Your campaign didn’t fail because you picked the wrong channel. It failed because the person who could change it couldn’t read the numbers, and the person who could read the numbers couldn’t change it.
- Most marketing teams know they’re weak on data
- Hiring an analyst next to your marketer doesn’t automatically fix it
- Real money can hide in that gap
- So, does every marketer need to become an analyst?
- Can one person actually have both skill sets?
- What should teams actually do?
- Check your own setup
- The short version
That is the disconnect. Modern marketing produces more data than ever, but having the data and being able to act on it are two different things. The real advantage comes when marketing execution and analytics are close enough that an insight can quickly become a decision.
Most marketing teams know they’re weak on data
The industry is well aware of the gap. In Marketing Week’s Career & Salary 2024 survey of more than 3,000 brand-side marketers, 36.9% identified data and analytics as the biggest skills gap in their team. It was the third consecutive year that data and analytics ranked as the top reported skills gap.
Highly Related
A 2021 CMO Council study reported an even broader problem. It found that 85% of marketing teams had data-literacy gaps, with many respondents also reporting difficulty using analytics for customer insight and taking action based on those insights.
Now imagine your cost per lead doubles in March. A marketer might say, “The algorithm changed.” That might even be true, but it is not a diagnosis. Was the increase coming from one audience? One campaign? One creative? A change in conversion rate? A landing page problem? Higher competition? The numbers can help answer those questions, but someone still needs to understand both the marketing context and the data.
Hiring an analyst next to your marketer doesn’t automatically fix it
Pairing a marketer with an analyst can be a very effective setup. The problem starts when every important decision has to pass through a handoff.
Imagine your PPC campaign starts getting more expensive on Tuesday. The marketer sees the change and knows something needs attention. The analyst can investigate the data and identify where the change is happening. But if the marketer cannot interpret the underlying numbers independently, or the analyst doesn’t understand enough about the campaign to recommend the right action, the insight still has to travel between two people before anything changes.
That delay may be small, but repeated across hundreds of decisions, it becomes a process problem.
It works the other way, too. An analyst can build a clean dashboard showing that one landing page has a 90% bounce rate. But the dashboard does not automatically explain why. Is the page slow? Is the traffic poorly matched? Is the keyword intent wrong? Is the copy unclear? Is the offer weak?
Answering those questions requires marketing context and hands-on experience. The dashboard named the problem, but someone still has to understand and fix it. That is why the goal should not be to eliminate specialists. It should be to reduce the distance between analysis and action.
Real money can hide in that gap
This is not only an efficiency problem, it can become a financial one.
McKinsey analysed roughly 400 marketing ROI engagements and reported that, in a typical range, 15% to 20% of marketing budgets could be reallocated to other activities or returned to the bottom line without reducing marketing ROI. The research dates from 2013, so it should be viewed as an established example of the potential value of better marketing analytics rather than a current benchmark.
The point is not that 20% of every marketing budget is wasted. The point is that better analysis can reveal opportunities to move money toward activities that produce better returns.
Attribution is a perfect example. Email and paid social might both report the same conversion. That does not necessarily mean one platform is lying; each platform may just be applying its own attribution rules. The problem begins when the business looks at those reports separately and never reconciles them with the wider customer journey. Then, budget decisions can end up following whichever report gets the most attention.
Good marketing analytics is not about collecting more dashboards. It is about creating a clearer connection between the data, the customer journey, the business objective, and the decision that follows.
So, does every marketer need to become an analyst?
No. A marketer does not need to become a data scientist, data engineer, or statistician. But a marketer responsible for making and optimising decisions should be comfortable with the fundamentals.
That might mean being able to:
- Understand the KPIs that matter to the business
- Investigate why a metric changed
- Recognise problems with tracking or data quality
- Understand the limitations of attribution
- Compare audiences, campaigns, channels, or segments
- Interpret an experiment
- Explain why a campaign or budget was changed
- Measure what happened after the change
McKinsey made a similar distinction in its earlier research: marketers do not need to become data scientists, but they need enough analytical understanding to know whether the data is trustworthy and to ask the right questions of it. That is data literacy, and it is different from being a specialist marketing analyst.
Can one person actually have both skill sets?
Yes. One person can understand marketing execution and analytics well enough to connect the two. For example, a modern marketing professional might be able to understand the marketing side:
- Campaign strategy
- Audience targeting
- Landing pages and funnels
- SEO and paid advertising
- Creative testing
While simultaneously understanding the analytics side:
- Tracking and measurement
- KPI design and attribution
- Segmentation
- Dashboards, spreadsheets, and SQL
- Experimentation and statistical thinking
That person can see a problem, investigate the data, understand the marketing context, make a change, and measure what happened next.
That does not mean one person should replace every specialist. A larger organisation may still need dedicated analysts, data engineers, media specialists, or SEO experts. The more complex the organisation and its data infrastructure becomes, the more valuable specialisation can be.
The advantage of combining the skills is different: the person making the marketing decision has enough analytical understanding to question the numbers behind it. And when a specialist is involved, they can have a much more productive conversation with them.
What should teams actually do?
There are several ways to close the gap.
1. Build analytical literacy into marketing roles
If someone is responsible for optimising campaigns, they should be able to interpret the core numbers behind those campaigns. You do not necessarily need SQL or Python for every marketing role, but you do need the ability to ask: What changed? Why did it change? What evidence supports that explanation? What should we change next?
2. Cross-train marketing and analytics teams
Instead of treating analytics as a reporting function, have marketers and analysts regularly examine campaigns together. Take one campaign. Look at the objective. Look at the data. Find the change. Investigate the cause. Decide what to do. Then measure the result. The goal is not to make everyone an expert in everything; it is to make sure the two functions understand enough of each other’s work to collaborate without unnecessary friction.
3. Give both sides a shared business outcome
If the marketer is focused on lead volume while the analyst is focused on data accuracy, the two functions can optimise for different things. The better question is: What business outcome are we trying to improve? That could be qualified leads, pipeline value, revenue, customer acquisition cost, retention, or another meaningful business metric. The exact KPI will depend on the business, but everyone involved should understand how their work contributes to it.
Check your own setup
Want to know whether your team has a marketing and analytics handoff problem? Ask these three questions:
- Can whoever runs your campaigns explain last month’s result with a number? If the explanation starts with “it feels like…” rather than evidence, there may be a data-literacy gap.
- Does an optimisation decision regularly wait for someone else’s report? If it does, ask whether the handoff is necessary or whether the person making the decision could answer the question themselves.
- Can your marketer explain why a change was made? Not just what changed, but why did they make that decision? What evidence supported it? What happened afterward?
If those questions can only be answered after a handoff, there may be an opportunity to bring marketing and analytics closer together.
The short version
A marketer who can’t read the data guesses with confidence. An analyst who can’t act on it describes problems nobody fixes. Put them in a meeting together and you get slower guessing. Put both skills in one head, and you get decisions.
Neither skill needs to replace the other. But when the two are connected, the path from data → insight → decision → action → measurement becomes much clearer. That is what modern marketing needs. You don’t need every marketer to become an analyst. You need marketing decisions to be informed by people who understand both what the numbers say and what the business can actually do about them.
You can’t fix what you can’t see. If you want to know how much budget is hiding in your team’s skills gap right now, run your last month’s spend through our ROI and Attribution Calculator. If the numbers don’t line up with how your business actually performs, that’s a signal worth investigating.
