How to Optimise Your Paid Media Testing Without Wasting Time or Budget

Each test in paid media turns uncertainty into learning: what to test, how to do it, and why the right budget and time make all the difference. Every part of a campaign can drive success, but it can also become a drain on time and resources if the approach isn’t right. So, what elements should you test, and how can you structure an effective routine for doing so?

The first step is understanding which elements have the greatest impact on results. Generally, the order of priorities should be: offer/value proposition, creative, headlines, targeting, format, primary text, call-to-action, and description. This isn’t just theory; it’s supported by performance data. For example, the offer is the backbone of any ad—without a clear and compelling value proposition for the user, no other element can compensate for it.

An effective routine for your testing strategies should include:

1. Define the key element to test: Instead of trying to assess everything at once, focus on a single element. For instance, does the headline effectively communicate the value of the offer?

2. Set clear hypotheses: Define what you aim to achieve with the change and how you will measure success (CTR, CPA, ROAS, etc.).

3. Design controlled variations: Create versions that differ only in the element being tested. This ensures results aren’t diluted by other factors.

4. Allocate an appropriate budget: This is where many uncertainties arise. Tests with very low budgets and/or short timelines tend to produce inconclusive data, mainly due to randomness. Without significant evidence, any decision based on such data will be built on shaky ground.

The issue with low budgets or short timelines is that they undermine the confidence that data can provide. Randomness, as mentioned, can lead you to incorrect conclusions, such as pausing an ad that could have performed better with more budget, time, or optimisation, or believing a winning variation is effective when, in reality, its better performance may have been random. A minimum timeframe of 7 to 14 days is recommended, depending on the volume of data you can gather, which will primarily depend on the budget and time dedicated to the test.

When this methodology is applied consistently, the benefits become much clearer. Optimisation is about building a system of continuous improvement. Ultimately, the real impact of paid media lies in discovering scalable and replicable patterns.

For those seeking sustainable results, the key is making the right changes supported by robust data. Next time you plan a test, ask yourself if the conditions are right for obtaining actionable insights. If they aren’t, it may be better to adjust the variables or postpone the test until they are. What do you think? What has been your biggest challenge when testing elements in paid media?

How to Optimise Your Paid Media Budget

Spending on paid media can determine the success or failure of your digital marketing strategies. Deciding how much to invest isn’t about picking a random number but rather balancing your goals and having a clear understanding of Cost Per Action (CPA) and Customer Lifetime Value (CLTV). A well-structured approach will allow you to establish a sustainable foundation for growth.

The starting point is to define what you want to achieve. Your goals will dictate how much you need to invest. For instance, a startup aiming for rapid growth might afford a higher initial spend, whereas an established business will prioritise sustained profitability.

CPA is a key metric. Spending between 5 and 15 times your CPA is common practice, although certain scenarios may justify deviations. For example, in the luxury market, where customers have a very high CLTV, it may be acceptable to invest 20 to 25 times the initial CPA to acquire exclusive customers. In contrast, a startup in a testing phase might limit spending below the common range to collect data without straining cash flow. A business with a high CLTV can justify greater spending as the long-term return outweighs the cost. Conversely, for an e-commerce business with tight margins, exceeding this proportion could jeopardise viability.

Highly competitive markets also require greater investment. For example, sectors like enterprise SaaS might justify spending up to 100 times the CPA if client relationships are long-lasting, CLTV is substantial, and the target CPA is relatively low. However, these cases are the exception rather than the rule.

Your business stage is another important consideration. New companies in growth markets might spend 10 to 20 times their CPA to establish themselves in the industry. This is justifiable because they often need to build brand recognition, gather initial data, and create a customer base from scratch, which requires greater upfront risk. In contrast, businesses in mature markets tend to maintain more moderate and targeted spending.

Budget allocation is equally crucial. Not all platforms offer the same ROI, and your strategy must be adjusted based on historical results. For instance, a business consistently seeing strong performance on Google Ads might allocate 60% of its budget there while exploring platforms like Meta Ads with the remaining 40%.

Finally, continuous testing is essential. An initial budget should be designed to collect sufficient data. As you gather insights on what works best for your audience, you can redirect resources to the most effective tactics and channels.

Once your campaign is running, increasing the budget will force the system to explore new audiences, which can destabilise the initial learning phase of your campaigns. This happens because the algorithm must test segments that may not be as effective as the original ones, leading to fluctuations in conversion rates and CPA while the campaign identifies new high-potential audiences.

This adjustment period may result in temporarily inconsistent performance as you expand into audiences with lower purchase intent. To avoid a significant negative impact on performance, gradually increasing the budget—ideally no more than 20% at a time—allows the system to adapt without resetting the learning phase. Accounts with larger budgets tend to manage this risk better, as campaigns have a greater capacity to run rapid tests and validate new channels with more flexibility.

Investing in paid media is about balancing investment and return with a strategic, data-driven approach. Prioritise analysing metrics such as CPA and CLTV, adjust according to your business stage, and never underestimate the importance of constant testing and learning.