Research Design & Data Collection Practices for Brand Health and U&A Surveys

Understanding customer attitudes and motivations, as well as how they purchase and use certain products, is vital to creating a holistic strategy around new product development and marketing. However, you don’t want to base your most important business decisions on assumptions or best guesses.

Quantitative market research offers a means to confirm ideas and theories about consumer usage and attitudes (U&A) by delivering objective and definitive statistics or numerical data.

The question is how to go about collecting that information, as there are different tools and approaches available to companies in the home improvement and building industries. One of the most popular quantitative data collection methods is surveying a targeted group of respondents using clear parameters and a methodical approach to garner reliable facts and figures.

What is Quantitative Research?

The purpose of quantitative research is to generate measurable data. While qualitative methods have their place in U&A research, quantitative research is essential for answering the key questions at scale: Who, what, when, where, why and how?

The figures and numbers you collect are then used to quantify product use, attitudes, behaviors, preferences, purchase factors and other defined variables. Often, a quantitative observation can help you support or disprove a hypothesis about a particular trend or occurrence using numerical measurements.

In short, quantitative usage and attitude research provides context and validates. It is a fundamental understanding of the customer. It is valued for its consistency and accuracy—especially when you take the right approach and employ appropriate tools that are tailored to your product and brand.

How to Structure a Quantitative U&A Survey

Surveys are a useful tool for collecting feedback that can be analyzed and translated into numerical evidence.

However, the quality of responses and value of the data you gather depend on an effective survey design. You also need a suitable sample size and respondents who fit within your key demographics. An effective U&A survey will include a variety of standard single choice, multiple choice, rating, ranking and open-ended questions.

Here are the different components of an effective U&A survey that can be used to gather valuable quantitative data:

1. Screeners and Demographics

In order to infer relevant trends in customer usage and attitudes, you need to have definitive data on demographics. At the start of a survey, respondents will provide this basic information, which might include factors such as age, gender, and business revenues or household income. For construction professionals or local showrooms, you might also want to ask about the size of their firm or types of projects they focus on. These demographical data points will be used for tabulations and comparisons among different segments of customers. Demographic questions also act as screeners to filter out prospective respondents who don’t fit your target audience.

Uncleaned Data = Bad Corporate Strategies Below is empirical evidence of the degree to which data quality impacts the actions your team may or may not take after getting the results from your research. In these real research projects, you can clearly see where the recommendations given to the client would have been wrong had our team not gone through The Farnsworth Group's industry leading approach to data quality and cleansing, which is necessary to rid erroneous responses from the client’s data set. Here’s what we found:

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2. Usage Behaviors Surrounding a Product

The next section of the survey focuses on the respondent’s typical use of the product category in question. Questions would revolve around the type of product being used by the respondent, what type of projects they use it for, and the frequency of use. Whether it be understanding primary use preferences for certain flooring materials, outdoor home improvement equipment or something purchased in higher frequency, like paints or stains. This data will be used to understand differences in use by various demographics.

3. Product Category Awareness and Perceptions

Respondents will provide their level of familiarity with the product category and particular product in question, as well as their awareness of the brands associated with it.

This section is designed to measure each respondent’s overall awareness and perceptions surrounding the research category to give you a baseline for evaluating other data gleaned from the survey. For example, if a respondent is deeply familiar with a product category but unfamiliar with your brand, that information might be interpreted differently than a respondent who simply has limited knowledge about the entire product category to begin with.

4. Product Selection Factors

Next, respondents will be asked questions that assess individual product features and identify which features are most important to or preferred by the respondents. For this portion of a quantitative survey, you might incorporate questions that ask respondents to rate and rank the importance of product attributes on a scale, from “not at all important” to “extremely important.” You then provide a list of the attributes related specifically to the product. Respondents will specify how they use these features (if applicable) and how likely they would be to purchase products that include certain attributes, features, or technology versus those without.

One popular approach to measuring these factors is called Maximum Difference Analysis, or MaxDiff. This tool asks respondents to identify the most preferred and least preferred attribute among a set of four to five choices. The exercise is generally repeated multiple times in a survey with alternative computer-generated sets of four or five attributes. In this exercise, price is excluded from the set of attributes.

The greatest strength of a MaxDiff analysis is that it delivers an accurate hierarchy among a larger set of options in a more “invisible” or indirect manner than simply asking which ones they prefer. The output of this practice is a defined hierarchy of product attributes so you can get an accurate picture, backed by numerical evidence, of their importance to respondents.

5. Product Satisfaction

Respondents will provide their level of satisfaction and performance ratings for the product they’ve experienced. This data will uncover potential needs not being met and gaps in their expectations versus what is actually delivered. Using this data will also allow for correlation analysis against product factor importance. This type of analysis will help you understand what is truly driving satisfaction of the product or brand.

6. Top Sources for Product Research and Purchase

Finally, your U&A survey should also include a section about where respondents gather information regarding the product category and where they typically shop for it and make their final purchase. What channels of distribution do they rely on most and why? What kind of information are they researching for when they are considering a purchase? Knowing the answers to this provides you insight into whether their product selections are influenced by their particular shopping sources and will also help you identify growth or decline in any of your product distribution channels.

7. Brand Use and Perceptions

Finally, you can delve into questions about specific brands to measure brand awareness and perceptions of your brand and competitor brands. Through the course of questioning, respondents will provide information about which brands they are aware of and the brand(s) they’ve purchased in the past.

This data will allow for a competitive assessment, highlighting areas of differentiation and the perceived market position between you and competitors. You will learn what respondents associate with your brand, whether positive or negative, and you can use that information to make modifications to your products, your messaging, your distribution channels or other factors to better address customers’ purchase motivations and preferences.

Collecting Quantitative Data to Improve Product Positioning

Surveys are a valuable tool for gathering information about how your customers think and act. Using real data, you can tailor your product development and marketing strategies to meet their particular wants and needs and as a secondary result, improve your brand health. However, the effectiveness of a quantitative survey depends on how it is designed.

Our team at The Farnsworth Group can help you develop a quantitative customer brand, usage, and attitude assessment to gather valuable insights into what products customers are buying, where, how often and why, enabling you to distinguish what drives and motivates their decisions across their journey and implement new strategies accordingly.