Marketing Metrics to Track

Attribution is overrated. Incrementality is what actually matters Every new-age brand wants to know what’s working. Meta ROAS is looking good. CAC is steady. Revenue is growing But here’s the truth: Your Meta ad might get the conversion. But did it cause the conversion? That’s the difference between attribution and incrementality. Most dashboards, attribution tools, and agency reports stop at attribution. But if you’re a brand selling across Amazon, Flipkart, GT, MT, Q-com, and D2C—pure attribution will always lie to you Because the sale might happen on Amazon. But it might have been nudged by a Meta video or a YouTube bumper ad 4 days ago. You don’t need a full-blown Marketing Mix Model to get started. There are simpler, street-smart ways to directionally understand what’s working—and what’s not. Here are 4 that have worked for us at Atomberg: 1. Geo Split Testing Pick two similar markets. Run campaigns in one. Don’t run in the other. Then track: • Branded search volume • Sell-through on marketplaces • Secondary sales from GT counters If the test market moves faster than the control, you’re seeing true lift. That’s incrementality. 2. First-Time Buyer Growth vs Returning Buyer Growth Track whether your growth is coming from first-time buyers or repeats. If your campaigns are just bringing back old customers—you’re not creating net new demand. But if there’s a spike in new buyers across Amazon, Flipkart, D2C—your campaigns are likely working at an incremental level 3. Paid Traffic vs Organic Trend Lines If paid traffic, clicks and spends are going up—but your organic sales or branded search isn’t moving—you’re likely just harvesting demand that already existed. But if organic lifts alongside paid—your ads are creating interest. Not just closing it. Directionally, this is one of the simplest sanity checks most teams ignore. 4. Channel Crossover + Offline Signal Mapping Your Meta ad may not show up in last-click attribution. But it might have nudged the consumer to visit your store or buy on Amazon. You can detect this through: • Post-purchase surveys (Where did you first hear about us?) • Branded search + store footfall spikes in campaign-active cities • And most powerfully—offline signals passed back to Meta At Atomberg, we pass back data from installations and warranty registrations—including pincode and purchase timelines Sometimes, we’re even able to identify this at a unique customer level through their cookies for warranty registration This has helped us understand true incrementality of perf marketing campaigns even for offline sales If you’re only measuring ROAS, you might scale what’s only taking credit for sale about to happen anyway If you chase incrementality, you’ll scale what’s working. For more details, read the full post- link in first comment.

🎡 How To Measure And Show UX Impact. With practical guidelines on how to track and articulate business impact of design work ↓ 🚫 Business rarely sees the value of UX the way designers do. ✅ To many, it shows up merely in good outcomes of A/B tests. ✅ To some, it’s reflected in satisfaction surveys (NPS, CSAT). 🤔 But most UX work goes unnoticed, and so does its impact. ✅ To change that, we can measure and report design success. ✅ Identify 10–12 representative tasks that users must do well. ✅ These tasks must reflect business priorities, get signed off. ✅ Your goal is to achieve 80%+ success rate for these tasks. ✅ Focus on task success rate and task completion times. ✅ You need before/after snapshots to explain your UX impact. ✅ Choose metrics to track impact of your UX changes. ↳ Global KPIs: success for key tasks in a customer journey. ↳ Local KPIs: success for key tasks in a single touchpoint. 🤔 Explain and report your impact with KPI trees/graphs. ✅ Show how your design KPIs reinforce business flywheels. UX work often appears to be disconnected from the heart of the business. As we tirelessly iterate on flows and features, it’s often very hard to make an argument that a design change that we've made recently had a profound impact on key business metrics. The reason for that is that, unlike other departments, we rarely have a set of widely established and regularly reported design KPIs. These KPIs are UX metrics that are tied to business metrics that they are impacting. Design KPIs https://lnkd.in/e5tWimWF Design KPI Trees https://lnkd.in/eTB3wrs9 How To Measure UX and Design Impact, by yours truly https://measure-ux.com Design KPI Graphs, by Ryan Rumsey https://lnkd.in/e5M2G-uu Business flywheels, by Timothy T Tiryaki, PhD https://lnkd.in/eJKuYu3R To visualize UX impact, we often use design KPI trees or design KPI graphs (see above). Both are different ways to visualize how design initiatives help reach business goals, and show the dependencies between them. Another way is to show UX impact within business flywheels — an artefact companies use to explain their business models. Basically they are self-reinforcing cycles of business growth, and design work typically enables these cycles to function. Study where exactly your work fits in those flywheels and attach design KPIs to them to reinforce the value that UX is driving. Surely not all design work is impactful. It depends on the audience it addresses and the value it delivers. But by measuring what matters, we can get a trackable record of the changes we enable over time — and once you shed light on it, it might change how your work is seen much faster than you think. #ux #design

New CMO: We're moving 50% of the marketing budget to brand / top of funnel. VP Growth: Hell no. My ROAS will drop, and my bonus depends on hitting a ROAS target. New CMO: Not anymore. Your bonus is tied to two metrics: 1. Total contribution dollars generated by the business (at 35% contribution margin). 2. Contribution dollar lifetime value (rolling 30, 60, 180, and 365 days) for our owned business. VP Growth: wtf?! How can I own this? New CMO: Metrics aren't about individual ownership—they're team-driven. The real challenge is choosing the right ones. VP Growth: How do we know these are the right metrics? New CMO: The right metrics grow business health and fundamental enterprise value. If we increase these metrics, while keeping fixed costs flat, we become more profitable. Are they perfect? Maybe not. But they're miles better than short-term ROAS or new customers acquired, which have far less of a direct connection to fundamental business health when we increase those numbers. VP Growth: How can you say that? New CMO: For ROAS, you can hit any number by: 1. Spending less. 2. Doubling down on branded keywords, existing customers, or retargeting. 3. Running more discount events. But ROAS lacks incentives to drive incremental revenue—what actually grows the business—and says nothing about the cost to generate it. And for new customers acquired, there is no notion of customer quality. A massive sale drives high ROAS but attracts discount hunters who won't buy at full price unless we run bigger sales. Both of these metrics lack context on quality and long term profit, which is ultimately the fundamental goal of business. VP Growth: Ok, I'll buy that, but how can I be responsible for overall contribution dollars? New CMO: As a singular individual, you can't. That's why half of your budget will now be based on team performance. For you though, it'll drive you to make better decisions with how you spend our marketing dollars VP Growth: What do you mean? New CMO: You're free from short-term ROAS pressure to pad stats and can focus on incremental profitable growth. You can step back and do the things you know are right to drive net new incremental demand (meaning: you would not have gotten that revenue if you didn't spend that ad dollar) even if it's low ROAS. VP Growth: And the mythical purse string holders are bought in? New CMO: Yup - the CFO and board now understand that the real goal for our marketing investments is both short and long term incremental contribution dollar generation at the highest possible contribution margin. That was my one condition for agreeing to accept the offer to join VP Growth: Well butter my biscuits, let's do this. New CMO: Please never say that again

If you thought PayPal’s $240K Head of CEO Content job was a big deal, Virio just posted the same role for $1.5 million. OpenAI listed theirs at $393,000. Meta followed at $223,000. Executive content has become one of the most valuable skills in tech. Rather than devaluing creativity, the AI boom has made it one of the highest-paid jobs today. Why? Because executive presence drives measurable revenue impact. - Sendoso saw 120% larger deal sizes when prospects followed their Director+ execs on LinkedIn. - Gong found deals closed 22% faster when buyers felt like they “knew” the seller. - Hootsuite ’s CEO, Irina Novoselsky, says 77% of buyers are more likely to purchase from companies whose leadership is active on social media. Companies pay 7 figures for content strategists because executive presence generates 8+ figures in pipeline. I’ve spent the last few months studying this. Interviewing startup founders. Digging into what works. Into building influence that actually converts, without burning out. The result is this new playbook: “Founder-Led Sales and Marketing Never Ends” which I’m so proud of. It’s a research-backed guide built for startup founders and GTM leaders who want to grow without chasing trends or sounding like everyone else. Inside, you’ll find: - Five clear steps to turn your expertise into demand - How to build visibility without burning out - How to use AI to speed up your thinking, not replace your voice - How to measure what really matters (beyond vanity metrics) It also includes insights from some of the smartest people I know: Gal Aga (Aligned), Arvind Jain (Glean), Irina Novoselsky (Hootsuite), Rand Fishkin (SparkToro), Peep Laja (Wynter), Alec Paul, Canberk Beker, Kacie Jenkins, Scott Albro, and more. Every founder has a story. The ones who tell it well build companies, and even movements. Gal Aga, CEO of Aligned, puts it perfectly: “LinkedIn isn’t just a social channel. It’s an all-in-one growth engine. A year ago, I’d have laughed if you told me 700k would read what I have to say every week or that LinkedIn would drive 65% of Aligned’s leads. I was so wrong.” If you’ve been building in the dark, it’s time to turn on the spotlight. Here’s exactly how: https://lnkd.in/dMwuyQTU #FLSMonLinkedIn #FounderLed #Startups #LinkedInForStartups

Analytics teams spend weeks perfecting their reports and dashboards only to hear: “This is interesting, but what should we actually do?” Recently, a marketing professor DM’ed me about his students struggling with data storytelling. His marketing research class was comfortable with the reporting aspects. But when asked to offer a clear point of view or insight, they froze. Some worried it might come across as manipulating the data if they offered interpretations or recommendations. This hesitation isn’t limited to these students. Many data professionals feel uncomfortable pushing beyond the “what.” Here’s why: 👉 Fear of being wrong publicly, especially when data involves uncertainty 👉 Desire to appear objective and “let the numbers speak for themselves” 👉 Lack of business context or confidence in their domain knowledge 👉 Positioning as a support function rather than a strategic partner 👉 Not enough time to dig deeper 👉 Strong technical skills but underdeveloped communication skills As a result, analytics often stops before the diagnosis—just listing symptoms without explaining the cause, let alone the cure. We stop at reporting what happened: “Revenue dropped 18%.” 📉 And we hesitate to explain why it happened or what to do next. What we should say: “Revenue dropped 18% because our top customer segment shifted to a competitor with faster delivery options. We should pilot same-day shipping in three test markets.” Ironically, what stakeholders need most—interpretation and direction—is what analysts often avoid. And yet, we don't go to doctors just to confirm we're in pain. We go to understand the cause and find a cure. That’s where data storytelling comes in as it moves us from: ✅ 𝐖𝐡𝐚𝐭 = Symptoms (the metrics and trends) ✅ 𝐒𝐨 𝐖𝐡𝐚𝐭 = Diagnosis (why it’s happening) ✅ 𝐍𝐨𝐰 𝐖𝐡𝐚𝐭 = Treatment (what to do next) If you want your work to drive action, you can’t stop at symptoms. You need to offer meaning and a path forward. What’s one technique that’s helped your team move from reporting to storytelling and action? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, AI, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 Check out my brand-new data storytelling masterclass: https://lnkd.in/gy5Mr5ky Need a virtual or onsite data storytelling workshop? Let's talk. https://lnkd.in/gNpR9g_K

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

Your influence in the board room and executive team is 90% communication with measurable examples. The words you use can make or break you. Naturally, I've been compiling a list of "instead of saying this, say this" with measurable results. Many are based on my gotcha moments where I've failed miserably at explaining what marketing does. I've said things like: “We’re increasing brand awareness.” “Our demand generation efforts are working.” “We’re improving our SEO strategy.” Every marketing leader has said some version of these. The problem? Nobody in the boardroom or executive team cares about (or understands) marketing buzzwords. They care about revenue, efficiency, and business impact. Let's flip the script. I've compiled a list of marketing-speak and translated these statements into terminology a room full of non-marketers would understand. And bonus, I've included the right metrics to back them up. Example: 🚫 Don’t say: “We’re generating a lot of leads.” ✅ Say this instead: “We’re bringing in people who are actually interested in buying.” 📊 Measure it with: Organic Traffic, Demo Requests, MQL-to-SQL Conversion Rate I put together a full table of these translations and a template so you can ensure your marketing efforts land in the boardroom. I'll share the list and other communication tips this weekend in my newsletter, but if you just want the table. Let me know. Drop a “TABLE” in the comments, and I’ll send it over.

Is ROAS the right metric for RMNs? Retail Media Networks (RMNs) have outgrown their early days when untapped demand meant every dollar spent was both high-ROAS and high-incrementality. Today, focusing solely on ROAS incentivizes behaviors that may appear efficient but harm long-term profitability and growth. Here’s how ROAS can be gamed—and why it’s problematic: 1️⃣ Over-spending on Retargeting or Brand Keywords. These tactics drive high ROAS but focus on customers who were likely to convert anyway, resulting in low incremental growth. 2️⃣ Discount-Driven Sales. Discounting boosts ROAS by generating short-term revenue but lowers margins, attracts low-LTV customers, and conditions buyers to expect promotions. 3️⃣ Cutting Spend on High-Incrementality Campaigns. Investing in new customer acquisition or brand building may have lower ROAS but drives long-term growth and quality customer cohorts. These behaviors lead to: ⛔️ Shrinking new customer cohorts. ⛔️ Increased reliance on discounts, reducing margins. ⛔️ Lower customer lifetime value (LTV) and diminished profitability over time. In essence, chasing ROAS at all costs leads to slower growth and declining margins—a losing combination for any business. Efficiency metrics like ROAS are necessary but must be balanced with an effectiveness metric that focuses on long-term outcomes. For example: ✅ 180-Day Contribution LTV: Measure the total revenue contribution from full-price customers acquired over six months. ✅ Incremental Revenue from Non-Brand Keywords: Track revenue generated from truly new demand sources. ROAS is an excellent efficiency metric but a poor north star. Striking the right balance between efficiency and effectiveness will ensure your business scales sustainably while maintaining margins. Keen to hear what other metrics are used for RMNs #advertising #media #tech

Don't just blindly use LLMs, evaluate them to see if they fit into your criteria. Not all LLMs are created equal. Here’s how to measure whether they’re right for your use case👇 Evaluating LLMs is critical to assess their performance, reliability, and suitability for specific tasks. Without evaluation, it would be impossible to determine whether a model generates coherent, relevant, or factually correct outputs, particularly in applications like translation, summarization, or question-answering. Evaluation ensures models align with human expectations, avoid biases, and improve iteratively. Different metrics cater to distinct aspects of model performance:  Perplexity quantifies how well a model predicts a sequence (lower scores indicate better familiarity with the data), making it useful for gauging fluency. ROUGE-1 measures unigram (single-word) overlap between model outputs and references, ideal for tasks like summarization where content overlap matters. BLEU focuses on n-gram precision (e.g., exact phrase matches), commonly used in machine translation to assess accuracy. METEOR extends this by incorporating synonyms, paraphrases, and stemming, offering a more flexible semantic evaluation. Exact Match (EM) is the strictest metric, requiring verbatim alignment with the reference, often used in closed-domain tasks like factual QA where precision is paramount. Each metric reflects a trade-off: EM prioritizes literal correctness, while ROUGE and BLEU balance precision with recall. METEOR and Perplexity accommodate linguistic diversity, rewarding semantic coherence over exact replication. Choosing the right metric depends on the task—e.g., EM for factual accuracy in trivia, ROUGE for summarization breadth, and Perplexity for generative fluency. Collectively, these metrics provide a multifaceted view of LLM capabilities, enabling developers to refine models, mitigate errors, and align outputs with user needs. The table’s examples, such as EM scoring 0 for paraphrased answers, highlight how minor phrasing changes impact scores, underscoring the importance of context-aware metric selection. Know more about how to evaluate LLMs: https://lnkd.in/gfPBxrWc Here is my complete in-depth guide on evaluating LLMs: https://lnkd.in/gjWt9jRu Follow me on my YouTube channel so you don't miss any AI topic: https://lnkd.in/gMCpfMKh

Claude is quietly becoming the most powerful AI tool in marketing. And almost nobody is using it right. I'm watching it happen in real time. Marketing leaders are still figuring out ChatGPT while their competitors are building entire campaign engines inside Claude. I spent 30 days going deep. Testing every feature. Breaking things. Rebuilding them. What I found changed how I run marketing permanently. Here are 9 Claude features most marketers don't know exist: → Projects — Your marketing command center. Upload brand voice, ICPs, positioning docs. Every chat remembers them. Every output sounds like your brand from prompt one. → Custom Instructions — Tell Claude exactly who your buyer is. "VP of Marketing, mid-market SaaS, 50-200 employees." Now every response is written for that person. Not some generic audience. → Extended Thinking — Turn this on for strategy work. I ran a competitive analysis using Playing To Win. The output read like a $20K consulting brief. Not a blog post. → Search — Claude pulls live data mid-conversation. I asked for our competitor's latest product launches. Had a competitive brief built without opening a browser tab. → Artifacts — I asked Claude to build a campaign ROI calculator. It built one I could edit live inside the chat. Input ad spend and ACV. Get projected pipeline instantly. → File Uploads — I dropped in 3 months of email data. Claude found the subject line patterns driving our highest open rates. Took 45 seconds. → Connectors — I linked Google Drive. Asked Claude to find our Q3 performance deck and summarize wins and misses. No uploading. No screenshots. Just answers. → Cowork — Pointed Claude at a folder of campaign assets. It read our positioning deck and built a full campaign brief as a Word doc. Right into the folder. → Styles — Created a custom style from my top 3 LinkedIn posts. Now every draft sounds like me without touching a word. Last Tuesday I built a full campaign plan in one afternoon. Messaging hierarchy. Channel strategy. 30 assets. Landing page wireframe. One brief. One tool. The marketers figuring this out right now will look like geniuses to their boards by year end. Here's a cheat sheet that will give you everything you need to start now.