We at TRC conduct a lot of choice-based research, with the goal of aligning our studies with real-world decision-making. Lately, though, I’ve been involved in a number of projects in which the primary objective is not to determine choice, but rather awareness. Awareness is the first – and arguably the most critical - part of the purchase funnel. After all, you can’t very well buy or use something if you don’t know it exists. So getting the word out about your brand, a new product or a product enhancement matters.
Awareness research presents several challenges that aren’t necessarily faced in other types of research. Here’s a list of a few items to keep in mind as you embark on an awareness study:
Don’t tip your hand. If you’re measuring awareness of your brand, your ad campaign or one of your products, do not announce at the start of the survey that your company is the sponsor. Otherwise you’ve influenced the very thing you’re trying to measure. You may be required to reveal your identity (if you’re using customer emails to recruit, for example), but you can let participants know up front that you’ll reveal the sponsor at the conclusion of the survey. And do so.
The more surveys the better. Much of awareness research focuses on measuring what happens before and after a specific event or series of events. The most prevalent use of this technique is in ad campaign research. A critical decision factor is how many surveys you should do in each phase. And the answer is, as many as you can afford. The goal is to minimize the margin of error around the results: if your pre-campaign awareness score is 45% and your post-campaign score is 52%, is that a real difference? You can be reasonably assured that it is if you surveyed 500 in each wave, but not if you only surveyed 100. The more participants you survey, the more secure you’ll be that the results are based on real market shifts.
Match your samples. Regardless of how many surveys you do each wave, it’s important that the samples are matched. By that we mean that the make-up of the participants should be as consistent with each other as possible each time you measure. Once again, we want to make certain that results are “real” and aren’t due to methodological choices. You can do this ahead of time by setting quotas, after the fact through weighting, or both. Of course, you can’t control for every single variable. At the very least, you want the key demographics to align....