Understanding Bottom Funnel Conversion Metrics

Most B2B brands overinvest in awareness content, then wonder why conversions stall. That gap between attention & action? It’s a bottom-of-funnel problem. While TOFU & MOFU content bring traffic & engagement, they rarely seal the deal. What actually drives demos, trials & revenue? BOFU content that speaks directly to in-market buyers. Here’s what the best-performing BOFU strategies are doing differently: 1. Prioritize buyer intent: → High-intent visitors don’t need education, they need confidence. → BOFU content should answer late-stage objections, showcase product differentiation & speed up decision-making. 2. Integrate product deeply: → Instead of hiding the product, winning brands embed it. → Think templates, tear-downs, product-led guides, or “how we use our own tool” posts. 3. Drive action, not awareness: → Clear CTAs that match stage-of-funnel mindset like: comparison charts, pricing breakdowns & interactive tools, are outperforming traditional long-form. 4. Optimize for distribution: → It’s not enough to publish. → The best BOFU content is supported by paid retargeting, internal sales enablement & email nurtures built to convert. In a market flooded with thought leadership, the brands growing fastest are the ones doubling down on late-stage, revenue-driving content. Is your funnel built for conversion, or just conversation?

When someone says “pipeline is down,” one of my first questions is always: where exactly? Volume? Conversion? Deal size? Speed? “Pipeline is down” is too vague. You need clarity! If you don’t know where the leak in the funnel is, you can’t fix it. And flooding a leaky pipeline with more leads isn’t the answer. I like to look at it in buckets (I've been a framework/systems/listicle nerd before it was an "AI-giveaway" trend). Start blended and high-level. Then, once you see the pattern, dig into segments like SMB vs. Enterprise, geo, or channel. Keep the first pass simple. 1. Volume – # of high-intent submissions – # of opps created (by stage) Is raw volume actually down? 2. Conversion / Quality in the Funnel Look at how deals move stage to stage. – High-intent → Meeting booked % • SMB: ~50–60% • Enterprise: ~30–50% • Blended: ~50% – Meeting booked → Meeting attended % • SMB: ~80–90% • Enterprise: ~70–85% • Blended: ~80% – Meeting booked → Opp created % • SMB: ~70–85% • Enterprise: ~40–65% • Blended: ~75% – Opp created → Closed won % • SMB: ~20–30% • Enterprise: ~10–20% • Blended: ~25% – High-intent → Closed won % • SMB: ~4–8% • Enterprise: ~1–3% • Blended: ~5% Always start with your own historical benchmarks. These ranges are directional, just to give you context. SMB and Enterprise behave very differently. If you apply one blended expectation to both, you’ll misdiagnose the problem. 3. Velocity (+/- your average) – Time between stages – Average sales cycle If conversion is steady but velocity is stretching, you don’t have a demand problem. You have a timing problem. That’s a completely different strategy conversation. When you look at Volume, Conversion, and Velocity together, you can actually spot the leak and work as one revenue team (not 2 departments) to fix it. From there, I use a simple lens: People, Process, Technology optimization framework. Let’s say inbound → meeting booked is under 5%. To me, that signals it’s not truly high-intent, and you likely have a marketing lead quality issue. That’s where you drill down using the same People, Process, Tech framework to find the root cause. 1. People – Do you have the right people doing the right things? Are reps overqualifying in the first email instead of getting the buyer into a conversation? I see this constantly. It turns it into “prove you’re worthy” instead of “how can we help?” 2. Process – Nothing kills a deal more than time. After submission occurs, how fast is it getting routed? Are SLAs actually being met? 3. Technology – Is the lead being enriched automatically? Are you using scheduling tools to reduce friction? This gives you a clear map to find the kinks in the funnel and optimize what’s already there. Auditing and optimizing this funnel is one of those things that makes your current demand work harder while you’re building the engine for future demand. & dare I say (I'm cringing at myself) a "low-hanging-fruit" exercise.

How I Build Bottom Funnel in Google Ads (Turn intent into profit) 👇 1️⃣ Define Bottom Funnel = High-Intent Conversion → Goal is efficient revenue capture, not exploration → Optimize strictly toward purchases, qualified leads, or revenue → Eliminate low-intent signals completely ↳ One primary conversion action per campaign. Any noise here directly inflates CPA. 2️⃣ Search = Primary Conversion Driver → Campaign type: Search (brand + non-brand high intent) → Keyword strategy: exact + broad match with Smart Bidding → Optimize for: Max Conversions → tCPA / tROAS ↳ Aggressively bid on high-intent queries (“buy”, “price”, “near me”) This is pure demand capture, no distractions. 3️⃣ Performance Max = Full-Funnel Conversion Closer → Optimize for: conversions or conversion value (tROAS) → Feed high-quality assets + audience signals (remarketing, customer lists) → Use product feed (eCom) or strong creatives (lead gen) ↳ Ensure clean conversion tracking, PMax amplifies inputs Garbage signal in = scaled inefficiency. 4️⃣ Remarketing = Conversion Recovery Layer → Campaign types: Display, Demand Gen, YouTube, RLSA → Audiences: cart abandoners, checkout users, high-intent visitors → Segment by recency (1–3d, 7d, 14d) ↳ Increase bids as intent deepens The closer they are to conversion, the more aggressive you get. 5️⃣ Bidding Strategy = Precision Over Volume → tCPA: stable lead gen with consistent conversion volume → tROAS: eCommerce or revenue-driven models → Avoid Max Clicks or top-funnel strategies here ↳ Requires strong data density (30–50+ conversions/month minimum) Bottom funnel runs on accuracy, not exploration. 6️⃣ Conversion Tracking = Non-Negotiable → Enhanced conversions + GA4 integration → Deduplicated events, accurate attribution → Revenue tracking within 5–10% of backend ↳ Feed real value (not proxies) into bidding The algorithm optimizes exactly what you track. 7️⃣ CRO = Force Multiplier → Landing page alignment with query intent → Fast load speed, frictionless UX → Strong offer + clear CTA ↳ Even perfect traffic fails with poor conversion experience Traffic quality × page efficiency = revenue. Bottom funnel is not where you experiment. It’s where you extract value. ___________________ ♻ Repost if you found this helpful! ♻ Follow Marc Jordan Waldeck and Bounce Marketing for more! Need expert management? Book a call! 🤓

VP Growth: Meta just wants you to use their in-platform attribution.  CMO: [slacks the below summary and link to the Meta paper debunking that myth]  VP Growth: [5 min later] We need to change the way we measure asap. – Summary & Takeaways: - Meta's latest paper on measuring ad effectiveness Incrementality as north star. Incrementality = driving a purchase from someone who would not have already purchased - If ad dollar is not incremental, it's wasting money—paying for a transaction that would have already happened - To maximize incrementality, use more than simple attribution tools - Need statistical modeling (MMM) and incrementality experiments - Those 3 things (attribution + statistical modeling + incrementality experiments) are the ideal triumvirate of measurement tools to maximize actual revenue and profit growth from ads - Expect conflicting results. This is a consistent learning journey, not one-and-done where conflicts cause us to throw babies out with bathwater. Commitment to consistent testing where learning and calibrating are expected - Use marginal return as the guide—constantly measure how much more revenue you get or lose when adding or subtracting dollars from a strategy, tactic, or channel - For diminishing lower-funnel returns, adding broader reach (e.g., non-purchase conversion campaigns) drives more incremental purchases at higher marginal return ($1.50+ revenue per $1 spent vs. <$1.50) Actions: - Start using MMM and incrementality experiments. Bad setups (wrong geos, insufficient time/spend) can be damaging. This is a learning journey - Test incremental attribution—don't be surprised if ROAS is lower. Doesn't necessarily mean 'it doesn't work', could mean previous attribution inflated ROAS by claiming credit for transactions that would have happened anyway - Measure impact using marginal return. Pulse spend up/down to understand revenue response. Results evolve over time/season/stage - Over-invest in measurement tools—tiny % of ad budgets but dramatically reduces money you light on fire -- Full paper: https://lnkd.in/gXDejV-G

MQLs are a Goodhart problem. It's when a measure becomes a target, it stops being a good measure. The moment marketing gets a volume goal on MQLs, the definition of MQL starts bending to hit the number It's why MQLs get such a bad rap! It plays out like this. Quarter one, score thresholds get nudged down Quarter two, form fills get reweighted Quarter three, webinar attendees get auto-qualified Each adjustment is defensible on its own BUT after four quarters the definition has shifted materially and sales acceptance is quietly falling It goes undetected because volume gets reported in one review and conversion gets reported in the pipeline review. It's a problem when there are different owners, different cadences, different slides. The two numbers rarely sit on the same view until someone runs a post-mortem on a missed quarter There's a structural issue underneath all of this. MQL is a lead level metric in an account level buying world. A single director downloading an ebook is a weak signal. Five people from one account engaging across six weeks is the real signal. The unit of measurement was built for a different era of marketing 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁:  1. Pair every volume metric with its conversion metric on the same dashboard. MQL count means nothing without MQL-to-SQL and MQL-to-pipeline sitting beside it  2. Calibrate score thresholds from data, recalibrated quarterly against actual conversion behavior  3. Move the target up the funnel. The further from revenue a metric sits, the easier it is to game. MQL volume is three or four conversions away from a booking, which is why definitions drift without consequence. Pipeline sourced and pipeline influenced are closer to the outcome and harder to manipulate. Bookings sourced and bookings influenced are closer still, and that's where a lot of mature marketing orgs are landing as the real target. The tradeoff is lag time, so you pair it with leading indicators like engaged accounts to keep the operating rhythm tight  4. Add account-level signals alongside lead-level ones. Engaged accounts is a stronger leading indicator for most B2B motions  5. Make quality degradation a threshold, not a discovery. If MQL-to-SQL drops below a defined level for two consecutive months, a review fires automatically MQLs as a target itself is rarely a good solution. Instead I would focus on conversion and pipeline with a designated target percentage and pipeline floor Good luck out there Go forth and operate 👋