1. Not running a survey
There are so many ways to collect data, it makes sense to exclude one or two from the scope of your study. But not asking your target audience directly is a major omission.
If your brand tracking study is based on website traffic alone, or if your focus is on social listening, you’re creating bias right from the start by filtering out people who don’t use these channels or haven’t explicitly mentioned your product.
Putting together a panel of respondents and surveying a representative sample gives you the chance to go beyond listening and generate a meaningful dialogue with your target audience.
2. Bad surveys
Designing surveys is a skill that pays huge dividends if you spend a little time learning how to do it right. Make sure you observe survey best practices and you’ll benefit from better quality data, a higher response rate and a smoother experience when you begin to analyse your results.
Avoid yes/no questions, use structures that avoid agreement bias, and double-check your wording to weed out suggestive descriptions, and you’ll be off to a great start.
3. Not establishing your measures of success
How will you know your brand’s status if you haven’t defined what you’re measuring against? As well as incorporating your brand values into your survey design, draw up a list of things you want people to think, feel and do in response to your branding and products.
4. Stopping at quantitative data
Quantitative data sets, such as website views, sales figures and CRM databases are all valuable, but they’re only a starting point.
To really understand what’s happening and to predict what’s going to happen next, you need Experience Data™. That’s the information about what people perceive, value and reject, and the types of experiences they are having with your brand.
5. Planning a project, not an ongoing operation
Done right, brand tracking is a continual process, not a one-off task. The benefits of running a program over time are exponential, since all the intelligence you collect adds context and background that feeds into the next round of testing, making your new insights more useful and helping you observe patterns over the short, medium and long term.