Interactive Ad Budget Allocation

I've spent at least seven-figures in LinkedIn Ads across B2B SaaS companies, and I keep seeing the same painful mistake: Teams treat budget allocation like a finger-in-the-air exercise. Someone asks, "What should we spend on LinkedIn?" and the answer comes back as "Let's start with $3k and see what happens". No math. No model. No meaningful way to know if you're underspending or lighting money on fire. This approach guarantees one thing: You'll never know what's actually working. Here's what I use to set budgets that account for market reality: Budget = Audience x Penetration x Frequency) x (CPM/1000) Let me break each variable and why it matters: - Audience: This is your actual number of profiles that match your ICP criteria on LinkedIn. If you're selling to VP+ of Sales persona at SaaS companies with 50-200 employees in NAMER, you might have 8k accounts. Maybe less. But get the exact number from LinkedIn campaign manager. - Penetration: What % of the above audience do you realistically need to reach each month? Most folks assume 100%. In reality, 50-70% penetration is strong. Why? Things like frequency caps, user activity patterns, budget pacing, and auction competition all limit your ability to blanket-cover your audience. Plus, not everyone is doom-scrolling over here. - Frequency: This is where many folks get it wrong. They either hit someone once and wonder why nobody remembers them or blast them 30 times and burn out the relationship before it even starts. The ideal range I try to hit is 10-15 in the last 30 days. That's about 3-4 impressions per week. - CPM: This fluctuates based on targeting specificity, creative quality, bid strategy, and competitive density in your market. I typically start with $130 if I don't have any data and then rerun my projections after I have some data. Quick example: - Audience: 7900 targetable profiles - Penetration: 60% - Frequency: 10/month - CPM: $130 That gives us 7900 x 0.6 x 10 x ($130/1000) = $6,162/mo That's your baseline monthly budget to achieve meaningful reach and frequency. The good thing about this is it gives you defendable budget tied to actual market math. So when your leadership team asks why you need $6K/mo, your response is strong. And this framework scales. Running multiple segments? You can calculate budgets for each segment and roll it up to give you your overall budget. Another deeper insight many miss: You've indirectly also set leading indicators. Penetration & frequency drives brand lift and pipeline contribution far more than raw budget size. I've seen $21k/mo budgets perform worse than $10k/mo budgets because the larger one was spread too thin across poorly segmented audiences. You hit 40% of five different segments at 4 frequency each... nobody remembers you. The smaller budget hits 65% of the one well-defined segment at 10 frequency... that audience actually knows who you are. This why precision beats volume on LinkedIn.

Programmatic buyers who recognize the flaws in user-based attribution will appreciate this approach. In platform we measure the impact of each channel/tactic on conversion pixel fires over time, which has proven more effective than user-based attribution in numerous cases. Instead of the traditional user-based attribution method (“I showed this user an ad, I cookie the user, and if within 30 days they fire the pixel, the last ad shown gets credit”), we measure how spend in each channel/tactic impacts pixel fires. Our methodology: “I’m allocating spend to this channel/tactic; over time, we observe its impact on total conversion pixel fires and adjust budgets based on each channel/tactic’s effectiveness in driving those fires.” This enables more accurate measurement of hard-to-track channels in-platform, such as CTV, or environments where user ID is blocked or absent (e.g., iOS). It also eliminates lower-funnel bias and budget waste on organic conversions, a frequent user-based attribution pitfall. Prospecting spend has a longer attribution window but still demonstrates impact. Over-prioritizing retargeting and lower-funnel tactics reduces overall impact by depleting budget from channels/tactics that feed the lower funnel. We can still measure and report user-based attribution to clients. However, we present this impact analysis and allocate budgets based on measured impact.

90% of brands are wasting their Google ads budget. Here's why: Most brands are throwing money at Google with ZERO strategy behind their allocation. Meanwhile, the brands crushing it right now have a precise framework for budget distribution that's dramatically increasing their profit. After managing millions in Google ad spend, here's what each campaign type is ACTUALLY best at: 🔍 Search Ads: - Highest intent traffic (they're literally typing what they want) - Most control over targeting and messaging - Best for complex products that need explanation - BUT: Limited scale and higher CPCs 🛍️ Shopping Ads - Product-focused visual format - Strong for price-competitive products - Direct product comparisons - BUT: No ability to add compelling copy, challenging with high priced products 🚀 Performance Max - Access to all Google properties in one campaign - AI-driven audience finding - Great for scaling when other campaigns plateau - BUT: "Black box" with limited visibility, can cannibalize other campaigns Here's the budget allocation framework that's working in 2025: For Brands Under $20K/Month Ad Spend: - 60% Shopping (gather data and find winning products) - 30% Search (high-intent traffic) - 10% Performance Max (testing) (Best to lean into standard shopping at low spend rather than pMax.) For Brands $20K-$50K/Month: - 30% Shopping (push all products) - 30% Search (scale winning keywords using broad) - 40% Performance Max (scale winning products) For Brands $50K+/Month: 20% Shopping (more a 'catch all' defensive campaign) 20% Search (you'll probably have hit a ceiling on search by now) 60% Performance Max (expand & spend more on TOF placements) BUT – these allocations should shift based on 4 critical factors: 1️⃣ For complex products: Increase Search (+10-15%), Decrease PMax (-5-10%) and send traffic to an education landing page. 2️⃣ For highly visual products: Increase Shopping (+10%), Decrease Search (-15%) best for fashion products etc. 3️⃣ In competitive niches: Decrease Search (-10-20%), Increase PMax (+10-20%) for cheaper CPC's. 4️⃣ For new accounts: Avoid pMax (until you have 50 sales per month.) Implementation tip: Don't make drastic overnight changes – Google's algorithm needs time to adapt. Shift 10-15% of budget per week and monitor 7-day performance. Agree or disagree? 👇

I scaled an app to $1.5M/month in ad spend. Here’s how I’d do it in 2026: 1. Under $50k in ad spend: Meta only Stick to Meta unless you’re a TikTok-first product. Meta still has the widest purchaser pool and the strongest potential performance. Focus on creative diversity here. Andromeda rewards accounts that test multiple formats, hooks, and angles. This is where you build your creative muscle before expanding. If you’re on Android, test Google UAC with 10–15% of budget. If you’re marketing a game, test an ad network like AppLovin early. They perform well for gaming. 2. $50k–$150k in ad spend: Introduce Google (iOS) / TikTok Start testing new channels like Google and TikTok. Allocate $5k–$10k as a test budget for each platform. For TikTok: creative matters more than targeting. Expect to test 20–30 creatives before finding what works. For Google iOS: focus on App Campaigns. Let the algorithm optimize while you feed it strong creative assets. Evaluate performance over 2–3 weeks minimum before judging scalability. Don’t spread budget thin. It’s better to test one channel properly than two channels poorly. 3. $150k–$250k in ad spend: Introduce ad networks Channels like AppLovin or Unity can be very effective at this stage. Be aware these channels have a learning phase, often 2–4 weeks before performance stabilizes. Start with broad targeting and let the algorithms find your users. Creative requirements are different here. Focus on playable ads, interactive end cards, and short-form video. Give them time and budget to learn before judging performance. 4. $250k+ in ad spend: Programmatic channels Programmatic channels can be massively scalable, but they come with a long learning curve. These may require $30k–$40k in ad spend (sometimes more) before you see consistent results, depending on your CPA and event volume. They’re best introduced once you have a solid foundation with other channels and a clear understanding of your LTV. Expect longer attribution windows and more complex optimization. We scaled programmatic to over $500k/month, but only after we had the infrastructure to support it. The channels may evolve. But the principle is evergreen. Progressive expansion unlocks scale gradually and profitably. Don’t try to be 40 before you’re 40.

Planning media budgets can be complicated; Last year’s run rates/ historic performance, market conditions, competitor impact, strategy changes, financial targets, etc all need to be integrated into your performance marketing planning.   One metric that is important in the decision making process is that of marginal ROAS.   In simple terms, Marginal ROAS shows the return from the next unit of spend. It tells you where the next £ invested will deliver the greatest return.   That makes marginal ROAS more powerful than blended or average ROAS, which can disguise underperforming spend hidden inside “good enough” averages. With marginal ROAS, you’re asking: if I put the next £ into this channel, how much incremental revenue will it actually generate?   How to get “the” number isn’t as easy as pulling Marginal ROAS directly out of a platform report. It requires modelling the relationship between spend and revenue per channel. Revenue attribution can be tricky to is trickier. Whether you’re using in platform conversions, (GA, Adobe, etc) or your own custom model (reported > restated numbers from SAP for example), you need a defensible way of assigning revenue back to that channel.   Once you’ve got that dataset, you model the spend<>revenue curve. This can be done at different levels of sophistication: anything from simple log trendlines in excel to advanced Bayesian regression. The slope of that curve at your current spend point is your Marginal ROAS.   It’s important to state that every channel follows diminishing returns and thus budget allocation, and understanding this at a detailed channel level is critical. The more you invest, the weaker the return from each additional unit of spend. That’s why Marginal ROAS is so powerful: it allows you to move money dynamically between channels, instead of sticking to static allocations.   When you work this way, your media planning becomes less about defending budgets and more about chasing efficiency > you will make smarter marketing mix decisions. If you’ve got an extra £50k to deploy for example, you know which channel should get it. If you need to cut, you know exactly where to pull from without losing incremental growth.   Over time, as your modelling matures, you can layer in more advanced measures; like moving from Marginal ROAS to Net Profit ROAS or even LTV-based views of incrementality. That’s where the real strategic allocation work begins; but Marginal ROAS is often the foundation you build from.   Finding the optimal media mix through trial and error can be slow + sub optimal if you do not “punch smart”. Advanced modelling and simulations can shortcut that process, showing you how different budget distributions will play out. These approaches give you a forward looking lens, instead of just reacting to performance curves after the fact. Note: image courtesy of segment stream