Gut instincts have their charm. They’re quick, and they feel decisive. Sometimes, they even work. But relying on intuition alone is a gamble that CMOs can’t afford. Instincts don’t show up at board meetings, explain missed revenue goals or justify budget decisions. And they certainly don’t help you fine-tune campaigns to deliver the best possible results.
Relying on “what feels right” wastes resources and makes proving marketing’s value an uphill battle. Data-driven decision-making offers a different path. It’s not about replacing intuition but refining it — backing bold ideas with clarity, precision and measurable outcomes to help you lead with confidence.
Why data-driven decision-making matters
Data-driven decision-making uses evidence from metrics on campaign performance, market trends and other factors to inform and validate choices. It helps you make deliberate decisions by replacing guesswork with quantitative reasoning based on your company’s goals.
Data puts you in the driver’s seat.
Think of driving around a new city by relying on your sense of direction instead of using a GPS. While intuition might occasionally lead you the right way, it leaves room for wrong turns that waste your time. On the other hand, a GPS provides real-time recommendations backed by data so you get where you need to go as quickly as possible.
If you are gearing up for a new product launch, you might follow your gut, which tells you to invest heavily in Instagram influencers since that seems trendy. Later, you realize conversions are low on Instagram. Looking back at the data from previous product launches, you find that high-value customers prefer LinkedIn. By focusing on sponsored posts there, you generate more qualified leads. In this scenario, using data to back your decisions leads to better results and less wasted marketing spend.
The cost of gut-based marketing
Relying solely on your gut puts you at risk of marketing missteps. However, experience and instincts have gotten you far — they make you a great marketer.
You need both experience and data to have full confidence in your decisions. Kelly Stancil, a seasoned data engineer at Mason, points out that most models are based on historical data, which tells you nothing about the future.
“We always have to be prepared for the unexpected and know how to pivot,” Kelly explained.
Raphaël Vaillancourt, a performance and data specialist at mint. numérique shares similar sentiments. They intentionally avoid relying too much on data from Google Analytics because of its attribution bias. Trusting data blindly is a mistake because its outcomes depend on how it’s collected. You need to be critical, question and investigate before relying on it to make informed decisions.
As budgets are scrutinized more closely, marketers will have to look at spending more closely.
HubSpot found that marketers hesitate to prioritize data because of its limitations, but this hesitation is increasingly risky. Six in ten marketers report their budgets face more scrutiny than ever, and 26% say data boosts ROI. By not making data-driven decisions, you risk:
- Struggling to justify your spend
- Leadership being less confident in your strategy
- Falling behind competitors who are investigating data
By asking the right questions and using data critically, you can stay agile in high-level strategy and tactical decision-making.
4 steps to making data-driven marketing decisions
Tim Radwanski, EVP of strategy at Convertiv, points out that we often talk about data as if it’s “the new oil.” But he agrees it’s more like clay — only useful when shaped and managed correctly.
Most marketers use data. But it’s usually fragmented data that’s been pieced together to justify gut instincts. For instance, maybe you’ve heard competitors are attending a trendy event. Your gut tells you that you should too. So, you pull together some data from a lackluster past event and argue it makes the case to try the new, trendy event.
You might be right — after all, your experience is valuable. But that’s not data-driven decision-making. For that, you need to get all your data in the same place and evaluate the impact of different marketing efforts.
1. Get all your data in the same place
The first step is to get all your data in one place. Use a data hub that connects the marketing platforms you use the most, such as Google, Meta, LinkedIn, email, CRMs, or sales enablement tools.
Your analytics tools should automatically normalize your data to uncover connections between platforms you wouldn’t see otherwise. It should transform inconsistent metrics (clicks, leads and impressions) into consistent formats.
But, your data hub is only as good as its integrations. It must capture data from every system that matters, including those tied to sales, where ROI is often measured.
2. Make broad strategic decisions
After you gather relevant data, you can evaluate your broader strategy across channels. This consolidation shows which audiences are most engaged, which products resonate in the market and which regions or segments offer the most potential.
For example, you might find that small business leads have a high conversion rate but a lower lifetime value. You could pivot to focus on enterprise clients as a result — even if their conversion rate is lower — because they deliver higher long-term profits. Use these cross-channel and audience insights to guide quarterly presentations on strategy performance.
3. Make data-driven decisions at the channel level
Centralizing your data empowers you to drill down on campaign performance across channels. Normalized key performance indicators (KPIs) like impressions, clicks and CTR let you accurately compare ROI and CPA across channels like events, paid search, organic content or email marketing.
This clarity helps you make strategic budget decisions. For instance, if you find TikTok Ads deliver high impressions but low conversions, you might shift that spend toward low funnel email nurture campaigns that need fresh messaging while you develop new TikTok creative. While the shift is temporary, you’re confident you’re focusing resources where they’ll drive immediate impact.
4. Make data-driven decisions at the campaign level
When you’re ready to dive into insights at the campaign level, start by identifying low-hanging fruit. For example, you might notice a Google Ads campaign for a product launch is generating clicks but no conversions, while a webinar target audience is performing well.
You use these data-driven insights to pause the underperforming product ads on Google and reallocate spend toward better-performing webinar ads.
Real-world examples of data-driven decision making
The process of unifying data and using it for business intelligence is continuous and evolves as your business grows. For a global company, this might mean integrating data as new markets emerge. For a startup, it might mean consolidating data under one roof.
Either way, this transformation begins when data is centralized. Real-world companies like Sephora and Limango have adopted data-driven decision-making at different stages of growth, allowing them to build smarter strategies at different scales.
1. Sephora’s emerging European markets base decisions on benchmarks for the first time
Sephora, one of Europe’s top beauty brands, was struggling with fragmented data management across 18 markets. Their central team of data scientists was spending an entire workday each week manually gathering and consolidating reports, which left them little time for strategic work. Plus, that meant local teams had limited access to valuable insights, making benchmarking nearly impossible.
Sephora partnered with Hanalytics and implemented Funnel to unify their data into one tool so emerging markets could access actionable insights independently. The clean, automated data visualization from integrations directly with BigQuery reduced data processing costs by 75%.
Centralized data made Sephora’s emerging markets more independent.
For the first time, local marketing teams could access operational reports and benchmarks on their own, which meant they could evaluate marketing campaigns independently. By connecting central and local teams with the same insights, Sephora transformed its strategy around the world and improved global campaign performance.
2. Limango dramatically reduces CPL by automating product-level insights
Limango, a leading e-commerce brand, found it challenging to manage fragmented data across multiple platforms. While some automation was in place, their team couldn’t handle the complexity of their growing channel mix. They scaled operational efficiency using Funnel to automate data extraction and integrated metrics into clean, standardized reports in BigQuery and PowerBI.
First, they wanted to optimize Meta Ads, which they spent a lot of their budget on. They experimented with dynamic creatives that served product-specific ads to targeted audiences. Initial testing showed above-average CPL, but deeper data analysis revealed certain products drove up acquisition costs.
With Funnel, they could automate product-level insights and blend them with backend performance metrics. They then used this customer data to exclude unprofitable products from their daily Meta feed, advertising only high-performing products. The results were immediate: CPL dropped by 20%, Meta Ads became a significant growth channel and Limango unlocked additional budget for campaigns.
Limango automates product-based insights from Meta.
Automation has allowed Limango to create other optimization loops that fuel real growth. However, for these insights to be valuable, the team must be ready to act on the opportunities that the relevant data reveals.
How to build a data-driven culture
Technical challenges are often named the biggest barriers to data-driven decision-making, but human behavior plays a significant role. According to Gartner, one-third of decision-makers cherry-pick data to support preconceived opinions and ignore data analytics altogether.
Trusting your gut is easier than trusting the data, but this bias undermines a truly data-driven culture amongst your team. A cultural shift toward accepting the benefits of data-driven decision-making in marketing starts with leadership in three steps:
- Use data in your own decision-making: Set an example by asking your team for data and visibly using it to guide a decision. For instance, if organic search outperforms paid ads, reallocate budget accordingly and explain why. If data overturns one of your assumptions, call it out: “I thought this messaging would resonate, but the data showed otherwise.” This reinforces evidence over instinct.
- Provide training to address fears and complexity of bias: Teach your team to analyze data confidently. Invest in tool-specific training or bring in experts for hands-on sessions. Allocate time and budget for certifications or other learning resources.
- Create accountability in performance evaluations: Your team should be able to see that a data-driven approach is valued. Regularly review data in meetings, challenge assumptions (including your own) and celebrate wins driven by data. Have open conversations about biases so your team stays invested.
Building a data-driven culture isn’t just about shifting perception — it’s about ensuring that data is actionable, not overwhelming. Making smarter decisions requires more than a massive amount of data collection.
Data-driven decision-making is about more than collecting massive amounts of data
Real data-driven decision-making isn’t about collecting big data — it’s about building a marketing strategy rooted in prioritization. Marketers tend to drown in analyzing data and try to treat every metric as equally important. When that slows down decisions, they start to mistrust it altogether. However, the most successful teams effectively leverage data to focus on a few critical signals that matter.
For instance, contrary to instinct, you might find that average time-on-site is a stronger predictor of repeat purchases than cart abandonment. Or, maybe you find social media shares better indicate product demand than website traffic, so you focus on these metrics and cut out other noise.
The right metrics help you prioritize your team’s actions and create alignment by focusing the team on key metrics. When everyone agrees, a data-driven marketing strategy becomes clearer and more effective.