品牌名稱測試市場研究

Managers and executives are often confronted with the need to understand how their Brand and Product names are interpreted by customers.

In Brand Name Testing Market Research, we test and interpret how brand and product names impact purchasing decision making, customer engagement and our clients’ strategic priorities.

Combining Qualitative and Quantitative research can provide in-depth strategic insight for managers.

定性研究

The purpose of a qualitative phase is to explore the mindset of the customer and to elicit the voice of the customer. The qualitative phase will obtain conscious and sub-conscious thoughts which will help us to develop the quantitative survey instrument.

Qualitative research can deliver a wide range of information regarding attitudes, beliefs, and opinions, as well as to allow for an examination of different social and psychological backgrounds.

Qualitative research also provides clients with the possibility to better understand and evaluate consumers’ behaviors and emotions. Consequentially, the client will be in a better position to understand what names will elicit a positive or negative response from customers.

These focus group conversations will help us probe and interact further into perspectives of current and potential customers. Some of the areas focus group discussions will explore:

• Customer demographics
• How product names influence their buying process
• Positive and negative reactions to potential names
• What a name is lacking
• How memorable a name is
• What thoughts potential names bring to mind
• Why they would choose name A over name B
• Current and target customer’s lifestyles and why they use products
• Their emotions toward the products
• Shopping and purchasing habits

量化研究

The purpose of the quantitative phase is to rate and rank each of the 4 potential product names. The quantitative research will give insights to product name preferences, market message, and met and unmet needs.

Given our experience with similar projects, we may recommend a quantitative phase to quantify “the voice of the customer.”  This online consumer survey will quantify:

• Preference level of potential product names
• The general importance level of a product’s name
• Whether they would not purchase a product because of a name
• Frequency of how often they would purchase a product with a certain name
• Frequency of purchase and how they use products
• Their attitudes and usage of products

For each product, 2 questions with a 3rd contingent follow-up question could be asked:

1. The first question is a fixed-sum allocation question to elicit relative preference, e.g., “Allocate 20 points among the following four product names, such that your allocation represents your relative preference for the names.” This kind of question measures strength of preference (amenable to interval or ratio data interpretations) rather than just rank (ordinal data). Importantly, the total number of points should be an even multiple of the number of choices presented, so respondents who really are indifferent can give the names the same number of points (here, 4×5=20).

2. The second question probes for absolute liking of the top choice, e.g., “On a scale of 1 to 7, where 1 represents strong disliking and 7 represents strong liking, how would you rate your most preferred choice from question #1?” The 7-point scale is the classic Likert scale, but other scales are possible; however, keep in mind that using a scale with an odd number of points allows the respondent to be neutral, whereas a scale with an even number of points (such as the 1-10 scale) deprives the respondent of a mathematically neutral response, which I think would be desirable in this case.

3. The third question comes into play only for those respondents whose rating in #2 falls below a certain threshold, and probes for a better alternative, e.g., “Can you think of a possible product name that, in your own personal view, is better than [the name you ranked #1 in Q2]?” This question should allow for no response and should also be timed with a short “expiration”, such as 5 seconds, to get a reply that is more spontaneous rather than considered.

Some Alternatives & Observations:

The survey could just ask Q2 for all 4 products, and infer the relative preference from the absolute liking. However, this type of question does not engage the respondent as deeply as the fixed-sum question, and is vulnerable to “across the board” answers, due to either respondent reluctance to make judgments or “spam responses and take the money” abuse.

Speaking of candidate names, some companies usually assign names that have some semantic connection with the product. However, within the same category of product, names may be interchangeable. If this is true, then we might reasonably ask respondents a “matching” question. For example, “from this pool of 7 candidate product names, pick the best name for each of these 4 products.”

Some clients have different customer profiles in mind for these four products. These profiles are largely combinations of categorical variables (gender, age group, race/ethnicity, etc.), so this information would be collected to see if different candidate names have varying levels of appeal to different demographic groups.

戰略分析

The strategic analysis will take the learning from Phase I: Qualitative Research – Focus Groups, 和 Phase II: Quantitative Research – Consumer Online Surveys and present them in a coherent and effective manner. The deliverable may be a comprehensive PowerPoint report identifying:

1. Customers perception of potential product name
2. Positive reactions
3. Negative reactions
4. Factors influencing customer’s perceptions of product names
5. Advanced Analytics
6. Best possible name for each product
7. Name recommendations
8. 結論

In terms of advanced analytics, it is better to pose questions first and then pick the tools. However, given that we’ve been talking about discrete choices in both the target variable (product name) and demographics, we can certainly say that we could use non-parametric statistical tests to tease out relationships between ordinal data.

Cluster analyses and logistic regressions might be useful depending on what the client specifically wants to know and, more importantly, the actual results.

Example:

• Within target variables: do respondents who prefer one name tend to prefer another? Are there “clusters” of names that are preferred?
• Between target variable and demographics: do certain respondent profiles prefer certain name(s)? Is absolute liking associated with some demographic characteristic?

The final deliverable is a high-impact report with an action plan for managers.