How effective will my ad be?
A classic ad pre-test will tell you, with reasonable accuracy, how effective your advertising will be and give you the chance to fine-tune it. The earlier you obtain feedback, the better.
Researching a rough version of an ad (e.g. an animatic) gives you plenty of opportunity to tweak both the storyline and the execution – for example, you can make the story easier to follow or make the brand’s role more prominent. It also helps you specify in the production brief what needs to be done to make the final ad as effective as possible.
Some of the best ads ever created were researched as roughs and were improved as a result of the learnings. Guinness ‘Surfer’ (repeatedly voted ‘Best Ad Ever’) is just one example.
With finished ads, you have less room for improvement than with rough versions — but small changes can still make a big difference. A change of voice actor or music track can lift engagement, or an added sound effect can make the story easier to understand. Of course, if you’ve already researched a rough version of an ad, you may not need to validate it again when it’s finished. If you do, a low-cost, KPI-focused approach is a good option. Such an approach also works well if you need to choose which ads to adopt from a pool of available creatives (for example: ads produced for the brand in another country).
Another major benefit of researching finished ads (even if you do it after they have gone live) is to benchmark your advertising against your competitors’ – or at least against relevant database averages. A/B testing might tell you which of your ads are strongest and help you maximize in-market impact, but that won’t help your brand in the long run if even your best ads fall short of your rivals’, and you don’t know how to make improvements.
A third reason for systematically researching your final ads is to build a knowledge bank you can use to create learning and optimize future ad performance. Once you have collected results from a number of projects, you can look for ad characteristics that are linked to higher performance (often called ‘meta-learnings’). Many of the world’s most famous and effective ad campaigns started off with average performance but improved over time thanks to the feedback loop provided by pre-testing. Examples include Stella Artois ‘Reassuringly Expensive, Lynx/Axe ‘The Lynx Effect and Oxo ‘Oxo Family.’