When a campaign underperforms, the first question is always why.
Why did CPA spike in one city? Why did ROAS drop after a creative swap? Why did conversions slow down when spend stayed the same?
Answering these questions is rarely quick—because performance marketing is a moving system. Changes in one variable ripple through everything else, making it hard to pinpoint the root cause.
That’s why the fastest way to get clarity is to think in terms of four levers:
- Audience – who sees the ad
- Creative – what they see
- Bids – what you pay per click or view
- Budget – how much you spend and where you spend it
If you can measure, analyze, and adjust these four elements effectively, you control your campaign performance. Everything else is a distraction.
Why “simple” questions get complicated
Take a common situation:
“Why was my CPA high in Austin last month?”
It sounds like a straightforward performance marketing analysis. But to answer it, you need a root cause analysis— which quickly turns into dozens of smaller questions:
Audience
- Did targeting shift to a higher-cost segment?
- Was there audience overlap with another campaign?
- Did frequency spike, hurting conversions?
Creative
- Did top-performing ads lose impression share?
- Was creative fatigue setting in?
- Was the messaging mismatched to that location or audience segment?
Bids
- Were bid strategies or targets changed mid-campaign?
- Did competitor activity push CPCs higher?
- Were bid caps too restrictive?
Budget
- Was budget reallocated mid-flight?
- Did pacing shift to less effective time slots?
- Did weaker segments get more spend than top converters?
Without a framework, you’ll find yourself chasing disconnected data points—and still lack a clear answer.
How to run a root cause analysis for performance marketing
To solve problems like “Why is my CPA higher?” you need a structured approach that examines each lever in order. Here’s a four-step method:
Step 1: Attribute Variance
The first step is to identify where the change came from and assign a measurable value to each lever. Start by setting a relevant baseline for comparison—this might be the average performance from the previous three months or the same period last year if seasonality is a factor. Then break down the variance in your key metric, such as CPA, into contributions from Audience, Creative, Bids, and Budget. For example, you might discover that a shift toward a younger audience segment with lower conversion rates added $6 to CPA, reduced distribution of two top-performing creatives added $4, and an increase in your target CPA added another $3. By quantifying each factor, you avoid guesswork and create a clear picture of where the problem is rooted.
Step 2: Isolate the Biggest Drivers
Once you have quantified the impact of each lever, the next step is to focus on the ones that matter most. This is about prioritization—chasing minor contributors wastes resources and dilutes focus. If your analysis shows that an audience shift explains 60% of the CPA increase, while budget pacing explains only 5%, the audience issue is the first problem to solve. Isolating the biggest drivers ensures you’re not spreading your attention too thin, and it also makes it easier to communicate priorities to stakeholders who need clarity on where to act.
Step 3: Test Counterfactuals
After you’ve identified the main drivers, run counterfactual scenarios to understand what would have happened if those factors had not changed. This step turns diagnosis into prediction. For example, model CPA under the assumption that the creative mix stayed constant, or that bid targets were left unchanged. You might find that restoring the previous creative mix and reverting bid targets could recover 80% of the lost efficiency without altering audience targeting at all. Counterfactuals let you evaluate multiple “what if” options before committing to a change, reducing the risk of making adjustments that won’t deliver meaningful results.
Step 4: Translate into Action
The final step is to convert your findings into a targeted, time-bound action plan. This plan should directly address the drivers you’ve identified, specify the changes to be made, and set clear performance expectations. If creative decline is the main culprit, your plan might include reactivating the top two creatives, running A/B tests on replacements, and setting impression share monitoring to avoid repeat losses. If bids are the issue, you might revert to the prior target, adjust caps, or introduce competitive monitoring to avoid future spikes. The key is to make the plan specific enough that your team can execute it without ambiguity and measurable enough that you can track its effectiveness.
Or, Let Prism Do These Steps for You
Running a full root cause analysis manually means switching between dashboards, exporting CSVs, reconciling metrics, and manually testing scenarios. It works—but it’s slow.
With Prism by Pixis, you can run this same four-step process in a single conversation. Prism connects directly to your ad accounts and analytics platforms, so it can attribute variance across Audience, Creative, Bids, and Budget; isolate the biggest drivers with exact percentage impact; model counterfactual scenarios in real time; and deliver a ready-to-execute action plan—all without leaving the chat.
With that one request, you get the same level of detail as the manual method—but faster, cleaner, and always up-to-date.
Why this matters for scaling campaigns
When you consistently evaluate performance through the lens of Audience, Creative, Bids, and Budget, you:
- Find issues faster
- Avoid wasted spend on low-impact changes
- Create clear, evidence-backed action plans
- Make informed optimizations that scale across markets
Key takeaway
Performance marketing will always have moving parts, but the root cause of most swings can be traced back to one or more of the four levers. A disciplined root cause analysis turns scattered metrics into clear, actionable insights, so you can pull the right lever at the right time. Whether you run this process manually or let Prism handle it for you in real time, the principle is the same: clarity before action, and action that moves the metrics that matter.