Marketing Metrics to Track

My first communications hot take of 2025! 🔥 Traditional PR metrics are dead. Stop counting press releases. Stop tracking AVE. Stop chasing meaningless numbers that don't tell you anything or move the needle. These metrics are a relic of the past. If you're still using them, your strategy is falling behind. (And if your agency is serving up these metrics as proof of their impact, then it's time for a hard conversation.) 🤨 Here's what needs to go: ❌ "Number of press releases" is just vanity. Nope, press releases aren't a strategy—they're a tactic. Publishing isn't the same as reaching. ❌ "Volume of coverage" tells you nothing. A hundred mentions in low-tier outlets don’t compare to one strategic feature that influences decision-makers. ❌ AVE (Advertising Value Equivalency) is meaningless. This was never a meaningful metric. Did you spend that budget on ads? No? Then why measure it like one? It doesn’t capture influence or impact. ❌ "Total impressions" lacks context. Reaching the wrong audience 1M times = wasted effort. Context is important here. If it’s 1M impressions with your target audience that drives some meaningful outcomes—that’s your metric. ❌ "Social follower count" is shallow. Having 50K silent followers is cool and all, but give me 5K engaged people any day. Social is shifting from brand followers to people, making follower count even less relevant. . Here's what (I think) actually matters in 2025: ✴️ Narrative Share (think thought leadership, elevated) → Are you shaping how people think about key issues? → What percentage of relevant conversations include your POV? → Are you leading the narrative—or playing catch-up? ✴️ Share of Voice *Quality* (not just mentions) Focus on: → Authority & Impact: Topic leadership and decision-maker credibility → Message Effectiveness: Perception shifts and resonance → Business Value: Lead quality and customer story impact → Stakeholder Engagement: How key audiences interact and respond ✴️ Audience Journey → What happens *after* someone sees your message? → Do your efforts drive real behavior change? → How are stakeholders engaging, retaining messages, changing behavior, or taking action? ✴️ Community-Driven Influence (beyond basic engagement) → Are you building advocates or just awareness? → What's happening organically in your networks? → Is your community telling your story for you? 🤔 I'll be the first to admit that measuring these isn't "easy" or as simple as open rates. Measuring the metrics that actually matter in PR requires a *mix* of qualitative and quantitative approaches, and it means leaning into tools, methodologies, and frameworks that go beyond surface-level data. 🗣️ Bottom line (and something I've been saying for years): PR isn’t just about getting your name out there. It’s about influencing how people think, feel, and act—to drive business OUTCOMES. If your metrics don’t reflect that, it’s time to rethink them. 📈 What PR metrics are you focusing on in 2025?

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

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

𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝘁𝗵𝗶𝘀: You’re the head of marketing, and your CEO asks, “𝗪𝗵𝗮𝘁’𝘀 𝗼𝘂𝗿 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲?” Pause. Breathe. And never give a single, blended number. Here’s why: Blended conversion rates lump together traffic sources with different goals and behaviors. It’s the fastest way to mislead your CEO—and derail your strategy. Instead, here’s how you should answer: “Great question! We have multiple traffic sources, each serving different purposes. Which one would you like to dive into?” 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗶𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝘆 𝗮𝘀𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: 1. Demand Capture → Paid Search / Affiliates → Pricing Page / Demo Request 2. Education / Exploratory → Main Website Pages → Blog & Resources Each source has unique intent—and requires a tailored measurement approach. 𝗙𝗼𝗿 𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗮𝗽𝘁𝘂𝗿𝗲, 𝗯𝘂𝘆𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗻𝘁 𝗶𝘀 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝟱%. 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝘁𝗿𝗮𝗰𝗸: → Landing Page Conversion Rates → Conversions to Opportunity → Opportunity to Revenue 𝗙𝗼𝗿 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 / 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆, 𝗶𝗻𝘁𝗲𝗻𝘁 𝗶𝘀 𝗹𝗼𝘄𝗲𝗿. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝘁𝘆𝗽𝗶𝗰𝗮𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿 𝟭%. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗯𝘆 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗹𝗶𝗸𝗲: → Assisted Conversions → Chat Engagements → Avg. Session Duration → New Visitors vs. Returning Visitors → Keyword Rankings → Brand vs. Non-Brand Clicks The key takeaway? Blended metrics hide insights that drive action. Specificity isn’t just better—it’s essential. Friends don’t let friends give blended conversion rates to CEOs. Let’s keep marketing data meaningful. 🚀 Have you faced this situation before? How did you handle it?

If I joined a new company as a Customer Success Leader, here are three ChatGPT prompts I'd use to understand the business and build my strategy quickly. 1. Decode the current customer health reality 2. Identify what's actually driving churn vs. what we think is 3. Map out quick wins that impact revenue immediately These prompts would need some data from your CRM, customer success platform but not much more than that. That seems very doable. Here they are: Prompt One (Customer Health Audit): "I'm a new CS Leader at a [company type] with [ARR/customer count]. Our customers use our solution for [primary use case]. Help me create a customer health diagnostic framework. I need to understand: What leading indicators actually predict churn (not just login frequency) Which customer segments have the highest/lowest retention rates and why What usage patterns separate our champions from our at-risk accounts Where customers typically get stuck in their journey with us What our most successful customers do differently in their first 90 days Give me 5 specific questions I should ask my team to uncover these insights quickly." Prompt Two (Churn Analysis): "Based on typical SaaS patterns, help me build a churn investigation template. For each churned customer in the last 6 months, I want to categorize: --> Primary churn reason (product fit, economic, competitive, internal changes) --> Early warning signals we missed (timeline them) --> Which stakeholder made the final decision and their typical role --> Whether this was preventable with different CS actions --> What this tells us about our ICP or positioning gaps Create a simple framework I can use to analyze 20 recent churns and identify the 2-3 root causes we need to fix first." Prompt Three (Quick Win Revenue Strategy): "I need to identify immediate revenue impact opportunities. Help me create: A 30-60-90 day action plan template focused on revenue protection and growth 5 questions to identify expansion-ready accounts in my current book 3 process improvements that could impact retention within 60 days A framework to spot accounts that need immediate intervention Scripts for conversations with at-risk accounts that focus on business outcomes, not product features For each initiative, include: How to measure success in 30 days What resources I need from other teams The specific business case to present to leadership Clear next steps and owners" --- With these three prompts, I can cut through months of "getting up to speed" and start impacting the business from week one. The difference between CS leaders who succeed in new roles and those who struggle? Speed to insight, not speed to activity. What's the first thing you focus on when joining a new CS organization?

The average partnerships team is leaving over 30% of their revenue potential on the table. Here’s the problem (and how one new metric can help you fix it): PROBLEM: The average partner manager: - Is managing 50 to 100 partners - Only knows core performance metrics on the top 20% of them - Spends 90%+ of their “relationship” time with these top 10-20 partners - Spends <10% of their time on the other 80% of their partners That’s why 20% of your partners are driving 80% of your partner revenue. It’s not because that other 80% of your partners are a bad fit. It’s not because you’ve squeezed all of the value out of those relationships. Most of them are probably a good fit. And it's likely they do have additional value to provide. But... they’re not getting enough attention (engagement, enablement, or value). HERE'S THE SOLUTION: Partner Health Scores. An objective scorecard, based on key metrics, that measures the health of each partnership. This could be based off of leads, deals, revenue, engagement, certifications, etc. Whatever metrics are relevant, and valuable, to your business and partners. This can be a game changer. HOW TO DO IT: 1. Determine what you consider to be a healthy partnership.  2. Create a scorecard based off of these metrics. 3. Score all of your partners. 4. Re-allocate resources. You may find that your partner managers are spending their time with the wrong partners. Partners that are doing great and don’t need as much attention. Partners that have not (and likely will not) produce despite getting lots of attention. Partners that have a lot of potential but have not been getting enough attention. And some partners that are just a bad fit. But there’s no way to determine this without an objective scorecard. HERE'S THE OUTCOME: Partner managers will optimize their time and maximize their relationships (read: MORE REVENUE). Partner executives will have a more accurate view into their team’s portfolios (read: BETTER MANAGEMENT). C-suite executives will love the data driven approach (read: STRONGER ALIGNMENT). P.S. Partner Health Scores can be a lot of work to manage and update. Luckily, EULER has custom Partner Health Scores built right into the platform. We show you the health of ALL of your partners, in REAL time, ANYTIME. DM me to find out more.

What Are the 10 Marketing KPIs You Should Be Tracking Tracking the right Key Performance Indicators (KPIs) is essential in marketing to measure the effectiveness of your strategies and campaigns. Here are ten crucial marketing KPIs you should consider tracking: Return on Investment (ROI): This measures the profitability of your marketing efforts. It's calculated by dividing the net profit from a marketing campaign by its costs. Customer Acquisition Cost (CAC): This KPI calculates the total cost of acquiring a new customer, including all marketing and sales expenses. Conversion Rate: This measures the percentage of visitors to your website or landing page who take the desired action (like making a purchase or signing up for a newsletter). Customer Lifetime Value (CLV): CLV predicts the total value your business will derive from its entire relationship with a customer. Traffic-to-Lead Ratio: This KPI measures the effectiveness of your website in generating leads. It's the percentage of visitors who become leads. Lead-to-Customer Ratio: This measures the effectiveness of your sales and marketing teams in converting leads into paying customers. Brand Awareness: This can be tracked through various means such as surveys, social media mentions, and search volume data to understand how well your brand is being recognized by potential customers. Social Media Engagement: This includes metrics like shares, likes, and comments on your social media posts, indicating how engaging your content is. Email Marketing Performance: Key metrics include open rates, click-through rates, and conversion rates from your email campaigns. Website Traffic: Monitoring the number of visitors, page views, and sources of traffic helps understand the effectiveness of your online presence. Each of these KPIs offers valuable insights into different aspects of your marketing strategy, and together, they can provide a comprehensive view of your marketing performance. It's important to select the KPIs that align best with your business goals and regularly monitor and analyze them for informed decision-making.

Time is a valuable commodity, and data is no exception. ⏱️ "Speed to Insight" is key to winning today's cultural and media landscape. 👀 It's everything. Companies tracking their KPIs and metrics daily instead of monthly or quarterly grow over 20x faster. If you're a day late, you're irrelevant. When choosing the right analytics tech stack, you should look beyond the standard features–unlimited users, historical data, data sources, access to LinkedIn data (yeah, sure), and cost. Yes, these are all important, but they're just bullet points on a PowerPoint slide without speed. Here's why. 1️⃣ Faster Decision-Making: Quickly identify cultural trends, brand crises, or program insights and allow prompt adjustments to strategies and campaigns. 2️⃣ Improved Agility: Brands can respond swiftly to market changes, competitor actions, and emerging opportunities, staying ahead of the curve. 3️⃣ Enhanced Campaign Performance: Real-time data enables immediate optimization of PR and marketing efforts, maximizing engagement and impact. 4️⃣ Increased Efficiency: Speed to insight reduces the time spent on data analysis, allowing teams to focus on implementing strategies and achieving goals. 5️⃣ Competitive Advantage: Rapid insights provide a competitive edge by allowing brands to capitalize on opportunities and mitigate risks more effectively. If you're in the analytics game, you know this. 👀 Agree or disagree? Let me know in the comments. Or, send me a direct message, and I'll share which tech vendors are at the top of my list. ~~~~~~~~~~~ #analytics ‣ #datadriveninsights ‣ #predictiveanalytics ‣ #mediaanalysis ‣ #campaignperformance ‣ #socialintelligence ‣ #sociallistening

How you report on campaigns— first-touch vs. last-touch or user-level vs. event-level attribution —dramatically changes what "success" means. 💁♀️ 𝗨𝘀𝗲𝗿-𝗹𝗲𝘃𝗲𝗹 𝘃𝘀. 🎉 𝗲𝘃𝗲𝗻𝘁-𝗹𝗲𝘃𝗲𝗹 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Standard marketing tools only focus on event-level attribution. This means they measure individual actions like “signup” or “purchase” and tie that back to the campaign that triggered it. However, user-level attribution attributes all revenue from a user back to the original campaign that brought them in, allowing you to see the long-term impact of your efforts. 1️⃣  𝗙𝗶𝗿𝘀𝘁-𝘁𝗼𝘂𝗰𝗵 𝘃𝘀. 💯𝗹𝗮𝘀𝘁-𝘁𝗼𝘂𝗰𝗵 𝗺𝗼𝗱𝗲𝗹𝘀 The model you choose can significantly affect how you interpret campaign success. There’s no one-size-fits-all all. First-touch attribution tells you which campaigns drive initial interest and traffic, making it a great option for top-of-funnel analysis. On the other hand, last-touch attribution focuses on the campaign that closed the deal. 📢 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 🤑 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗱𝗮𝘁𝗮 To move beyond CAC, you need to bring your marketing analytics and revenue data together. By doing this, you can report on metrics like lifetime value (LTV) and return on ad spend (ROAS) to understand which campaigns drive the most valuable users—those who spend more, stay longer, and refer others. Event analytics tools make it easy to integrate both marketing and revenue data into a single report. 🤷♀️ 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? If you’re only looking at last-touch CAC, you might prioritize campaigns that are cheap to run but bring in low-value users. Comprehensive marketing reporting helps you: 👉 Identify high-ROI campaigns and allocate your budget accordingly 👉 Avoid spending on campaigns that bring low-ticket customers 👉 Improve your overall marketing effectiveness Want to learn more about the type of marketing reporting you really need? Then head to the link in the comments for the full article with Mixpanel.