That’s because the recommendations from the platforms are based on the general performance of all their advertisers. They are not tailored to the nuances of your brand and your customers.
For example, we have found that Google often suggests using a broad-match keyword approach combined with smart bidding on search campaigns.
In general, this approach is effective for low-cost conversions, but for a niche brand, it can be sub-optimal because it wastes money by attracting irrelevant traffic to the website.
This is the sort of lesson that a brand can only learn through testing different tactics and approaches to see what delivers the best results.
Although this experimentation can hinder short-term campaign performance, it allows you to get better ROI in the longer term. It helps you to refine your strategies and tactics to get the best results for your brand as well as for different target audiences, products and campaigns.
Most of the platforms offer a range of basic tools to help you test different elements of your campaign, from target audiences to creative executions. More advanced solutions are available when you partner with a Google- or Meta-certified agency.
Using these tools for testing and experimentation will enable you to optimise your campaigns to reach more customers and achieve more conversions.
A/B testing, available on most platforms, is a good starting point. A/B testing allows you to compare two different approaches — such as audience targets, landing pages, creative executions, or keyword match types — to see which is most effective.
You can, for example, compare which strategy or execution delivers the best cost per conversion. You can set up A/B tests to compare results across a campaign, a set of ads, or a single ad.
Here are some guidelines for getting the most from A/B testing:
When you’ve concluded your A/B test, analyse the data to determine the best strategy. If there is a clear winner, the platform will provide an overview of the best strategy on a cost-per-result basis and outline the margin by which it won.
If there is no clear victor, test more variables to determine if other changes will improve marketing performance. The goal of A/B testing should be to discover at least a 20% gain in performance.
Once-off A/B testing is not sufficient to ensure sustained results. Digital marketing algorithms are continuously changing, so today’s best-performing strategy may not work as well six months down the line.
This highlights the importance of establishing a testing culture in ensuring you are always getting the best returns for your marketing rand.
Brands with large digital marketing budgets and dedicated platform account managers can unlock a range of advanced testing solutions on platforms such as Google or Meta.
Most companies, however, partner with certified agencies to tap into a range of testing and experimentation offerings that go beyond A/B testing. One example is a brand lift study, which allows you to measure brand perception and awareness. Brand lift tests are available on Meta as well as YouTube through Google Ads.
These tests don’t just focus on traditional metrics such as clicks, impressions or views. They provide insights into how campaigns influence people’s perceptions of a brand by measuring brand metrics such as ad recall, awareness, consideration, favourability or purchase intent.
In these studies, surveys are served to audiences that have been exposed to your ads, and those who were eligible to see your ads but didn’t. The difference in responses determines the influence your ads have on key brand metrics.
Brand perception and awareness are among the key reasons consumers may choose one brand over another. Brand lift studies help you to gauge whether your digital communication is driving brand awareness and interest. The results give you actionable insights you can use to refine your marketing strategies and campaigns to ensure they resonate with your target audiences.
When it comes to digital marketing, following the same approach as everyone else will not enable you to get breakthrough results. Marketers can’t rely on machine learning or platform best practices alone to realise the full potential of their budgets and campaigns.
It is only by combining testing, learning and human insight with platform tools that you can tailor your tactics and strategies to your brand and audience so that you can, in turn, optimise your ROI.
