Building product brands succeed when they are keen to develop and market their products using data, not just assumptions, about customer behaviors and preferences.
Success in the marketplace does mean creating quality products that feature good usability, an appealing design, and durability that is easily accessible through customers’ preferred distribution channels. But you must look beyond your internal team brainstorming sessions to test assumptions among your target customers about which features of your entire go-to-market strategy correlate to increased purchase preference and brand loyalty.
Applying correlation analysis when conducting market research will help your cross-departmental teams identify areas for improvement across your product portfolio and your distribution strategies. This starts by commissioning primary market research to understand the most meaningful relationships between choices that customers are forced to make during product and project research and finally the purchase.
What is Correlation Analysis?
Correlation analysis is a statistical tool that’s used in market research to reveal patterns and relationships within a dataset and its variables. You can use it to uncover and measure relationships and trends between two or more factors.
The result is a numerical output between +1, which is a positive correlation, and -1, a negative correlation. More precisely, a positive relationship is defined as 1>r>0, and a negative relationship is 0>r>-1.
If there is a positive correlation, it means that as the impact of one variable increases, the impact of the other variable increases as well. On the flip side, if there’s a negative correlation, that means as the impact of one variable increases, the impact of the other variable decreases. A correlation that is 0 means there’s a neutral or insignificant relationship between the two variables.
How To Apply Correlation Analysis
The advantage of correlational research is that it enables you to see patterns within data and figure out which variables are connected or have the strongest relationships.
Through the process of correlation analysis, you look at a variety of factors pertaining to your building materials and home improvement products, including marketability factors, financial factors, and production factors. You can also analyze specific characteristics of your products, such as quality, functionality, durability, cost and availability.
These variables and their relationships to one another or to certain outcomes—such as increased sales—are charted on a correlation matrix, and they can be used to make informed decisions at various levels of product development, channel partner strategy, and market positioning efforts.
However, the numerical outputs are not predictive, and correlation is not the same as causation. But if there is a strong correlation between variables, it might be worth investing in further analysis to see if there is a causation relationship as well.
It’s important to note that the metrics derived from correlation analysis are used primarily for identification purposes; they imply that there is more to understand. Metrics from correlation act as markers that often require a more in-depth look to avoid making false assumptions.
Even still, there are a few important ways to use correlation analysis in market research to inform decision-making about your building products.
Use Correlation Analysis to Validate or Debunk Assumptions
Correlation analysis can help you reduce business errors by addressing existing assumptions. For example, you might assume there is a particular feature of a home improvement product that makes it desirable to the customer, when in reality, the channel in which it’s being marketed or the packaging used have a stronger correlation with increased sales. Before investing in a new product that is designed to highlight that feature, you want to research whether the correlation truly exists.
Another example relates to pricing strategy. You might falsely assume sales of a product will go down if the cost goes up. But it’s important to look for other correlations among variables.
- How do the functionality, features, and durability of the product factor in? Is this relationship true across demographics?
- What impact does brand equity play in the decision and price acceptance?
- Could it be that higher cost would deter new customers, but it doesn’t affect sales among customers who’ve purchased from your brand for more than a year or so and understand they’re getting higher value at the higher price point.
With correlation analysis, you can examine these kinds of assumptions and develop new or different hypotheses to test with additional research.
Use Correlation Analysis to Identify Trends
Another way to use correlation analysis for product development is looking for trends. For example, you may discover that sales for a particular product line are correlated with certain life events that are consistent across demographics and their purchasing motivations.
As you develop new items to include in this product line, you can do so with that life event in mind, testing what product features various demographics would find most desirable or what challenges they experience during such life event.
Correlation analysis is about getting an overview of how variables relate to one another and to what degree. It can be used to inform further quantitative market research to ensure you’re investing your efforts and budget in the right areas during the product development process.
Common Methodologies for Using Correlation Analysis in Market Research
The first step is designing a survey to distribute to your target audience, like DIYers, Builders and Contractors, Facilities Managers, and the like. As with any type of quantitative market research, it’s important to have an adequate sample size for respondents, in order to see what significant patterns emerge, without inflating your sample size with irrelevant respondent data that may skew findings.
After you have gone through the necessary process to design your survey and field the survey, you can tabulate and analyze the data.
There are two primary formulas used by market researchers for ranking statistical correlation: Spearman’s rank correlation coefficient formula and the Pearson product-moment correlation coefficient formula.
Spearman’s Rank Correlation Coefficient Formula
Spearman’s rank uses ordinal data—i.e., first, second, third, etc.—to identify the strength and direction of association between two variables. It is typically applied when dealing with qualitative data.
Results are shown on a scatter graph to indicate whether there is a positive or negative correlation, or no correlation at all. Researchers tend to use this formula when it isn’t feasible to make assumptions about probability distributions.
The Pearson R Formula
The Pearson formula, also known as Pearson R, is the more popular method for ranking statistical correlation. It is used when data must be analyzed in relation to specific parameters, such as probability distributions or populations. In this approach, variables have normal distribution and linear relationship to one another, not ranks. Because of this, the metrics tend to be more precise.
Building product companies can use correlation analysis to understand what factors influence the behaviors and purchasing decisions of customers. You can also use this type of market research to identify and analyze trends in the industry and make more informed decisions when it comes to product development and market strategies aimed at improved sales.
Using Market Research to Inform Business Strategies
Building materials companies need to prioritize data-driven decisions when developing and marketing new products. Using correlation analysis to put assumptions to the test and identify trends ensures you are being strategic with how and where you invest as a business.
To help you get started, the Farnsworth Group can work with you on product development research or customer usage and attitude research. By understanding actual purchase drivers and customer behaviors, you can align your product, channel, marketing, and sales teams around a data-backed narrative to gain market share.
