion, outstream adds reach in articles and feeds, social video taps native engagement, native video blends into editorial, and shoppable units pull viewers straight into commerce. Start with your goal, then match formats to attention, interactivity, and the screen you’re buying.
In-stream video runs inside a player alongside editorial or entertainment content:
Units can be skippable or non-skippable, with common lengths of six, 10–15, and 30 seconds.
Use in-stream when you want the context of premium content, strong completion rates, and broad reach.
📍Best practices: hook viewers in the opening seconds, brand early and naturally, and align the cut to the placement (shorter for skippable pre-roll, slightly longer for mid-roll where attention is steadier). Track quartile completions, view-through rate, and frequency by placement.
Outstream video renders outside a traditional video player—inside articles, feeds, and content modules on publisher pages and apps. It typically autoplays muted when in view and pauses when scrolled out of view.
Outstream is useful for incremental reach on the open web where publishers don’t have their own video content. Creative needs to earn attention quickly without relying on sound; subtitles and punchy supers are essential.
Optimize toward viewability and completed views, and set sensible viewability thresholds in your buys.
Social placements (e.g., in-feed, Reels, Shorts, Stories) blend into the user’s feed and benefit from rich audience targeting and engagement signals.
Plan for mobile-first delivery with vertical or square aspect ratios, bold visuals, and captions. Lean on platform-native calls to action (swipe, tap, shop, learn more) and expect quicker creative fatigue—rotate variants and refresh regularly.
Measure beyond clicks: use view-through events, add-to-carts (where available), and on-platform engagement to identify early winners.
Native video matches the look and feel of the surrounding environment, appearing as a sponsored post or content card rather than a traditional ad slot.
The advantage is lower disruption and higher relevance; the trade-off is that the creative must carry the story without heavy framing.
Keep headlines clear, thumbnails purposeful, and disclosures compliant.
💡 For a deeper treatment, see Native advertising: a complete guide for marketers in 2026.
Shoppable formats connect viewing with buying by surfacing product pins, carousels, or overlays that open a product detail page or checkout flow. They work well for retail, beauty, home, and other visually led categories.
Use tight framing on the product, concise benefit copy, and a decisive call to action. Ensure landing pages mirror the creative (same product, price, and offer) to reduce drop-off. Track product interactions, add-to-cart rate, and conversion alongside video metrics to judge true performance.
Below is the OLV framework explained as a system: what happens inside the pipes, how platforms make decisions, and how signals flow from an impression to a business outcome.
Before you launch, every setup choice becomes a rule or a signal the system will use. Objectives turn into optimization targets, audiences become eligibility filters, and creative specs define which assets can serve. Here’s how those inputs translate into delivery decisions.
When you pick an objective (for example, completed views or site visits), the buying platform maps that choice to an optimization target. Under the hood it trains or applies a predictive model that estimates the probability of that outcome for each future impression. Your early indicators—quartile completions, view-through rate, click-through rate—become training signals and guardrails. They also inform pacing, so the system can spend at a steady rate while chasing the cheapest path to your goal.
Your audience definition compiles into a set of allow lists and deny lists: geos, devices, OS versions, app/site categories, content labels, and data segments. At bid time, the DSP checks the incoming opportunity against these rules and only scores impressions that qualify. If you plan OLV to complement CTV, the system also consults frequency logs to avoid over-exposing the same people or households.
Choosing skippable in-stream, non-skippable, outstream, social, or shoppable isn’t just creative preference. Each format arrives with different auction dynamics, floor prices, and measurement capabilities.
For example, non-skippable mid-roll in long-form content often has higher floors and higher completion probability. Outstream is abundant but viewability-gated. Those supply-side realities affect the model’s expected value calculation.
When you upload 6s, 10–15s, and 30s cuts in multiple aspect ratios, the ad server transcodes them into renditions for various players and bandwidths. Captions are attached as VTT files for silent autoplay. The system stores “decision metadata” such as which asset is eligible for which placement, the skip offset, and whether a companion banner or end card can be shown.
Your A/B plan creates traffic splits that route a fixed share of eligible impressions to each variant. Most platforms run a short learning phase, then reallocate budget to winners using simple bandit logic or similar heuristics. Pre-defining promotion and pause rules prevents the model from chasing noise.
Brand-safety tiers, publisher allow lists, and viewability targets are enforced twice: once before bidding (hard eligibility) and again after delivery through verification. Frequency caps live in the DSP and ad server; both keep counters keyed to identifiers such as cookies, device IDs, IP+UA, or publisher IDs on CTV. Pacing uses a target delivery curve so you do not overspend in the morning and starve the evening.
Pixels and SDK events are embedded in VAST tracking URLs. They fire at start, first quartile, midpoint, third quartile, and complete. Separate beacons capture viewability via OMID (Open Measurement SDK). Conversion pixels on your site or app pass back events with timestamps and anonymized IDs so the platform can link exposures to outcomes within your chosen windows.
Targeting defines who and where you’re willing to show up; bidding sets how hard you compete for those moments. Together they determine eligibility, price, and priority in the auction. Here’s how that plays out from request to render.
A user opens a page or app. The video player requests an ad from the publisher’s ad server or SDK. The supply-side platform (SSP) packages a bid request that includes context (site/app, content category), device details, rough location, predicted viewability, and any allowed user identifiers. It attaches a floor price and any private-deal terms.
Your DSP receives the request and runs a fast pipeline:
The exchange runs a first-price auction in most markets today. If you win, the player receives a VAST response or an ad URL. Playback begins, and skip logic activates at the configured second for skippable units.
Prospecting relies on contextual signals (page topics, app categories, channel metadata), modeled interests, and first-party lookalikes. Retargeting uses viewer lists (watched X% of a video), site pixels (visited a product page), or customer files to reach people who already signaled intent. The DSP treats retargeting as higher-probability traffic and will often bid more because the predicted value is higher.
Open auctions maximize reach. Private marketplace (PMP) deals and programmatic guaranteed (PG) give priority access, consistent pricing, and stricter quality controls. A deal ID in the bid request changes the auction path and can bypass some open-market competition.
When the campaign goes live, each impression returns signals the optimizer can learn from—play time, viewability, clicks, conversions. Those signals drive bid updates, creative rotation, and supply choices. Here’s how that feedback loop works in practice.
Each impression generates logs: whether it started, how far it played, if it was viewable, whether it was clicked, and if a conversion followed. The optimizer aggregates these signals by supply path, audience, and creative. It then updates bid multipliers. High VTR on a certain publisher raises bids there; poor mid-roll completion triggers a down-weight. If a creative underperforms after a minimum impressions threshold, the rotation shifts toward stronger assets.
Skip behavior is a powerful negative signal. If early drop-off spikes, the optimizer lowers bids on similar contexts or devices and your team should adjust the opening: tighter framing, earlier branding, fewer words on screen, simpler motion. Shorter durations also change the expected completion curve, which improves predicted CPCV.
The platform tracks how outcomes change as frequency climbs. When the curve flattens for a segment, the system reduces caps or shifts spend to new supply. On CTV, where ads are often unskippable, caps are conservative to avoid annoyance; OLV can tolerate higher caps because people can scroll or skip, but you still want diminishing-return protection.
Supply path optimization prefers direct and transparent routes to the same inventory to reduce fees and fraud risk. If specific apps or placements chronically miss viewability or VTR targets, they are excluded. Viewability thresholds can be raised mid-flight. If a campaign is under-delivering, the buyer can open additional formats or activate a PMP to stabilize volume.
Once a viewer completes Ad A or watches beyond a set quartile, the DSP adds them to a list eligible for Ad B. Recency windows ensure the follow-up runs soon enough to feel connected. This is how you progress from teaser to proof to offer without manual trafficking.
OMID signals confirm the ad was actually in view. Invalid-traffic filters look for bots and mismatched human behavior. Brand-safety classifiers scan page or video metadata to block unsuitable content. These checks run alongside delivery, and their outcomes are factored into future bidding.
Measurement translates exposure into evidence: first confirm what was actually seen, then connect exposures to outcomes across screens, and finally assign credit. That sequence starts with consistent definitions.
The player fires standardized VAST beacons at start and each quartile. Viewability is measured by an SDK sitting in the player that confirms how much of the video was on screen for how long (for example, MRC’s guideline of ≥50 percent in view for ≥2 seconds on web). On CTV and SSAI (server-side ad insertion), measurement uses server beacons and watermarking to approximate the same events.
Clicks are straightforward: a redirect tags the session, then your site conversion pixel closes the loop. View-throughs use impression logs joined to conversion logs by an identifier or clean-room match within your attribution window. If cookies or mobile IDs are sparse, platforms lean on publisher-provided IDs, login matches, or household-level keys.
Vendors build device graphs that connect phones, tablets, laptops, and TVs to a likely household or person using login data and other deterministic signals. Reporting then removes overlaps so you see true reach at your chosen frequency across OLV and CTV, rather than double-counted exposures.
Lift tests hold out a comparable audience that does not see your ads. Differences in outcomes—surveyed awareness, conversions, sales—estimate causal impact. For large programs, geo-experiments randomize markets and read sales or site traffic directly. Clean rooms enable matchbacks with retailers or publishers while keeping user-level data private.
Rule-based models (last click, time decay) are simple but can under-value video. Data-driven models use path data to estimate each touchpoint’s contribution. For long sales cycles, marketing mix modeling adds a top-down lens that accounts for seasonality and non-digital channels, then you reconcile the two views.
Post-campaign, platforms export performance by creative, audience, and supply path. You capture pattern-level learnings, feed them into the next flight’s targeting and creative brief, and the optimizer starts with better priors.
That is how OLV improves over time: the system continuously converts your goals and signals into bid decisions, and your team continuously supplies clearer goals and better signals.
The OLV stack has two halves: where ads appear and how they get there. Platforms like YouTube, Meta, TikTok, and publishers supply the reach; programmatic tools route, verify, and optimize delivery using shared standards (VAST, OMID) and privacy-safe identity. Start with the major surfaces, then map the pipes that connect them.
Below is a quick read on the largest surfaces and what they’re best at—from broad, brand-safe long-form to mobile-first short-form and context-rich publisher inventory.
💡 For a deeper dive into how automation is changing planning and bidding, see AI in DSPs: How Artificial Intelligence Transforms Programmatic Buying.
Programmatic connects sellers and buyers in real time. Think of it in two halves: the supply side, where publishers package impressions, and the demand side, where DSPs evaluate, bid, and deliver.
Optimization. As impressions accrue, the DSP shifts budget toward higher-quality routes (supply-path optimization), better-performing audiences, and stronger creatives. Frequency caps and sequencing rules keep exposure efficient, while pacing prevents end-of-flight spikes.
These are monetization models for OTT/streaming services, not OLV platforms themselves. They matter because they determine where ads can run and how much ad inventory exists—especially in ad-supported environments.
PVOD (premium video on demand). Higher-priced early-window rentals or purchases. Typically ad-free.
These terms overlap, but they describe different things: OLV is a digital ad placement context, OTT is a content delivery method, and CTV is a device category. The same ad can be counted in more than one bucket depending on where and how it’s viewed.
Let’s define each clearly, then map where they overlap:
How they intersect:
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Now, let’s start with the screen: define CTV first, then contrast it with OLV’s off-TV delivery and interactivity.
Rule of thumb with examples:
Most plans use both: CTV for big-screen impact and reach; OLV for incremental reach, interactivity, and retargeting between exposures.
💡 For details on big-screen buying and measurement, see Connected TV advertising.
Use these as a pre-flight checklist and a week-one playbook. They focus attention, keep creative tight, and make optimizations faster.
Attention drops fast, so make the first seconds work: front-load branding, a single message, and a clear next step. Six-second bumpers and 10–15s cuts consistently punch above their weight for recall and incremental reach.
In Google’s U.S. analyses, 70% of bumper campaigns lifted brand awareness (avg. +9%), and 9 in 10 drove ad recall; pairing short and longer formats further improves lift.
📍 Practical bar: produce modular edits (6s / 10–15s / 30s). Use aspect ratios by placement (9:16 vertical, 1:1 square, 16:9 widescreen). If skip rates spike, re-cut openings or tighten pacing, and retest within 48–72 hours.
Plan assets for vertical viewing and subtitles (sound-off). Treat OLV as the workhorse across phones and laptops, with CTV as a reach multiplier at higher frequencies.
Recent measurement shows streaming adds unduplicated reach to TV:
📍 Tip: build a cross-screen frequency strategy (e.g., 1–2 exposures on CTV, 3–4 on OLV) and manage caps by audience stage.
Spell out the action and make it visible on screen and end cards (e.g., “Shop the collection,” “Get a quote,” “Book today”).
On YouTube, creative that follows the ABCD principles (Attention, Branding, Connection, Direction) delivers ~30% higher sales lift than creative that does not.
📍 Map CTA language to your objective (view, visit, lead, sale).
Sync hashed CRM/site lists for suppression, reacquisition, and lookalikes. Use AI-assisted bidding where available to optimize toward business outcomes.
Example: Omnicom’s AI bidding program reported +567% video ROAS and +153% YouTube ROAS for a large client when optimizing to offline sales signals.
💡 Related reading: Marketing strategies for economic volatility.
Lock a learning tranche (10–20%) to A/B openers, CTAs, and lengths. Read early indicators (quartile completion, VTR, CTR, CPCV/CPV) by creative and audience, then promote winners.
Sequencing works: Google’s own video sequencing tests show 80–110% higher brand lift and 50–70% lower cost per lifted user vs. non-sequenced approaches.
📍 Use built-in Brand Lift and third-party sales-lift to validate impact.
Checklist you can apply next flight:
☑️ Ship a modular asset kit (6s / 10–15s / 30s) with captions and visible CTAs.
☑️ Cap frequency by stage; plan OLV + CTV for deduplicated reach.
☑️ Start with first-party seeds; expand with lookalikes and contextual packages.
☑️ Reserve 10–20% for creative and audience tests; decide promotion rules upfront.
☑️ Instrument Brand Lift and third-party sales-lift (e.g., NCS, Circana) to prove incrementality; fold results into MMM. (Circana added coverage for Shorts and In-feed in 2025.)
OLV advertising gives marketers a flexible way to reach people across phones, tablets, and computers while pairing neatly with the big-screen impact of CTV. It isn’t the same as OTT or CTV, but it complements both: OLV brings clickable formats, granular targeting, and rapid optimization; OTT/CTV bring premium, lean-back viewing and high completion. The right mix depends on your goal (reach vs. response), budget, and audience—plus how you plan to measure outcomes across screens.
As streaming usage grows and buying becomes more automated, OLV will take an even larger role in delivering measurable, high-impact video experiences.
💡 If you’re exploring the next wave of automation in TV and video, see The rise of AI in TV advertising.
If you want a partner to plan, activate, and prove impact across OLV and CTV, we can help:
Ready to build a cross-screen plan that proves outcomes? Reach out to AI Digital—we’ll design the OLV/CTV mix, orchestrate the right supply, and instrument lift and attribution so you can scale what works.
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Inefficiency
Description
Use case
Description of use case
Examples of companies using AI
Ease of implementation
Impact
Audience segmentation and insights
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Not typically. Netflix is an OTT streaming service. Ads on its ad-supported tier are OTT, and when viewed on a smart TV they’re counted as CTV. If watched on a phone or laptop, they behave like digital video, but most plans still categorize Netflix as OTT/CTV rather than OLV.
Both—device decides. YouTube on a TV app is CTV (and OTT). YouTube on desktop or mobile is OLV. Many plans split reporting by device for that reason.
Often, yes. Skippable in-stream is common, but there are also non-skippable 15s (and 6s bumper) units. Outstream usually autoplays muted and is “scroll-skippable.” Social video skippability varies by placement.
In-stream (pre-roll, mid-roll, post-roll), outstream (in-article/in-feed), social video (Reels, Shorts, Stories), native video, and shoppable/interactive video, plus 6-second bumpers and vertical/square cuts for mobile.
It can be. OLV typically delivers lower CPM/CPV than premium CTV and supports clicks, retargeting, and rapid optimization. Cost-effectiveness hinges on creative quality, targeting, frequency control, and clear outcome measurement (e.g., CPCV/CPA/ROAS).
Recent, well-documented OLV examples include L’Oréal USA’s meta-analysis of ~30 Nielsen Catalina Solutions sales-lift studies, which found ~6% average sales lift from YouTube activity and ~6:1 ROAS on YouTube CTV screens; DocuSign’s CTV-dedicated YouTube campaign that delivered +33% ad-recall lift and +126% conversion lift; and Adidas’s YouTube sequencing work showing +33% brand awareness and +317% product-interest lift.
The OLV acronym stands for online video; in advertising, “OLV advertising” means video ads delivered on web and app environments (desktop, mobile, social, and streaming off-TV).
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