Scaling paid social past a plateau
The Performance Marketing work group argues over measurement, payback targets and whether to move budget to a second channel.
Updated
Before adding budget, check how the channel is judged. Grading it on platform reporting or a blended CAC target can make a channel with room left look tapped out.
- Grade the channel on company numbers. Mark Fiske sees channel teams graded only on platform reporting, which ignores incrementality and diminishing returns. He lines it up with company-level results, blends in post-purchase surveys, and moves the target from blended CAC to marginal payback.
- Push lookalikes wider. Timothy Davis splits customer lookalikes into 1%, 2–4%, 5–7% and 8%+ bands and rebuilds them from the highest-value customers. Mark adds that the bid signal and efficiency target have to favor those customers too, or the platform drifts back to the cheapest converters.
- Concentrate or diversify. Daniel Pearson would keep funding Meta while its marginal dollar still returns well. Mark would fund a fixed learning budget on a second channel early, judged on falling CPA and customer quality, because finding a working formula takes months.
- Rerun lift tests, then check them. Timothy treats each platform lift result as a multiplier on reported numbers and reruns it, though below roughly $50K a month a test has too little signal. Once paid social is a large share of the mix, Mark checks platform lift against a matched-market test and trusts the geo test when the two disagree widely.
Group chat with Timothy, Mark, Daniel, Mike, Micah — the Performance Marketing work group
Question:
How do you keep growing paid social spend once results plateau?
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
When paid social plateaus, I start by asking why before adding budget. A plateau is rarely one thing. In my experience it's usually several smaller problems stacking up: creative that has run too long, audience lists that have gone stale, or a landing page change that quietly broke the match between the ad and the page. Fix those first, because more spend on a fatigued account only buys you more expensive results.
After that, these are the levers I pull, in order:
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Creative volume and refresh. Creative fatigue is the most common ceiling I see. Test formats systematically and refresh on a schedule so you aren't waiting for performance to fall first.
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Wider lookalike bands. Build lookalikes from your own customer data and split them into separate ad sets: 1%, 2–4%, 5–7%, and 8%+. The 8%+ band usually doesn't work, but sometimes it does, and that's where new volume comes from once the tight bands are saturated.
YouTube
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New markets. At ElevenLabs, a team of four paid marketers who all speak one language runs and proves profitability on campaigns in multiple languages, because AI handles the localization. A new language is a fresh pool of audiences and auctions. Localization shouldn't be the reason you don't enter a new market.
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Incrementality before you scale further. Rerun lift studies. Sometimes a channel turns out to be more incremental than attribution showed, and that justifies pushing harder at a "flat" ROAS. Sometimes the lift study shows the opposite.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
Timothy's lift-study point is right. The failure I see more often sits upstream of the test, in how the channel team is graded. In a lot of portfolio companies, the channel manager has an efficiency target that should be profitable, but it's graded only on publisher reporting, so Facebook is effectively saying "what I'm doing is right." That number doesn't account for incrementality or diminishing returns. A flat platform ROAS can hide real incremental lift, and a healthy ROAS can hide spend that's already cannibalizing organic. Before you push budget or pull it back, line up channel reporting with the company-level numbers. That usually means blending platform data with post-purchase surveys, and for brands with an offline mix, running matched-market tests.YouTube
Next, change the constraint you scale against. I care most about payback period because it tells you how fast the cash comes back for reinvestment. Moving from a blended CAC target to a marginal payback target, supported by a granular LTV model, often gives you headroom you can defend to finance.YouTube+1
The third lever is the slow one: creative and channel diversification. Teams got spoiled by Facebook and PMax, where you turned it on and it worked. TikTok and the newer channels make you learn the creative formula and the buying mechanics from scratch, so you need a roadmap and explicit expectations. A $200 test over six weeks tells you nothing. Often the diversification roadmap shows you're overinvested in parts of Google, and that money can fund the next channel's learning curve.YouTube
Most plateaus I see are measurement or payback-target problems that look like media problems, so fix how spend is judged before deciding the channel is tapped out.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
I agree with Mark that grading the channel on publisher reporting hides the real picture. One more cause gets missed, though: the seed data feeding the algorithm. Teams build lookalikes once from a customer list and never refresh it, so the platform keeps optimizing toward a customer profile from a year ago. Stale audience data is one of the main reasons volume declines, and it looks exactly like saturation. Re-upload your current customer base on a regular cadence. Better still, seed from your highest-value customers instead of everyone who converted, and then rebuild the 1% through 8%+ bands from that list.YouTube
For B2B, the next unlock is adding third-party buyer intent data on top of job title and industry targeting. That gives you new high-propensity pools to buy into without lowering the bar to broad audiences. Judge those pools on downstream quality. High traffic volume from a new audience means nothing if it doesn't convert past the top of the funnel.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
Seeding from your highest-value customers only pays off if the rest of the system is graded on value too. If the channel team still has a blended CPA target, the platform will drift back toward the cheapest converters, and you lose the gain you got from a better seed list. The seed, the bid signal and the efficiency target all have to point at the same customer.
That leads to the lever most paid teams don't own: raising what you can afford to pay. Acquiring the user is only step one. If onboarding, engagement and retention raise LTV, the marginal payback on the next dollar of social improves without any change to the media. In diligence I look at payback period first because it shows how quickly the money can be reinvested. A company that shortens payback from 9 months to 6 can keep scaling into more expensive auctions that a competitor stuck on a front-end CPA target has to walk away from.YouTube
In practice, put the retention and lifecycle owner in the same weekly review as the paid social buyer, and report LTV by acquisition cohort and audience band. That shows whether the 5–7% and 8%+ lookalike bands Timothy described bring in customers who stay, or just cheaper first purchases.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
Mark's org point is the part that holds up in practice. At ElevenLabs, much of our scaling came from performance, creative and lifecycle working as one team instead of three separate owners passing work between them. When the lifecycle owner sees which audience band a cohort came from, paid can defend a higher CPA on a band that retains. Paid can also cut a band that only looks cheap on first purchase.
I'd add one thing to Mark's cohort report: include creative. Some creative concepts bring in buyers who stay, and others bring in deal-seekers. If you only judge creative on front-end CPA, your refresh cycle will keep picking more of the creative that attracts deal-seekers.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
Agree on adding creative to the cohort report. The practical problem is lag. A 6- or 12-month LTV read arrives long after the creative has been refreshed and the budget moved, so the buyer goes back to front-end CPA because it's the only number available this week. Fix that by agreeing with finance on an early proxy that predicts retained value, such as second purchase or week-4 activity, validating it against mature cohorts, and then setting paid social targets on the proxy by creative concept and audience band.
This is where budgeting maturity matters. Growth-stage companies can optimize to LTV only if they understand it at a granular level, and that understanding has to be built before plateau pressure hits.
builtin If finance trusts the proxy, paid can defend a higher CPA on concepts that bring in customers who stay. If finance doesn't trust it, every plateau ends in a budget cut.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
I agree with using an early proxy, but it has a volume limit. The proxy is a deeper-funnel event, so it fires less often than a purchase. When you slice it by creative concept and audience band, each cell can end up with too few events to read. The same thing happens with incrementality tests: below roughly $50K a month on a platform, the signal is too thin and the analysis costs more than it tells you.YouTube For smaller accounts, read the proxy at the concept level first, and only break it out by band after a concept has enough volume behind it.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
Timothy is right about cell size, and it changes which tools a smaller account should use. Below that roughly $50K a month threshold, I'd stop chasing statistical precision and use the simpler methods that still work: post-purchase surveys blended with platform reporting, and accepting that the read won't be perfect. Brands without enterprise-level modeling budgets are bringing judgment back into measurement this way, and it beats letting Facebook's own reporting decide by default.YouTube The proxy and the survey answer the same question from two directions: is this concept bringing in customers we want? When both point the same way at the concept level, that's enough to keep scaling while volume builds toward a band-level read.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
That works for the small-account stage. When an account crosses the threshold, switch to the lift tools the platforms already offer. Meta, Google and the others each have a built-in way to test incrementality, so a dedicated modeling budget isn't needed to get started.YouTube Treat each lift result as a multiplier on what the platform reports, and rerun the test regularly. User behavior changes, and a channel that was more incremental than you thought last quarter can come back lower on the next read, or the reverse.YouTube The survey and the proxy then become a check between lift tests.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
One caution on the platform lift tools: Meta's lift study is Meta measuring Meta. That's useful as a multiplier, but once paid social is a meaningful share of the mix, especially next to TV or other offline spend, I'd regularly check it with an independent matched-market test. Matched-market testing has been around since the 1960s and it still holds up, because it measures what the whole media mix does in a geography rather than what one publisher can see.YouTube If the platform's lift result and the geo test disagree by a wide margin, trust the geo test when you make the decision to push spend past the plateau.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
I agree, and the geo test fits well with the new-market lever from my first answer. When we expand into new languages, launches go out market by market, so the markets that haven't launched yet already work as holdouts. Plan the rollout order with the geo test in mind and you get a matched-market read on the new spend without setting up a separate test.
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
That works as long as the holdouts are real matches. A newly launched language market and an established market differ in brand awareness, organic demand and pricing, so a comparison between them mixes the launch effect with the market difference. Pair each launch with a holdout market of similar size and maturity, and record a few weeks of baseline before spend starts. Without that baseline, the rollout will show that the new market grew, but it won't show how much of that growth paid social caused.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
Fair correction, and I accept it. The baseline weeks also tell you something about localization quality. If a new language market converts well below its matched pair once spend starts, look at the localized creative and landing pages before you blame the audience. A broken match between the ad and the landing page is one of the main ways performance drops without anyone noticing.
Daniel PearsonCo-founder of Bamboo. Advisor to HexClad Cookware
I disagree with Mark on diversification as a plateau lever. If Meta is still returning well on the marginal dollar, moving budget to TikTok or a new channel takes money out of your best-ROI ads and puts it into worse ones while you climb a new learning curve. When marketers tell me "we're too dependent on Instagram," I ask them what happens if things go right. For a team that knows its numbers, the answer is usually to concentrate harder on what's working.
Substack
In practice, putting more into your best channel means producing more creative for it, because creative output is usually what caps how much a winning channel can absorb. HexClad's Head of Growth drives hundreds of millions in revenue with four ICs. One freelancer runs Meta and TikTok, one person runs Google and YouTube, and two people make ads full time. Half of that team builds creative. If I'm staffing for more paid social spend, I hire another creative maker before another buyer or another channel.
Payback period is the right constraint, and Mark and Timothy have covered it well. One caution from my Bamboo years: most teams I talked to tracked six or more different success metrics (installs, CPI, D1, D7 and D28 ROAS, CAC:LTV), and few actually optimized to payback across every account. That was usually because they lacked org buy-in and cohort infrastructure, and the right KPI was never the issue.Substack Mark's early-proxy agreement with finance is how you get that buy-in.
Diversification makes sense when marginal returns in your main channel have actually fallen below what a new channel can deliver. You should prove that before you spread the budget out.
Micah MoreauVP of Growth at OpenAI. Former VP of Growth Marketing at DoorDash
Daniel's test for diversification is to prove that marginal returns in the main channel have actually fallen. That proof depends on a correctly fit curve. Marginal targets only help if the spend curve behind them is current. A common mistake is fitting one curve across a business that changed partway through the data.
At Hims & Hers, the Weight Loss spend curves mixed Q1 seasonality and the Novo partnership period into one aggregate curve. When we re-fit them on July '25 to present data only, Meta's marginal cost per subscriber dropped from $26,000 to $850. Long-term LTV was $1,020, so a channel that looked plateaued actually had room to grow, as long as we accepted some short-term profit in exchange for revenue. Google's re-fit came out at $2,300, still above LTV, so we held Google flat. The stale aggregate curve would have sent budget away from Meta, which was the channel with headroom. If the business has shifted, one aggregate curve is wrong. Split the curve at the shift points before you decide anything.
A team gets the math wrong if it reads blended averages. At Jet we used this table: spend goes from $1M to $2M to $3M, and blended CAC moves from $20 to $27 to $33, which looks fine against a $35 target. The marginal CAC on that last $1M was $66.67.
Timothy and Mark covered creative and lookalikes. Two levers they haven't mentioned:
- Advanced pixel matching. It raises match rates and conversion lift, and it usually just needs a sprint from product and engineering.
- Separate targets for prospecting and remarketing. If both share one blended target, remarketing's cheap conversions cover for weak prospecting, and the account settles into harvesting demand it already has. Give prospecting its own marginal target. Prospecting is the only place new scale comes from.
Keep scaling for as long as marginal cost per acquisition on the current curve stays under LTV.
Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify
Daniel's concentrate-first view matches how I'd order the levers, and his test fits the new-market point too. Expanding into new languages keeps the money in the channel that already works and opens new auctions there, so the team doesn't have to learn a new platform's buying mechanics from zero. At ElevenLabs, four paid marketers who all speak one language run that across multiple languages, so expansion doesn't require new buyers. I agree with his staffing call as well. Creative fatigue is the ceiling I hit most often, so the next hire for more spend should be someone who makes creative.
Question:
Daniel says that if Meta still returns well on the marginal dollar, moving budget to TikTok just trades your best ROI for a learning curve, and you should prove Meta's marginal returns have dropped before spreading out. A new channel's early numbers will almost always look worse, so how would you compare the two fairly? What specific evidence would you use to justify funding a new channel's learning period?
Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma
I wouldn't compare the two on the same scorecard during the learning period, because the new channel will lose on CPA every time. Run them as two budgets with separate questions.
For Meta, the question is whether the marginal dollar still clears payback. The evidence there is a current spend curve, re-fit at the shift points the way Micah described, plus a lift or geo read on what it's actually incremental. If marginal CAC is still well under the payback-adjusted LTV, Meta keeps first claim on new budget, and Daniel is right.
For the new channel, the question is whether it's learning toward a viable cost. Before launch I'd agree on three things with finance:
- A fixed test budget and time window big enough to produce a read. $200 over six weeks on one placement can't.
- Trajectory targets: CPA falling period over period as the creative formula and buying approach improve, and a clear end date to judge it.
- Customer quality matched to the core: the early retention proxy and post-purchase survey responses compared against Meta cohorts, so cheap but low-value buyers don't look like a win.
Funding for the test comes from spend that measurement already shows is past diminishing returns, often parts of Google, and the Meta campaigns that still clear payback stay where they are.YouTube The new channel moves to the scale budget only when its marginal CAC, adjusted for incrementality, approaches Meta's marginal CAC at Meta's next spend increment. That threshold is where both channels can finally be compared like for like.
The case for running the test before Meta flattens is lead time. By the time the core curve bends, you're months away from a working formula anywhere else.
Question:
Mark wants a small parallel learning budget on a second channel before the plateau hits, while Daniel says concentrate on Meta until its marginal returns actually fall. At Etsy you ran a Media Mix Model alongside the creator program. How did you decide how much spend went to proving a new channel or format while the main one still cleared payback, and what result told you to scale it?
Mike JacobsChief Growth Officer at Consortium Brand Partners. Former VP of Growth at Etsy
I never set the test budget as a fixed percentage. The core channel had to clear payback on the marginal dollar, measured with all three methods together: MMM, experiments, and MTA. Teams that lean on only one of those are often off by 3 to 5x in some channelsYouTube. Whatever the model showed as past diminishing returns funded the test. That puts me closer to Mark on timing. I'd also go a step further than Daniel's "prove it first," because the proof he's asking for comes from your own data, and that data is self-fulfilling. You keep optimizing toward the audience you already buy, and the model keeps confirming it. If a new channel or format only gets money once the model approves it, it never gets money.
New formats got judged on a different scorecard. With creator content and newer formats at Etsy, we set proxies like consideration or a quality visit before we touched purchase CPAYouTube. Time on site was our strongest predictor of a purchase 60 to 90 days later
Apple Podcasts. So a new format earned more budget when it drove quality visits at a reasonable cost. It earned scale budget when an experiment confirmed incremental lift and the MMM showed its marginal return near the core channel's next increment.
Two more levers that raise the ceiling inside the core channel:
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Feed value into the bid. At Upside we moved optimization from DMA level to ZIP level and passed predicted LTV to the media partners as the conversion value. LTV rose 20 to 30% in the pilot markets. That's the bidding version of Mark's finance-approved proxy.
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Staff for creative. I'm with Daniel on hiring makers before buyers. Meta's data shows phone-shot creative beats professional shoots about 80% of the time for known brands
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Frequently Asked Questions
How do you find out why paid social costs went up?
Find the step where the cost rises. A higher CPM with steady CTR points to the auction, a falling CTR to tired creative, clicks that stop converting to the audience, and several audiences stalling at one step to the offer or checkout.
How do you test whether paid ads bring in customers you wouldn't have gotten anyway?
Compare a group that sees the ads with one that doesn't, through a lift study, holdout or matched-market test, and divide the extra customers or revenue by spend. Then follow those customers until they pay back, and rerun the test as spend grows.
When is a marketing mix model worth building?
When a wrong split of the next budget would cost more than the model, the spend history varies enough to separate channels, and finance and channel owners will act on it. Check its estimates against holdout tests.
