Most marketers think they're data-driven, but they're not actually driving results with data. They collect metrics, build dashboards, and talk about analytics in meetings. But when it comes to making real decisions about where to spend budget or which channels to double down on, they're still flying blind.
The reality is harsh: marketing teams are chronically under-resourced from an analytics perspective. It's common to see one analyst supporting 20 to 50 marketers across an entire organization. That setup might work for other departments, but marketing generates massive amounts of complex data across multiple touchpoints and platforms. One person simply can't handle it all.
The problem gets worse when analytics resources are centralized. These teams usually focus on finance, product, or engineering data first. Marketing becomes a secondary priority, handled by people who don't understand the nuances of attribution, customer journeys, or campaign measurement.
But here's the thing: you don't need a massive data team to become truly data-driven. You just need to be smart about your approach and relentless about making it happen.
Marketing analytics often gets the short end of the stick. While other departments secure dedicated analysts and data engineers, marketing teams make do with borrowed resources and shared dashboards. This isn't just an oversight – it's a fundamental misunderstanding of how complex marketing measurement really is.
Central analytics teams might be brilliant at analyzing product usage or financial metrics, but they struggle with marketing data. They don't understand the difference between first-touch and last-touch attribution. They can't explain why a lead from a webinar should be valued differently than one from a paid search ad. They're not familiar with the quirks of Facebook's API or the limitations of Google Analytics cross-domain tracking.
Meanwhile, marketing teams find themselves in a catch-22. They need better data to prove their value and secure more resources. But they can't get better data without more resources. So they muddle through with incomplete reporting and hope for the best.
Real data-driven marketing means understanding the complete customer journey, not just pieces of it. It's tracking someone from their first website visit through multiple touchpoints – ads, emails, content downloads, webinars – all the way to becoming a paying customer.
This requires connecting data across your entire tech stack. Your front-end analytics platform shows traffic sources. Your marketing automation system tracks email engagement. Your CRM houses lead and opportunity data. Your ad platforms measure clicks and impressions. Most teams look at each of these in isolation, but true insight comes from connecting them together.
The gold standard is having all this data flow into a marketing-specific data warehouse where it can be properly modeled and analyzed. But this requires expensive data engineering resources and dedicated analytics support that most marketing teams simply can't justify to leadership.
Marketing consultant Gallant Chen, who works with companies like DocuSign and Shopify, advocates strongly that "marketers should have their own data warehouse that captures all of the marketing data, puts it into one place where the marketing team can report on that." This setup allows for complete attribution modeling and sophisticated analysis of what's actually driving results.
But Chen is realistic about the challenges. These resources aren't cheap, and marketing teams struggle to justify the investment. Most companies already have a data warehouse – it's just focused on product or finance data, not marketing needs.
The result is that most marketing teams make do with fragmented data across multiple platforms, incomplete attribution, and a lot of guesswork about what's actually working.
The first major benefit of better marketing data is understanding what's actually working versus what just looks like it's working. Most marketing teams spread their budget across multiple channels based on hunches, industry best practices, or last-touch attribution that gives all credit to the final interaction.
Better data reveals the truth. Maybe that expensive display advertising campaign isn't generating any quality leads. Maybe your email nurture sequences are doing more heavy lifting than you realized. Maybe paid search is profitable, but only for certain keyword categories.
As Chen explains: "If you can have all of the data, then it should allow you to understand essentially like where you should focus your marketing resources going forward and better sort of allocate the limited resources that you have from a budgeting perspective."
This isn't just about cutting waste – it's about moving money from low-performing channels to high-performing ones. The same budget can generate significantly more results when it's allocated based on actual performance data rather than assumptions.
The second major benefit is identifying where you have room to grow. Most marketing teams hit a plateau because they don't know which channels have headroom for increased investment. They're afraid to spend more because they can't predict the outcome.
Good data changes this completely. Chen shares a specific example: "If I'm investing in non-brand paid search as a channel and it performs well from a return on ad spend perspective... I can look at impression share, I can look at what my competitors are spending, I can look at how much I've spent in the past and understand that if I invest these incremental dollars that I can expect this incremental return."
This transforms budget conversations with leadership. Instead of asking for more money based on hope, you're presenting a clear business case with expected outcomes. You can say with confidence that an additional $10,000 in paid search will generate X leads, Y opportunities, and Z revenue.
Better data also unlocks the power of AI-driven optimization that ad platforms are heavily investing in. Google and Facebook want you to give them your conversion data and let their algorithms figure out the best audiences, bids, and creative combinations.
But this only works when you give them the right signals. Chen worked with a client who was optimizing Google Ads for leads and getting terrible results. "Google could drive leads at a low cost per lead, but that the lead quality was quite poor. And that's because ultimately, Google is trying to drive as many leads as possible at the lowest cost for you."
When they switched to optimizing for SQL conversions instead of raw leads, Google's algorithms quickly learned which keywords and audiences actually generated qualified prospects. The same budget started producing much better results because the platform had better data to work with.
Most marketing teams can't build the perfect data setup overnight. But they can make meaningful improvements with the resources they have. Here are three practical approaches that don't require massive investment.
If you're a B2B company, you probably already have Salesforce or another robust CRM. Instead of building a separate data warehouse, use your CRM as the central repository for all marketing data.
This means appending attribution data at the lead level – tracking which campaign, channel, or touchpoint generated each lead. Then ensuring that data flows through to opportunities and closed deals. Your CRM becomes your attribution system, and you can build reports that show marketing's impact on revenue.
This approach isn't perfect. CRMs aren't built primarily for marketing analytics, so you'll run into limitations. But it captures most of what you need to understand performance and make better decisions.
For teams with simpler needs or limited technical resources, spreadsheets can be surprisingly powerful. The key is automating data exports from your various platforms into Google Sheets or Excel, then connecting them together.
Pull your Google Ads spending and conversion data into one sheet. Export your CRM lead and opportunity data into another. Use tools like Funnel, Supermetrics, or native integrations to automate these exports. Then pivot the data together to understand which ad campaigns are generating not just leads, but qualified opportunities and revenue.
This approach requires more manual work than a proper data warehouse, but it gives you unified reporting in a familiar format. Most marketers are comfortable working in spreadsheets, and you can build surprisingly sophisticated analysis without any technical skills.
If most of your marketing spend happens in paid channels like Google and Facebook, consider using those platforms as your measurement system. This means sending your downstream conversion data – leads, opportunities, purchases – back to the ad platforms through their APIs.
Once Google knows which clicks generated actual customers (not just leads), its optimization algorithms can focus on finding similar high-value prospects. Facebook's Conversions API can track actions beyond just website visits, giving you a complete picture of campaign performance within the ads manager.
This approach works particularly well for companies with concentrated ad spend. If 80% of your budget goes to Google and Facebook, why not let them be your attribution system? They have sophisticated measurement tools built in – you just need to feed them better data.
Regardless of which approach you choose, focus on future impact when pitching to leadership. Don't emphasize better reporting on past performance – emphasize better decisions about future investments.
Frame your request around growth capability, not just measurement. Explain how better data will help you identify new opportunities, optimize existing spend, and scale what's working. Connect everything back to revenue outcomes that leadership cares about.
Many marketing teams get bogged down trying to build the perfect attribution model. They spend months debating whether to use first-touch, last-touch, or multi-touch attribution. They try to account for every possible interaction and create models that satisfy every stakeholder.
This approach usually fails because it tries to please everyone and ends up pleasing no one. As Chen observes: "You have this sort of like by committee decision-making process that essentially creates, in a lot of cases, an attribution model that is essentially good for nobody, right? Because it's trying to accommodate too many different stakeholders."
Different marketing functions need different measurement approaches. The person running top-of-funnel campaigns to generate new leads needs to understand first-touch attribution. The email marketer nurturing existing prospects cares more about influence on deal progression. The account-based marketing team wants to measure engagement across multiple touchpoints within target accounts.
Instead of building one model that tries to do everything, build multiple models that help different people do their jobs better. Let your demand gen team optimize using first-touch attribution while your sales development team uses multi-touch modeling to understand lead quality.
The key is keeping attribution separate from compensation whenever possible. When people's bonuses depend on attribution models, every conversation becomes political. When attribution is just about optimization and decision-making, you can be more pragmatic about what actually helps.
Keep it simple at first. Pick one primary model that covers 80% of your needs, then add complexity only when it solves specific problems. Perfect attribution is less important than consistent measurement that drives better decisions.
The biggest barrier to better marketing data isn't technical – it's organizational. Too many marketers accept incomplete data as inevitable instead of fighting for what they need to succeed.
Your success as a marketer often depends on having proper measurement in place. Chen shares a blunt perspective: if you can't measure a channel properly, you should question whether you should be spending on it at all.
Take Facebook advertising as an example. Without Conversions API setup, you're probably missing a significant portion of your actual conversions due to iOS tracking limitations. Your campaigns look less effective than they really are, which leads to suboptimal bidding and targeting. In this case, setting up proper tracking is more important than testing new creative or audience segments.
As Chen puts it: "If that channel cannot be successful because you don't have the right reporting, then you need to be spending the time to figure out how to solve for that, as opposed to... change my bids, refresh this creative, do all the things that you're normally going to do because those things are actually more important."
When you hit technical roadblocks, don't just accept them. Research alternative solutions. If your engineering team can't implement Conversions API, look into reverse ETL platforms that can do it for you. If you can't get a dedicated analyst, explore self-service analytics tools.
Build the business case for these solutions and present them to leadership. Show how the investment in better tracking will improve campaign performance and ROI. Be persistent – the first "no" doesn't mean the conversation is over.
Most importantly, don't give up after the first rejection. Chen emphasizes that many marketers try once, get told no, and then never bring it up again. But if your success depends on better data, you need to keep pushing until you find a solution.
You don't need to become a data scientist, but learning the basics will make you much more effective. Understand enough about APIs, data connections, and analytics platforms to have intelligent conversations with technical teams.
Learn basic SQL so you can pull your own data when needed. Understand how tracking pixels work so you can troubleshoot implementation issues. Know the difference between client-side and server-side tracking so you can make informed decisions about data collection.
This knowledge helps you advocate more effectively for solutions and work more productively with technical teams. You'll stop being dependent on others for basic data needs and can focus their expertise on more complex challenges.
The future belongs to marketers who can leverage data effectively, but that doesn't mean you need a massive data team or perfect infrastructure. True data-driven marketing is about consistently using data to make better decisions, optimize spend, and identify growth opportunities.
Start with what's practical for your organization. If you can't build a data warehouse, use your CRM as a system of record. If you can't get dedicated analytics resources, connect your data in spreadsheets. If you can't hire data engineers, use tools that automate the connections for you.
The key is taking action with available resources rather than waiting for perfect conditions. Every improvement in your data capabilities compounds over time. Better tracking leads to better optimization. Better optimization leads to better results. Better results lead to more budget and resources.
Don't let perfect be the enemy of good. The marketing teams that thrive in the coming years won't be the ones with the fanciest data setups – they'll be the ones that relentlessly pursue better measurement and use whatever tools they have to make smarter decisions.
Your competition is probably still making decisions based on hunches and last-touch attribution. While they're waiting for ideal conditions, you can be building competitive advantages through better use of data. The only question is: will you start now, or will you keep waiting for perfect conditions that may never come?
"If you don't have the right data and reporting to be effective in your job, you got to fix that. You have to figure out a way... if your success is tied to some of these analytics or data or reporting issues, to me, those things are far more important." - Gallant Chen
3:17 - Under-resourced data analytics
8:59 - Marketing-specific data warehouses
18:35 - AI/Smart bidding with proper data
27:02 - Practical alternatives
43:22 - Taking ownership of data problems
48:01 - Leveraging platform AI investments
Connect with Gallant on LinkedIn: https://www.linkedin.com/in/gallantc/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Check out Gallant’s work: https://gallant.co/
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Gallant is the Founding Partner of Gallant Growth and a seasoned Marketing Executive with a track record of scaling growth for B2B SaaS and marketplace businesses.
He has served as an Advisor and Consultant for companies including Docusign, Shopify, New Relic, Mixpanel, Nutanix, Upwork, and Thumbtack on marketing strategy, customer acquisition, retention, and monetization. He has deep expertise in demand gen and paid acquisition, but also supports areas including the hiring of internal/agency teams, lifecycle and email marketing, CRO, marketing analytics, and marketing operations.
Prior to founding Gallant Growth, he ran Digital Marketing at Zendesk and held marketing roles at SurveyMonkey and Apple. He started his career as a strategy consultant at Bain & Company and holds an MBA from the Kellogg School of Management and a BS from Stanford University.
