Market research is imperative to today’s utilities industry. Understanding current and potential customer needs and wants helps us make more informed business decisions and aids in the development of focused business strategies. Customer surveys take the guesswork and the assumptions out of determining what initiatives will likely be successful and cost effective. Consistently working to understand our customer base provides us with the direct feedback and a communication channel to really “hear” what they are asking of us. Data analytics allows us to target customers and obtain specific feedback for any topic, including opinions on utility rate changes, reliability, renewables, customer programs, and much more.
The industry’s leading provider of customer research for community-owned utilities, GreatBlue Research is sharing insights into the world of market research. Below, the GreatBlue team explains how sampling is used when surveying customers, why it’s important to capture your community’s demographic data, and the fundamentals of data analysis when interpreting results.
Sampling: What is it and How is it Done?
Sampling, by definition, is a statistical analysis tool used to analyze a subset of a larger data set. You can think of it as a representative snapshot of a larger population.
Sampling comes with many advantages. Besides not having to conduct a study with the entire population, sampling allows us to gather insights in a cost-effective, time efficient manner. Additionally, different sample sizes come with different advantages. Smaller samples allow for quicker analysis turnarounds while larger sample sizes provide increased accuracy. Statistically, the larger sample size, the smaller margin of error, and vice versa.
The type of sampling utilized often depends on the data set and the goals and objectives of a research study. The most common types of sampling options include:
- Random Sampling: Software is utilized to randomly select subjects from the whole population.
- Stratified Sampling: Smaller sets of data are created from the original data set based on a common factor (i.e. hair color), and samples are randomly collected from each group.
- Cluster Sampling: The larger data set is divided up into subsets (clusters), then a random sampling of clusters is analyzed.
- Systematic Sampling: A sample is created in which an interval is used to extract data from the larger population. For example, if using an Excel spreadsheet, setting an interval of every ten rows to extract data would be systematic sampling.
Demographics: What Are They And Why Do They Matter?
You may notice that in every study we conduct, there are questions pertaining to age, gender, ethnicity, household income, education level, household dwelling type, etc. that seem to have nothing to do with the rest of the study. These seemingly irrelevant questions are called demographics and actually have a very important role in understanding the target audience.
Demographics capture a snapshot of the socioeconomic background of the sample. In other words, demographics tell the story of the respondent. These snapshots provide critical information about the types of people surveyed, and can be used in a variety of applications. For example, say an electric utility company conducted a survey and concluded that 46% of respondents were over the age of 65, female, have a household income of $50,000 or more, and owned their homes. The utility company can use this demographic data coupled with the responses captured in the survey questions to tailor their marketing efforts, programs and/or service offerings to that specific customer segment. This is just a basic example and market researchers have gotten very creative in the way they use demographics to explain data on a deeper level.
A Peek Behind the Curtain into the World of Data Analysis
One of the more mysterious steps in the market research process is the data analysis stage. Oftentimes, we use phrases such as multiple regression, factor analysis, cluster analysis and multidimensional scaling do describe data analysis, but what do all of those mean and how are they used?
While those phrases may seem intimidating or complicated at first glance, they are simply used to describe the techniques data analysts utilize to interpret a raw data set. These raw data sets are usually large files filled with hundreds, if not, thousands of responses all displayed as numbers. In the market research world, these numbers usually correspond to certain, preselected responses to different questions. These files contain all survey questions asked of respondents, verbatim open-ended responses, and incomplete surveys. It’s a data analyst’s job to sort through these data sets using one or a combination of the following common methods:
- Multiple Regression: This method, in essence, answers the question of how one variable changes, when another is intentionally changed. For example, this method can answer how sales revenues changed based on placement of advertisements, advertising budget, etc.
- Factor Analysis: This method identifies how underlying variables relate to each other. In every data set, there are bound to be correlations between two or more variables. Factor analysis determines which variables have the strongest correlations. A market researcher can use factor analysis to find the best combination of factors that are attractive to customers.
- Cluster Analysis: This method separates data into separate, homogeneous groups based on alike traits. Common traits are based on demographics, but can be as specific as an analyst wants them to be. This method is useful to separate consumers into market segments.
- Multidimensional Scaling: Arguably one of the more abstract methods of data analysis, multidimensional scaling is useful for comparing competing brands and products. For example, different types of air fresheners can be compared based on scent strength, scent type, and longevity. The competing products would be put onto a perceptual map, with the distance between these brands highlighting the dissimilarities.
These are just some of the tools GreatBlue’s data analysts use to interpret data from each and every study the firm conducts.
The market research partner of Hometown Connections, Inc. and the leading customer opinion firm serving community-owned utilities, GreatBlue Research Inc. is a full-service, in-house market and public policy research company utilizing a variety of methodologies, including telephone, email, web-based surveys, focus groups, and one-on-one interviews.