Paid Media Strategy

Paid Media Strategy

A senior performance marketer's playbook for running paid media that produces real outcomes.

The default state of paid media is wasted spend. Most accounts have campaigns running because they always have, audiences targeting because the rep suggested it, bid strategies on auto because manual is hard, creative not refreshed because there is no system. The cost compounds. A 20% efficiency gain on a $500K-per-year account is $100K back to the business. A 50% gain on a $5M-per-year account is $2.5M.

This skill is the discipline that produces those gains. It assumes you have a paid media platform (Google Ads, Meta, LinkedIn, TikTok, or aggregators like Synter) connected. It assumes you have working analytics and conversion tracking. The hard part is the strategic discipline behind the spend, and that is what is here.

When to use this skill: any time you are designing a paid media plan, evaluating whether to scale or kill a campaign, allocating budget across channels, or auditing an existing account.

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What this skill is for

This skill spans paid media strategy and operations. It does not cover ad creative production (use ads-creative-development), result interpretation in depth (use ads-performance-analytics), or platform-specific MCP tooling (consult each ad platform's official documentation for current MCP setup, auth, and example prompts).

The audience is a performance marketer (in-house or agency), a growth lead allocating spend across channels, or a founder making early paid budget decisions. The voice is tactical. There is no "evaluate every option yourself with no opinion." Paid media decisions have shape, and a senior practitioner can map a situation to a defensible plan in an afternoon.

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Hypothesis discipline for paid spend

Most paid media failures start with a vague reason for spending. A real spend hypothesis has five parts: audience, offer, channel, outcome metric, and magnitude. Missing any of them and the campaign cannot be evaluated honestly.

A bad reason: "We need to scale Google Ads spend." No audience, no outcome metric, no magnitude. Nothing is falsifiable.

A good hypothesis: "Top-of-funnel SaaS prospects searching for project management tools convert from a free-trial CTA at 3.2% CAC under $80. Increasing Search budget from $40K to $80K per month should hold CAC under $80 and add roughly 500 trial signups based on Q3 search volume."

That hypothesis names the audience (top-of-funnel SaaS prospects on PM-tool keywords), the offer (free trial), the channel (Google Search), the outcome metric (CAC, trial signups), and the magnitude (500 signups, CAC under $80). It is falsifiable: if CAC blows past $80 or signups come in below 250, the hypothesis is wrong and you pull back.

Pre-commit the falsification rule. Decide before scale: at what CAC do we hold? At what CAC do we pull back? At what trial-signup count do we kill? Without pre-commit, every result becomes a debate. With pre-commit, the decision is mechanical.

Primary metric is the one you are optimizing for (CAC, ROAS, CPL). Guardrails are the metrics you do not want to break (LTV, retention, brand search lift). Scaling the primary metric while breaking a guardrail is a Pyrrhic win.

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Channel selection: when to use which platform

Pick the channel where intent matches your offer. Run the wrong channel and your CAC reads as a channel problem when it is actually a fit problem.

Google Search. High-intent demand capture. Best when you have a real product people search for and the query volume justifies the floor. Worst when category awareness is low and no one is searching. Predictable, expensive at scale, the highest floor of any channel.

Google Performance Max. Automated multi-channel within the Google ecosystem. Best when you have a strong product feed (e-commerce) or want to lean into Google's automation. Worst when you need control over placements; PMax is a black box and disagreements with the algorithm cost money.

Meta (Facebook plus Instagram). Broad-targeting demand creation. Best for visual products, lifestyle brands, B2C scale, and direct response with strong creative. Worst when targeting is too narrow (audiences saturate fast) or when the offer is high-consideration B2B.

TikTok. Discovery-mode advertising. Best for native-feeling video creative, younger audiences, and brand awareness. Worst for direct response with high consideration cycles. Spark Ads (boosting organic posts) outperform pure paid creative.

LinkedIn. B2B targeting precision. Best for high-LTV B2B with clear job-title targeting. Worst for low-AOV products; the floor is too high to be efficient.

Reddit, Pinterest, Snapchat, X. Niche or supplementary. Best as scale-out channels after primary channels are working. Worst as starting points; spreading thin across niches before you have proven any channel is the most common waste pattern.

YouTube (Google). Video at scale. Best for awareness or for B2C consideration. Underrated for B2B SaaS in some categories where the buyer-research path includes long-form video.

The decision rule. Start with the channel where intent matches your offer. Search for high-intent demand capture. Meta or TikTok for demand creation. LinkedIn for B2B precision. Do not run all of them at once until you have proven any of them. Detail in references/channel-decision-matrix.md.

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Budget allocation: brand vs performance, baseline vs test

Four splits operate at the same time. Get them all right and the budget compounds.

Brand vs performance. Brand keeps the demand pipeline filled (long term); performance captures it (short term). 70-30 to 80-20 performance-heavy is typical for most B2C. Brand-heavy splits fit high-consideration B2B where the buying cycle is months long and pipeline visibility matters more than week-over-week conversions.

Baseline vs test. 70 to 80% of budget to channels, campaigns, and audiences that are working. 20 to 30% to systematic testing of new channels, new audiences, new creative. Without test budget, you stagnate. Without baseline budget, you have nothing to scale.

Primary vs secondary channel. One channel does the heavy lifting (60 to 70% of budget). Others scale supplementally. Resist the equal-split temptation; spreading across channels before any one is proven is the most expensive way to learn nothing.

Daily vs lifetime budgets. Daily for ongoing campaigns where you want a stable spend floor. Lifetime for finite tests where the platform should pace itself across the test window. Lifetime budgets prevent runaway spend during testing.

Budget pacing matters too. Front-load some weeks to test creative aggressively. Back-load others to capture seasonality (holiday, end-of-quarter, category-specific moments). Do not run flat; flat budgets miss the demand peaks.

Detail and templates in references/budget-allocation-templates.md.

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Audience targeting: prospecting vs retargeting vs exclusion

Three audience types. Treat them as separate strategies.

Prospecting. New people who have not heard of you. Lookalike audiences (Meta), in-market audiences (Google), interest stacks, lookalikes seeded from high-LTV customers. Largest budget share for growth-mode brands; this is where new demand comes from.

Retargeting. People who engaged but did not convert. Smaller audience size, higher CTR, lower CAC. Do not bid too aggressively or you train the platform to charge a premium for users who would have converted anyway.

Exclusion. Current customers and recent converters kept out of prospecting and retargeting. Saves spend, keeps frequency low, prevents creative fatigue from people who are already paying you.

Common mistakes. Prospecting too narrow (not enough audience for the platform to optimize). Retargeting too aggressive (cannibalizing organic conversions). No exclusions (paying to advertise to your own paying customers). Lookalikes off the wrong source (use top-LTV customers, not all customers).

Detail in references/audience-segmentation-patterns.md.

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Bid strategy: when each fits

Bid strategy depends on data state. New campaigns need a different strategy than mature ones. Switching too often resets the learning phase and wastes the data you just gathered.

Manual CPC or CPM. Full control, slow to scale. Useful for diagnostics and very early campaigns where you do not trust the platform's machine learning yet.

Maximize Conversions. Platform optimizes for volume within budget; the platform decides CPC. Use when you want as many conversions as possible regardless of cost. Good early-stage strategy while you gather data.

Target CPA (tCPA). Set a max CPA, platform delivers within. Use when CPA is the constraint and you have at least 30 conversions in the recent window for the platform to optimize against.

Target ROAS (tROAS). Set a min ROAS, platform delivers above. Use when revenue per conversion varies and you care about value, not count.

Maximum Conversion Value. Like maximize conversions but optimizes for total revenue, not count. Use when high-value conversions are the goal and you have the conversion-value data wired in.

The progression for new campaigns: start manual or maximize conversions to gather data, switch to tCPA or tROAS once you have 30+ conversions, revisit periodically.

Common mistakes. Using tCPA before you have 30+ conversions (no data to optimize against). Setting tROAS too aggressively (platform throttles delivery). Switching strategies too often (each change resets the learning phase). Detail in references/bid-strategy-reference.md.

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Campaign types

For each major platform, the campaign types and when to use them.

Google Ads. Search, Shopping, Performance Max, Display, Video (YouTube), Demand Gen, App. Search for direct demand capture. PMax for catalog-driven e-commerce. Display for retargeting. Video for awareness or consideration.

Meta. Sales, Leads, Engagement, Awareness, Traffic, App Promotion. Sales for direct response. Awareness for brand at scale. Leads for B2B with native lead forms.

LinkedIn. Sponsored Content, Conversation Ads, Lead Gen Forms. Format types: Single Image, Carousel, Video. Lead Gen Forms convert hardest because they pre-fill from LinkedIn profile data. Sponsored messaging cannot target EU members, so Conversation Ads are non-EU only.

TikTok. In-Feed, TopView, Spark Ads, Branded Hashtag Challenge. Spark Ads (boost organic posts) outperform pure paid creative because they retain organic-feel signal. Use Spark when you have organic posts performing.

Detail per platform in references/campaign-type-reference.md.

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What NOT to spend on

Direct list. Audit any account against these and you will usually find easy savings.

• Branded keywords beyond defensive. When ranking number one organically, branded paid clicks cannibalize free traffic. Some defensive spend is fine to block competitor bidding. Aggressive bidding on your own brand is waste.
• Display network without targeting. Broad display drives garbage traffic. Use only for retargeting unless you have specific contextual targeting.
• Geographic markets you do not serve. Sounds obvious, fails 30% of accounts. Audit geo targeting quarterly.
• Hours you cannot service. For service businesses (legal, B2B, medical). Pause off-hours unless lead form clearly converts asynchronously.
• Devices that do not convert. If mobile converts at 1% and desktop at 5% with the same CPC, bid down mobile aggressively.
• Audiences who never convert. Pull last 90 days of converters, build exclusion lists for everyone else who repeatedly clicks but never converts.
• Creative that is tired. Frequency above 4 with declining CTR means refresh. Refusing to refresh because "it still works ok" is incremental loss.

The pattern across these is the same. Default settings or accumulated cruft generate spend without producing outcomes. Audit, exclude, and reclaim.

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