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?

High-Quality Leads: What Are They and How to Obtain Them?

A high-quality lead is not just a contact; it’s a potential customer that aligns closely with our offering. They match our target profile and have a need that our solution can fulfil. But how do you obtain a high-quality lead through a paid media strategy?

First, it’s important to remember that the quality of a lead doesn’t depend solely on volume. Focusing exclusively on volume can be counterproductive, as a high number of leads doesn’t guarantee they will be relevant or have conversion potential. Increasing the number of contacts often comes at the expense of lowering their value. For example, if form requirements are reduced to capture more leads, you might end up with many contacts who have no real intention of purchasing or a clear need.

This is where the concept of friction comes into play: the more friction there is in the capture process (detailed forms, questions that identify customer needs, etc.), the better we can qualify leads. Of course, this also implies a reduction in lead volume. Striking a balance in friction is crucial to achieving the right balance between quantity and quality. Too much friction can reduce the number of leads, while too little can result in contacts that don’t meet the appropriate profile. Identifying the sweet spot allows you to obtain qualified leads with a higher potential for conversion.

To qualify a lead, four key criteria are fundamental: need, interest, purchasing capability, and urgency. A genuinely high-quality lead demonstrates a clear need that our offer can address. They’ve shown an interest in learning more. They have the means to make the purchase. Moreover, they have urgency or motivation driving their decision.

The qualification process starts with forms and extends to the first interactions. During these initial interactions, it’s helpful to ask questions like: “What service are you most interested in?” or “What budget do you have in mind to solve this problem?” These types of questions help quickly identify the lead’s need, interest, and capacity, enabling more accurate qualification. Asking key questions early is essential to determine whether you’re dealing with a potential customer or just a curious contact. Using an appropriate CRM also helps analyse lead behaviour and their level of interest.

Some effective practices to improve lead quality include:

• Using initial interactions to ask questions that help determine whether the person truly has a clear need, genuine interest, and purchasing capacity.

• Implementing precise segmentation in ads. Clearly defining your audience and adjusting segmentation parameters in each campaign ensures your messages reach those with the most potential.

• Adapting creativity and messaging to resonate with those closest to making a decision. What problem do they have? How can we help solve it? Tailoring the message not only attracts, but also better qualifies leads.
  

Improving lead quality always requires a mix of techniques: more friction, more precise segmentation, and a clear focus on identifying needs.

What about you? What techniques do you use to improve the quality of your leads?

Consent Mode: A Key Tool to Improve the Measurement of Your Google Ads Campaigns

Consent Mode is a tool that can enhance the measurement of your Google Ads campaigns. Are you making the most of it?

Proper implementation of Consent Mode not only helps comply with privacy regulations but also significantly improves the ability to measure and understand campaign results, especially in a context where user consent is becoming increasingly complex.

What happens without Consent Mode?

Ad clicks that lack appropriate consent turn into lost data for platforms, leading to incomplete conversion reports. For instance, a campaign might appear less effective than it actually is, prompting decisions such as cutting budgets or changing strategies based on inaccurate data. This impacts the efficiency of advertising investment. With partial information, decisions are based on an incomplete reality.

How does Consent Mode help?

With Consent Mode, Google can model conversions that would otherwise remain invisible. This is achieved using statistical modelling techniques that analyse similar behavioural patterns to estimate how many conversions likely occurred, even when direct data is unavailable. Even without cookie consent, statistical modelling allows the estimation of conversions to provide a more realistic view.

The difference is clear: a 5% conversion rate without Consent Mode could rise to 5.9% with conversion modelling enabled, representing an 18% improvement in reporting accuracy.

Implementation is key

Ensuring that Consent Mode is properly configured is crucial. It’s not enough to rely on just any CMP (Consent Management Platform). A Google-certified CMP, such as Cookiebot, can automate this process, simplifying management and ensuring compliance with regulations.

In my experience, there are several ways to check if a website has implemented Consent Mode correctly. Some useful tools include Consent Mode Inspector and Google Tag Assistant (the latter is used to verify proper implementation of Google Tag Manager and Google Analytics on the site). You can even perform a more in-depth analysis through Google parameters, where "gcs: G111" would indicate that Google recognises Consent Mode.

Do you already have Consent Mode implemented on your website?