Google Ads Campaign Essentials

B2B companies dramatically overspend on paid search with very low ROI because the reports & metrics they use ALLOW IT to happen. All you need to measure is two core revenue metrics: 1. $ Closed Won Revenue : $ Ad Spend (Lagging) Example: For every $1 we spend on Google ads, we get $2.20 in revenue. You should be targeting an absolute minimum of 1:1. Very few B2B companies I interact with ever get this minimum baseline metric. 2. $ HIRO Pipeline : $ Ad Spend (Leading) Example: For every $1 we spend on Google Ads, we get $10 in HIRO pipeline. Our HIRO win rate is 25% historically, so we can project forward that we’ll get approx $2.50 in revenue for every $1 investment in Google Ads. ___ This will immediately tell you whether your investment in paid search is working (Paid Search is one of the Top 3 largest annual Marketing expenditures at most B2B companies). Then, break down these metrics by each core campaign group: 1. Branded 2. High Intent Non-Branded 3. Low Intent Non-Branded 4. Competitor With this view, you’ll probably see that the blended ROI you see on paid search is actually being propped up by Branded keyword conversions that would’ve happened anyway, while non-branded and competitor campaigns are bleeding big losses & negative ROI. __ If B2B companies evaluated their paid search investment through this simple, logical lens, they would spend 50-75% less on paid search every month. Because that investment is clearly not driving actual business outcomes or positive ROI when evaluated through this lens. Which would create a large additional budget that could be deployed to much more effective GTM programs. #b2b #marketing #google #gtm #sales p.s. Paid search is a 100% demand capture channel. By definition, if someone makes a search, it's a signal of intent. The only appropriate way to measure demand capture is how much of that intent you've captured into sales meetings, pipeline, and revenue. Don't overcomplicate it. p.p.s. You don't need any fancy technology to do this. Implement persistent UTMs and track it to opportunities in Salesforce against the converting contact.

A colleague of mine asked a great question recently: “Our display ads show solid view-through conversions, but how do I know if we’re spending too much or too little? Some of these conversions would happen anyway.” It’s a question I get a lot — and one that cuts right to the heart of modern measurement. Here’s what I told him: 1. View-through conversions ≠ incrementality. Just because someone saw your ad and later bought doesn’t mean the ad *caused* the sale. Many of those users might have converted anyway. So before increasing spend, it’s critical to know: *What’s the true lift?* 2. Incrementality testing is essential. The best marketers run geo holdouts, sophisticated A/B tests, or randomly selected matched market experiments. These give you a clean read on whether your display ads are actually *driving* results — or just taking credit. 3. Leading indicators matter too. One sophisticated client I have uses AI to track marketing metrics as leading indicators of effectiveness: increases in brand measures, branded search activity, CLV shifts among exposed audiences. These signals tell you if you’re moving in the right direction *before* the conversions show up. 4. Ask better questions, not just measure more. Don’t settle for surface-level metrics. Align your measurement to business impact. That means understanding how different channels contribute to awareness, consideration, and — most importantly — profitable growth. Efficiency metrics like CTR or ROAS don’t tell the whole story. The smartest brands go deeper. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

I’m not sure who needs to hear this… 🗣️ Running campaigns for a week or two is NOT a good test You need Statistical Significance - - I’ve seen a lot of accounts where a limited budget was applied and set to run for 1-2 weeks as a “test”. I can’t stress enough that while running tests is an essential part of marketing to figure out who and how to target your audience, if you don’t give it time to build up statistical significance, you’re not accruing enough value from the data for it to impact your test in a meaningful way. I’m taking some courses on the newest Google updates, and it showed this message: “Take these steps to run good experiments When testing, marketers should run experiments until they reach statistical significance, which usually takes at least four weeks. Avoid making changes to your base or trial campaigns during that time. Then evaluate the results from the experiment while excluding the ramp-up period (usually about one week).” I can’t agree with this more, and it mirrors MANY conversations I’ve had with our clients over the years. - - So what does that mean? When you start running a campaign, there’s an initial period when all the information and data are still new, and the algorithm is trying to “learn” to optimize campaigns. If you have an audience of 50,000 people and run a campaign for a week, you’ve only reached a tiny sample of that audience from which to gain insights. You want your ads to get enough exposure to enough people that you can start seeing trends in the data. As more people have the opportunity to engage, you’ll see what’s resonating and have enough of a sample to make an educated decision on what worked, what didn’t work, and what your next test should be. I can’t tell you how many times (even though we set expectations) a client has asked us on the second week of a campaign running, “What optimizations are you doing and what insights do you have for my campaign?” The answer is usually NONE. If we start changing things too quickly without enough data to justify them, we’re actually hurting the campaign. I’ve seen so many examples of an ad that starts as a “low” performer after a week or two, but as we get more exposure during the 3rd and 4th week, becomes one of the highest performers. You can ask 5 people what color they prefer, blue or green? You might have 4 out of the 5 answer that they like green, so you assume everyone likes green better. Then you ask 50 people, and 15 say green, but 35 say blue. As you gain statistical significance, you’ll gain a better perspective of the results and be able to make a more informed decision about your test and the next step. #linkedinads #linkedinmarketing #funnelbuilder #stasticalsignificance

The better (and cheaper) alternative to multi-touch attribution 🎯📊 Look, I like multi-touch attribution. It’s a way to nice way to divvy up credit across the multitude of marketing activities needed to get a deal across the finish the line. But good multi-touch attribution is expensive and hard to implement. It can struggle with offline vs online, mobile vs desktop, and impressions vs clicks to name a few. But as a marketing leader it’s your job to help determine if your marketing activities are actually working. Did that new revised homepage work? What about that big Youtube campaign? What about the substantial ABM investment? How about those billboards? Marketing is hard. Stakeholders want answers. Your CEO, your board, your CFO, your CRO … better have some solid data because those questions are coming at the next e-staff or board meeting. So today, I’d like to share a simple yet effective technique I’ve used to help get you those answers. Control groups 🧪📊 What’s a Control Group, and Why Does It Matter? If you’ve ever taken a science class, you’re already familiar with the concept. A control group is the group that doesn’t get the “new thing” you’re testing. It serves as your baseline so you can compare it to the group that does get the new experience. Why is this so important? Because without a control group, it’s hard to know if the results you’re seeing are due to the change you made or something completely unrelated. Maybe a competitor launched a new product, or a major economic event shifted customer behavior. Maybe you ran an event that same week. Without a baseline for comparison, you’re guessing at best. Control groups let you measure the real impact of your marketing initiatives. And the best part? It’s free. No fancy tech required. Real-World Examples of Control Groups in Action Ad Campaigns 🎯 At Slack, we tested campaigns in select cities while using the rest of the U.S. as a control. This helped us measure the lift in awareness, leads, and pipeline. Later, we scaled this approach to national campaigns using the rest of the world as a control. Website Changes 🖥️ At Salesforce, we kept a control group that saw the old homepage while testing a new design. This ensured we could attribute any performance improvements to the change, not external events. ABM Campaigns 🏹 In B2B marketing, ABM is powerful, but how do you prove its impact? Target 50 accounts with ABM and leave 50 as a control group. Then measure conversion rates, deal size, and sales velocity. I love control groups. Anyone else out there using them?

💡 It’s not uncommon for you to hesitate when it comes to testing your strategy. From my experience, it usually sounds like this: "We spent months creating this strategy, let’s just do it!" And to be fair, if you’re working in a stable market, solving a known problem with a known solution, then yes go do it. You probably don’t need to test your way through it. Not everything needs to be an experiment. Lex Roman and I even discussed this very point here: https://lnkd.in/gFJfJ3Qz But that’s rarely the case across your entire business. Most of the companies I work with are operating in fast moving markets and in this environment, placing large bets on a predefined strategy without testing key assumptions can be fatal for your business. However, testing your strategy doesn’t necessarily mean second guessing your vision. It means asking: 🔍 Which parts of this strategy are we assuming will work, but without any evidence? - Which core capabilities can build or scale in time? - How will brand loyalty or switching behavior will favor us? - Are we counting on talent or retention strategies that haven’t been tested? - What methods will we use to reach and retain these future customers? It means prioritizing: 🧭 Which of these assumptions would have to be true but lack evidence? 1. Which core capabilities can build or scale in time? 2. What methods will we use to reach and retain these future customers? 3. Which core capabilities can build or scale in time? 4. Are we counting on talent or retention strategies that haven’t been tested? It means testing: 🧪 How can we quickly test these assumptions in our strategy? Assumption: How will brand loyalty or switching behavior favor us? Experiments: Customer Interviews – Ask target customers what if they've recently switched brands and how they perceive loyalty in this category. Preference Testing (Landing Page) – Show competing brand value propositions side by side and measure which gets more engagement. Ad Campaign with A/B Messaging – Test acquisition rates with messages targeting switchers vs. loyalists. And there are many different types of experiments you can run for each assumption. The Testing Business Ideas book has 44 of them here: https://lnkd.in/ev-Qvnh In short, it’s not testing that’s risky... it’s assuming you’re right. If you’re wrestling with how to test your strategy, we are considering a webinar on this topic if it could help you out. Just let me know in the comments 👇

I've audited 7 port-cos in 3 months, finding $1M/month in wasted marketing spend. Here are the 6 biggest offenders: 1. Low Quality Scores Google ranks the "quality" of every ad campaign from 1 to 10. The higher it is, the less money you spend on clicks (and leads). I regularly saw scores in the 3-4 and even 1 range. For the record, we wouldn't even *dream* of letting anything below a 7 run long-term. The biggest culprit here was a mismatch between the keyword, ad copy, landing page, and the company's value prop. 2. Too Many Keywords One enterprise B2B SaaS was vaporizing over $100k a month to target 1000s of keywords. This is not necessary. You're much better off finding the top 10-20 best-performing keywords and focusing on them. You'll get a much more easily manageable campaign PLUS lower cost per MQL. 3. Lame Ad Copy And yes, I'm grouping "clever" here under "lame." Look, enterprise decision-makers aren't looking for anything clever or cute. Instead, they want clarity. The easiest formula is to simply state what you offer, who it's for, and why it's better than alternatives. Ideally including the original search terms. Or, explain it like you’d talk to my 6 year old. Simpler and better. 4. Optimizing to the Wrong Conversion This is a problem I see often, having the algorithm optimize for "button-clickers" vs. "hand-raisers”. A hand-raiser is someone who took the time to fill out a form with their details. Not a guarantee that they'll buy, but an infinitely better signal-to-noise ratio than paying obscene amounts of money just to track and optimize for people who will press a landing page button with zero qualified intent. 5. No Retargeting Instead of devoting 10-20% of ad spend to retarget folks who’ve been to your site or raised their hand (but haven’t bought), companies are only trying to win new clients. Big rookie mistake. If someone's already shown interest, you want to keep that spark alive, not let it die. 6. Bidding on Wrong Countries/Languages If 99% of your clients are in North America, then 100% of your marketing spend should target North America. Also, if your ICP speaks English, don’t target Spanish speakers. Bidding on places or languages unrelated to your ICP is another fantastic way of vaporizing money with *zero* results. TL;DR I can't name names because of NDAs, but even big-name companies you admire make marketing mistakes that cost them a lot. To avoid that: 1. Check your ad quality scores 2. Bid on fewer keywords 3. Write clear and simple copy 4. Optimize for "hand-raisers" 5. Retarget a few segments 6. Focus on relevant countries/languages only Questions? AMA in the comments:

In the last 90 days, I’ve seen 7-figure sellers lose over $50,000 to these 3 PPC mistakes. Scaling to 7 figures takes hard work, but keeping profits up at that level? That’s where it gets tricky. Here are the top 3 mistakes I’ve uncovered in recent audits: 1️⃣ Wasted Ad Spend: Ad budgets that go to SB and SBV can bleed tens of thousands of dollars. 2️⃣ Match Type Gaps: Not testing missing match types. (Pro Tip: Use Data Dive for this!) 3️⃣ Not TOS Worthy: TOS looks great for CTR, but it doesn’t always mean profit. In theory, it makes sense. TOS usually has the highest CTR. So, many sellers assume it's best for conversions, too. But here’s the thing: high CTR doesn’t always mean high profits. We recently analyzed a campaign. TOS was performing well on the surface. CTR was solid; CPC was manageable. It looked like everything was working. But when we dug deeper, the product was actually losing money. The issue? The high TOS bids were hurting margins. The conversion rate didn't justify the spend. We analyzed PPC data with Sellerboard. Then, our focus went to ROS and PP. That change turned a losing campaign into a profitable one. TOS isn’t always the answer. Knowing when to push it—and when to pull back—can make or break profitability. Are any of these happening in your account?

After overseeing $150M+ in ad spend for 8-figure finance, lead gen, app & omnichannel brands, these are the 5 pillars I came up with that go into every high-performing direct response ad. 1/ Alignment to your target audience Making sure the creative is aligned to your target audience is vital. This one obviously doesn’t need much explaining, but it’s important to note since a lot of brands seem to lose sight of this. 2/ Clear brand/product presence Your brand and product presence needs to be crystal clear in the ad. If it’s hard to tell what product/service you’re actually selling, chances are you won’t be getting the right people in the door. 3/ Value proposition is clear This answers the question of: “Why should I care about watching more of your ad/clicking through?” If the value you’re providing/problem you’re solving isn’t clear to the user, they have no reason to click or be interested. They could be the perfect, 10/10 ideal client who’s super problem aware… But if they aren’t even aware that you can help them with that problem, they won’t convert. 4/ Clear CTA This is a direct response ad. So that said, we want to make sure that the user knows you want them to click through and take some sort of next step. Whether it’s to book a call, buy now, or enter info for a free lead magnet… What their next step is, shouldn’t be up for debate. Make it nice and easy for those who are interested. 5/ Mixing of formats You can’t just be running videos. You can’t just be running images. You need to be mixing it up, running ALL formats. Reason why, is because Meta delivers ads to users with preference in mind. For example, if person A prefers to receive video ads, they’re going to end up getting more video ads. If person B prefers image ads, they’re going to get more of those. If you only run one type, you’re going to be neglecting an entire subset of your audience. Overall, those are the 5 most important principles that all high performance direct response ads have in common. Did I miss any? Let me know in the comments!

Over the last 4 years, Directive has run over $65M in Google ads for the top companies in tech. Here are the 3 biggest mistakes I see SaaS companies make with Google ads (and how to fix them): 1. They spend their budget on “informational intent” queries that have no chance of converting This seems obvious, but *every* account we audit makes this mistake. Here’s how to fix it: Step 1: Analyze Non Brand Queries - Filter to look at non-brand keywords - Filter those keywords to look at "keyword text contains" and input all the modifiers - Compare these keywords to "keyword text does not contain" the modifiers Example commercial intent modifiers: Software, Services, Provider, Company, Quote, Vendor, Solution, Best, Tool, Platform, Buy, Top, Comparison. Step 2: Segment your Campaigns by Intent - Ensure commercial and non commercial keywords are not in the same campaign (i.e. do not have "employee recognition" in the same campaign as "employee recognition software"). - Control budget on the campaign level. Ensure you don't have commercial intent keywords fighting for budget with lower intent keywords that have more search volume. Step 3: Max out budget on those high intent Keywords - Grow your keyword pool by finding more commercial intent keywords from scraping relevant software directories 2. They Use Request a Demo CTA The most widely used CTA in B2B SaaS is “Request a Demo”. Unfortunately, this causes hidden psychological friction... When you request something there’s an opportunity you wont be able to have it. See IMAGE BELOW for CTA’s you should test instead. Backed by data from our portfolio. 3. They Don’t Audit Their Ad Experience From Search Term > Ad Copy > Landing Page > Form > CTA > Scheduling It ALL needs to be audited. - Search term: Google ads pretends to target by keywords, but the truth in how you show up is in your search term report. Audit and understand this first. - Ad Copy: Don’t give Google control of your messaging. They will keyword stuff your ads and that won't work. - Landing Page: Is it fast? Does it deliver on the promise of the ad? - Form: Is your form too long? Can you do a two column layout for field? Does it have copy to entice you to convert? How long is the trial? Are you using an enrichment tool? - CTA: What is the copy on your form submit button? Can you be more compelling or creative? - Scheduling: What happens after they convert? Can they schedule a call? Do they have to wait for sales? Are they added to a retargeting campaign to support your lifecycle conversion rates? TAKEAWAY: Anyone who says Google Ads don’t work anymore is lying to you. It’s poorly managed campaigns that don’t work. So, go be your prospect. Experience your ads. Be critical. Ask yourself... “If I had to narrow my search to three brands while searching on Google would I choose ours?” Then run AZ tests, not AB tests. And GET OUT of the platform. Great ads are optimized for the wild, not in platform

How I build Google Search campaigns (Step-by-step tutorial guide) 👇 1. Intent-first keyword planning → I don’t start with keywords. I start with the funnel. → Bottom-of-funnel: “Buy [product]” | “[service] pricing” | “[brand] reviews” → Mid-funnel: “Best [solution]” | “[product] vs [competitor]” | “Top tools for [job]” → Top-of-funnel: “How to [solve problem]” | “What is [solution]” → I group by intent, not just by match type. ↳ Then I build separate campaigns for each stage. 2. Match types & keyword structure → Exact Match = for bottom-of-funnel + control → Phrase Match = for mid-funnel + flexibility → Broad Match = ONLY with tight audiences + RSAs + Smart Bidding (I keep SKAGs dead — but I segment tightly by theme, CPC band, and search intent) 3. Ad copy that does 3 things: → Qualifies the click (not just gets it) → Mirrors the search (use DKI or smart keyword insertion) → Offers a next step (CTA = phone call, demo, form) ↳ Headline 1 = Keyword insertion ↳ Headline 2 = Unique Value Prop ↳ Headline 3 = Social Proof or CTA ↳ Descriptions = address objections + reinforce credibility 4. Bidding & budgeting → Manual CPC only for new accounts or branded search → Target CPA / Target ROAS for scale → Maximize conversions for lead-gen with tracking in place ↳ Each campaign gets a MIN daily budget of 10x target CPC. ↳ Anything lower = you’re flying blind. 5. Landing pages → 1:1 relevance with keyword + ad copy → Fast, mobile-optimized, scroll-tracked → Forms above the fold with real CTAs ↳ No generic homepages. No multi-intent pages. ↳ Every ad click should feel like: “Yep, this is exactly what I searched for.” Bonus tip: ↳ Use Google’s “Search Term Insights” weekly. ↳ Remove junk queries ↳ Double down on high-intent variants ↳ Discover new negative keywords Request a FREE Google Ads audit & strategy in the link below: https://lnkd.in/dBPzChJr ___________________ ♻ Repost if you found this helpful! ♻ Follow Marc Jordan Waldeck and Bounce Marketing for more! Need expert management? DM me! 🤓