5 common mistakes that can affect your Brand & Advertising Research

We have spent the last 8 articles talking about the importance of brands and in particular brands that advertise. We also talked about why research is important in your branding and advertising strategy. Then, we explained how to conduct brand and advertising research.

Now we are going to go through 5 common research mistakes that will spoil all your efforts to create a research-based branding and advertising strategy

1) Sampling Frame

• Defining an accurate profile target market is one of the most crucial and important stages to ensure your research findings are actionable. If the profile you define and research is different from the profile that your brand wants to sell to, you will not be able to use your research findings.
• When defining your target market ensure you understand who is currently buying from you, who is buying from your competition, your product/service features and who they benefit. With this, you can define an accurate profile of your target market. You might have and also research more than one target market, that is perfectly fine. The problem arises when you do not research the target market that matters.

2) Sampling Method

• Research is not conducted on the entire population but rather just a small subset of the population (a sample). However, in many cases, the findings from the research are used to generalize to your entire population. As such the methodology you use to select a sample from the population has to be as scientific as possible.
• We recommend random sampling; whereby every member of the population has a fair chance to be selected into the sample. Following the defined rules of random sampling makes the finding more accurate, more generalizable to the population, and more reliable (if you conduct the study again you will get the same results). Also, remember that the quality of your data is as a result of the people you sample.

3) Data Collection Methodology

• After selecting the methodology you would like to use to collect your data, the next step is creating the guides (questionnaire, interview guide or moderator’s guide). This is another process that needs to be as scientific as possible. The questions you ask will give you the findings you asked for. We researchers use the phrase ‘Garbage in, Garbage out’. Ask questions that are biased and confusing, you will get answers that are biased and confusing.
• We recommend you keep your guide simple, straightforward and short. Using the funnel approach, start with questions that are light and exploratory before you ask questions that are deep and detailed. Do not be blunt in your questions, warm participants up.

4) Data Collection

• The actual process of data collection might seem very easy and straightforward, ‘just like talking to someone’, but it is not. It is still very scientific; some processes need to be followed and structures that need to be put in place.
• When collecting the data, in-person or virtual, it is very important to start with an ice breaker. Let people be comfortable and relaxed, this helps you get as much information from them as you like. Be impartial, open-minded, ready to dig deeper and probe on what your participants say. Be aware of your personal biases as well as privileges and how they impact participants’ responses.

5) Reporting

• Now that you have the data, the difficult part is done, but the most sensitive part has just begun. Interpreting the data! There are so many extraneous factors that affect the success of your data based recommendation and you need to account for this risk. If this risk is not accounted for, the time and money put into the research will be wasted.
• When making meaning out of the data, it is essential to look beyond the data and look at the political, economic, social and technological landscape to see how your findings and recommendations fit within this landscape and any changes in this landscape.

Have further questions or branding research & advertising research needs? Contact us at Versa Research!