How to Overcome Marketing Analytics Challenges

𝐖𝐡𝐲 𝐚𝐥𝐥 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐧𝐞𝐞𝐝 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐱𝐩𝐞𝐫𝐭 Not long ago, data science made its entrance onto the marketing arena. “Data marketers” were already a thing—essentially marketers who knew how to use data to improve campaigns. Targeting and personalization were already driving marketing results by tens of percent. So why did we need a new generation of data scientists with little to no traditional marketing background? What value do they bring to the modern marketing world? 1) 𝘾𝙡𝙚𝙖𝙧 𝙙𝙖𝙩𝙖 𝙛𝙤𝙪𝙣𝙙𝙖𝙩𝙞𝙤𝙣 𝙖𝙣𝙙 𝙨𝙘𝙖𝙡𝙖𝙗𝙡𝙚 𝙙𝙖𝙩𝙖 𝙛𝙡𝙤𝙬𝙨 When you’re handling large data volumes, you need a well-defined data layer, storage design, and efficient transfer to the right channels. That foundational work is where data science shines. A solid technical backbone supports an effective data strategy and, beyond marketing, can unlock value for the entire business. 2) 𝘼𝙙𝙫𝙖𝙣𝙘𝙚𝙙 𝙖𝙣𝙖𝙡𝙮𝙩𝙞𝙘𝙨 𝙖𝙣𝙙 𝙙𝙚𝙚𝙥𝙚𝙧 𝙗𝙚𝙝𝙖𝙫𝙞𝙤𝙧𝙖𝙡 𝙞𝙣𝙨𝙞𝙜𝙝𝙩𝙨 Data scientists bring research techniques that complement marketing instincts. Marketing is about behavior; understanding deeper drivers of that behavior creates new insights and a competitive edge. Common techniques include anomaly detection (spotting rare events) and data mining (extracting useful patterns from raw data). 3) 𝙋𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙫𝙚 𝙘𝙖𝙥𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙛𝙤𝙧 𝙨𝙢𝙖𝙧𝙩𝙚𝙧 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨 Marketing often focuses on the here and now. Data scientists add the ability to forecast and explore possible futures. Since marketing deals with people and groups, predictive analytics—when combined with data from multiple sources—can reveal insights we wouldn’t see with a single data source. 4) 𝙍𝙤𝙤𝙩-𝙘𝙖𝙪𝙨𝙚 𝙩𝙝𝙞𝙣𝙠𝙞𝙣𝙜 𝙖𝙣𝙙 𝙗𝙚𝙩𝙩𝙚𝙧 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣-𝙢𝙖𝙠𝙞𝙣𝙜 Data scientists excel at identifying root causes, not just surface symptoms. Data—properly explored—clarifies what’s really happening rather than relying on gut feel or surface-level impressions. This mindset supports stronger, long-term strategic decisions and guards against compartmentalized thinking or incorrect assumptions. 5) 𝙑𝙞𝙨𝙪𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣 𝙖𝙣𝙙 𝙧𝙚𝙖𝙡-𝙩𝙞𝙢𝙚 𝙧𝙚𝙥𝙤𝙧𝙩𝙞𝙣𝙜 𝙛𝙤𝙧 𝙛𝙖𝙨𝙩𝙚𝙧 𝙖𝙘𝙩𝙞𝙤𝙣 There’s a powerful value in translating insights into clear visuals and real-time dashboards. The quicker insights reach management, the faster decisions can be made. Visualization bridges the gap between experts and executives, enabling informed choices in a fast-paced economy. 👇 In short: data science and marketing are increasingly inseparable. For data scientists, this is a dynamic, behavior-driven environment with huge growth potential. For marketing teams, it’s a chance to leverage rigorous analysis, predictive power, and clear storytelling to accelerate results. Techonomy #datascience #marketing #datamarketing

In researching the state of data governance, I uncovered some stark realities most leaders underestimate: - $12.9M lost per year, per company due to poor data (Gartner) - 27% of employee time wasted fixing data instead of adding value (Monte Carlo Data) - 86% of executives admit they’ve made wrong decisions because of inaccurate data (FirstEigen) This isn’t IT’s problem. It’s a marketing leadership problem. And it’s a growth problem. In my latest Perspective, I explore why marketing data governance is one of the biggest blind spots in business today:

The Most Underrated Marketing Skill in 2025: Data Literacy 📚 Everyone talks about “learning ads.” But few talk about learning data. Data is where all the answers live. It shows you what works, what doesn’t, and what’s next. If you can read data, you’ll never guess your way through marketing again. Let me tell you a quick story... When I first started in digital marketing, I was guessing. I relied on intuition and what "felt" right. But I quickly learned that intuition doesn't drive results—data does. Data showed me the numbers. It told me where I was wasting money. And more importantly, it told me what to fix. Here’s why data literacy matters: → You stop guessing and start knowing → You make smarter decisions faster → You see where to double down and where to pivot → It’s the foundation for scaling your marketing So what happened to me when I embraced data? • My campaigns started performing better. • I stopped wasting time on strategies that didn’t work. • I started to see the true value of each dollar I spent. I know what it’s like to feel overwhelmed by numbers. But trust me, data isn't scary—it’s empowering. To wrap it up: Data isn’t just about charts and graphs. It’s the key to smarter, faster, and more efficient marketing. If you want to stay ahead, you need to embrace it. The future of marketing is not about creativity—it’s about data. P.S. Would you rather hire a creative marketer or someone who can read and leverage data to make decisions? Let me know in the comments!

Don't MMMs get outdated pretty quickly? We got this question the other week: "If a model looks at the last 2-3 years of data, how can it possibly respond to our rapidly-changing market?" Great question and valid concern. This person was skeptical, given how much channels evolve and how competitors come in and out of the market. Honestly - this is an astute observation. We'll explain our solution below. But first, it's key to mention that this question is a FUNDAMENTAL misunderstanding of MMM's purpose. There are 3 essential ideas to grasp about MMMs first: 1. If you use an MMM to answer "What will my sales be tomorrow?", it's not a good tool for that job. You'll probably make lots of hacks to improve predictive power that will become outdated. If you use MMM as we do — as an estimate of channel impact over time and a guide to where you should investigate further — then you care less about exactitude and you're less at risk of your model becoming outdated. 2. Worrying that an MMM will become outdated is an example of the "You can't learn from the past" mindset that we hear a lot. It pretty quickly turns into a "Why measure anything?" conversation, every time. You can learn from the past, but not with the granularity you might be thinking. Running MMM without an advanced setup isn't easy. You need to find a smart data scientist or sophisticated partner. There's at least as much to how you run an MMM as there is to what model you use. 3. An MMM solution might not be right for every brand. Marketing effectiveness may or may not be consistent over time. We recommend you think deeply about your business first. Try to understand if there are large differences over time to measure. If there aren't such differences, some solutions might be overkill. Sure your strategy and your competitor's strategy change over time. Does it really change that much month over month? Now, for the solution 👇️ Experimentation. In our opinion, this is a much easier, faster, and more-affordable alternative to building MMMs with things like time-varying coefficients. That's why MMMs alone are not sufficient for any marketing team. Experimentation, aka incrementality testing, allows you to test your hypothesis at any time. We advise our customers to run experiments in parallel to using MMMs to make decisions. How does this work in practice? Example: Your model might suggest that YouTube is highly incremental. Instead of taking that at face value and starting to dump more money into YouTube, we'd assist our customers in running a pulse-up test on YouTube (constrained to specific time, budget, and geos) to detect a causal relationship... before scaling nationally at that level of spend. Long story short? We don't use MMM to suggest false precision. We use it to making generalized statements about how a certain channel is doing overall and relative to other channels. This helps you create a roadmap. And you pave that road with testing.

CMOs Say 45% of Their Marketing Data Is Bad Modern marketing runs on data — but what happens when that data can’t be trusted? According to Adverity’s latest State of Marketing Data Quality 2025 Report, CMOs estimate that 45% of the data their teams use is inaccurate, incomplete, or outdated, leading to wasted ad spend and poor decision-making. 💡 LeadSmith’s View: Poor data quality is one of the most expensive and overlooked drains on a marketing budget. When your inputs are unreliable, every downstream decision suffers — from targeting and creative to budgeting and messaging. The good news? Fixing it doesn’t require a full data overhaul. 👉 Here’s where to start: ✅ Audit your current data sources — email lists, ad pixels, CRM records ✅ Run regular hygiene checks to catch outdated or duplicate entries ✅ Align your team around data trust as a shared priority 💥Reliable data = reliable strategy. 📊 Read the full report here → https://lnkd.in/eq5P7HwY #MarketingData #DataQuality #DigitalAdvertising #Analytics #MarketingStrategy #LeadSmith

Why Your "Data Problem" Isn't a Data Problem. It's an Intelligence Problem. 43 dashboards. 27 reports. 14 different tools. Your team is drowning in data, but starving for insight. The result? Deals hiding in plain sight. Your reps spending 30% of their day copy pasting info between tools instead of actually selling. The promised land of "data driven decisions" has become a daily grind of confusion and busywork. The math that should concern every sales leader: The average rep spends 2.5 hrs per day gathering data, updating fields, and trying to figure out what to do next. That's a lot of time spent wrestling with tools that are supposed to help. This is exactly where Mergenia.ai comes in. We don't just add AI to an old CRM. We provide an AI-native CRM where intelligence isn't a feature, it's the foundation. Here’s what happens when you stop managing data and start deploying intelligence: 1. Instead of reps wasting 2.5 hrs a day digging through tools, our AI Co-Pilot: Automatically updates records and flags deals that need attention. Drafts and sends tailored follow up emails for each prospect. Books calendar reminders so nothing slips through the cracks. Result: Your team gets 30% of their selling time back. 2. From Dashboard Watching to a Clear To Do List Instead of staring at 43 dashboards wondering "what should I do?" your reps get a simple, prioritized list of actions. They get direct prompts like: ‘Call this customer today, they’ve just increased their usage by 40%.’ 3. From Missing Patterns to Predicting Revenue Our AI continuously scans your entire customer base to surface what human eyes miss. Expansion signals (clues that an existing customer is ready to buy more) Churn signals (early warnings a customer might leave) Competitive displacement (when a competitor is slipping and you can swoop on and win the deal) 4. From Scripted Talk Tracks to a Real Time Coaching Intelligence Imagine having an expert coach whispering in your ear during a call. Our AI does just that: “The prospect just mentioned ‘integration’, the system instantly brings up the right one pager or FAQ so the rep can answer confidently.” "Sentiment dipping, suggest "ABC" case study to rebuild confidence." "Competitor named, here are their three weakest points to highlight." Our AI-Native CRM doesn’t ask your team to dig for answers, it delivers them, right when they’re needed. The Bottom Line: You don't have a data overload problem. You have an intelligence shortage. Stop adding more dashboards to monitor the problem. Start deploying AI that solves it. At Mergenia.ai we turn your data burden into your greatest competitive advantage. If you're ready to give your team their time back and your pipeline a major boost, I'd welcome the chance to show you how it works. Book a time for a demo today. https://lnkd.in/gFpp2GeJ https://lnkd.in/gjxjt53h #AINativeCRM #SalesTransformation #RevenueIntelligence #B2BSales #AI #MergeniaAI

Mr. Amit Kurhekar 🚀, Fractional Chief Data & Digital Officer, TransformTechX article on " Data Bottleneck No One Talks About, Until It Hits the Boardroom & Growth Targets " : https://lnkd.in/gWxF_8Wu About the Author Mr. Amit Kurhekar 🚀 : Nobody sets out to build a Data & AI program just to watch it stall—yet, most do. Across global CPG and fintech, I’ve been the one asked why the dashboard isn’t matching the business outcome the team promised last quarter. Here’s the real story: When I joined MoneyLion, customer data lived in five places, under five teams—all convinced their view was “the system.” We kept launching campaigns, until a CEO offsite cut through the noise : “Why are we sending 10 different emails to the same customer? Are we actually personalizing at scale—or just segmenting last year’s problems?” So I built something different - a CDP and User 360 system built for business, not just tech. With buy-in from both CTO and CMO, and a lot of back-and-forth about privacy, consent, and what counted as “activation,” we rebuilt the journey: - 10X revenue uplift on real personalization and digital marketing—not just a glossy slide deck - 2M new users at category-leading acquisition cost - 5X CAC reduction by using different growth loops, which actually delivered Was it smooth? Never. Bringing product, risk, marketing, and ops to one table is never “plug and play.” But we got from siloed pods to shared outcomes—and faster than the skeptics predicted. Those playbooks worked at Yodlee (where financial enrichment meant chasing both benchmarks and regulatory sanity) and at Procter & Gamble (where shop floor ML drove asset uptime and a surprise $15M impact for a business used to squeezing costs from the old levers). Now I advise teams—full-time, fractional, and “in the trenches”—who care less about dashboards and more about actual boardroom growth. My next challenge? Helping CEOs, CDOs, CMOs, and Heads of Marketing turn managed data into provable financial results. I offer hands-on strategic leadership as a fractional/interim executive, not just consulting. Let’s compare notes if you’re sorting your next transformation. I can promise: the big wins are always a little messy, usually cross-functional, and best solved before quarter’s end—not after.

" Data Bottleneck " - No One Talks About, Until It Hits the Boardroom & Growth Targets https://lnkd.in/gR9T344v #data #dataanalytics #dataanalysis #databottleneck #datascience #digital #digitaltransformation #dataengineering #technology #datamining #tech #dataresearch #martech #datawarehouse #systems #enterprise #crm #ai

Why Campaign & Placement Taxonomy Matters in ensuring smooth data pipelines? Data engineering can feel like detective work. You pull campaign data from 5+ platforms — DV360, Meta, YouTube, TikTok, Innovid, Google Ads — and realize… none of the IDs match. Campaign IDs, placement IDs, creative IDs — everyone speaks a different language. That’s when **taxonomy** becomes your single source of truth. When you have a well-defined **taxonomy for campaigns and placements** — you’re not just naming things neatly. You’re building the *bridge* that lets data from multiple ecosystems talk to each other. Without it: * Joins fail. * Spend data doesn’t align with impressions. * Reporting pipelines break. * You spend hours writing regexes, substring matches, and manual mapping logic just to make sense of data that *should have* aligned from the start. With it: 1. You can harmonize data across all ad platforms 2. Campaign performance tracking becomes effortless 3. Dashboards show “one truth” — not five versions of it 4. Teams finally trust the data So next time you see your data team obsessing over naming conventions or taxonomy sheets — know that’s not overkill. That’s **the backbone** of making your marketing data reliable, comparable, and scalable. In AdTech, **taxonomy isn’t documentation — it’s data infrastructure.**