In these turbulent times, many companies are trying their best to adapt to best suit the needs of their customers to achieve long-term brand loyalty. The key to achieving this loyalty is by truly taking time to understand the customer and what they expect of your brand. However, how can you know you are taking the correct steps to best understand your customers?
Brands use market research for a wide variety of reasons to tap into the voice of their customers. Qualitative research methods such as focus groups or in-depth one-on-one interviews allow the customer to fully express their opinion about a variety of topics and allow for rich insights to the factors which may drive or impact brand loyalty.
In order to truly understand the full picture of your customer base, it is wise to utilize quantitative research via surveys to determine if the metrics enhance the story being written by the qualitative data.
Understanding Quantitative Research Methods: An Overview
Quantitative research is any type of research which focuses on numerical data. These types of research projects can be conducted at any stage of wanting to understand feedback, from exploring what customers might think of a particular offering to gauging feedback after a product launch in order to make any needed tweaks. Examples of quantitative research you may have encountered include surveys or polls.
Quantitative research methods are a good way to quickly answer questions to meet your business objectives and empower companies the ability to capture various metrics to support or reject proposed next steps.
To determine which tools to use to better understand your customers, it is important to know different quantitative methodologies exist, specifically through surveys. Although there are many different types of surveys employed by companies to understand their customers, we’ve outlined some common types of surveys below.
Please note this list is not exhaustive, but rather meant to outline different options available to businesses to answer important questions:
- Customer satisfaction surveys, in which a respondent may be asked to share feedback about a particular experience, or about opinions of the company overall. Some companies choose to track data from these surveys over time as a way of measuring their overall performance.
- Messaging or packaging surveys, when respondents are shown messages or packaging and asked to respond directly about what they see.
- MaxDiff or conjoint surveys, or surveys which expose respondents to a variety of attributes such as flavors, adjectives, or other categories, in order to determine which are most desirable among customers.
- Pricing surveys, such as a Van Westendorp, asks respondents to rate how much they would be willing to pay for a product or service.
- Journey mapping surveys, which ask respondents a series of formulated questions to gauge feedback about a variety of touchpoints related to the customer experience. For example, a company may utilize a journey mapping survey when they want to understand what steps their customers took when signing up for a certain product or service.
- Persona and behavioral surveys, which are carefully crafted in order to evaluate and categorize your customer base into different groups so you can better understand the different personas or target groups among your customer base.
- Longitudinal studies, sometimes in the form of tracker surveys or other surveys meant to be repeated, are sent to your customer base over a span of time to understand behaviors or perceptions in the moment. The findings are compared over time to examine shifts.
Of course, surveys often contain closed-ended questions, but a well-crafted survey will contain a variety of both closed and open-ended responses to ensure reasons for ratings or specific feedback are well understood. For example, when asking a customer to rate their overall satisfaction of your brand on a scale from 1-10, having an open-ended question after asking them to explain the rating will allow the respondent to share deeper insights or specific examples.
Mixing these question types allows for a well-rounded view of the customer experience through richer data. Therefore, it is important to incorporate a mix of quantitative and qualitative questions into the survey design to ensure you can fully understand the feedback customers provide.
Analyzing Quantitative Data to Understand the Full Story
After conducting a survey, it is sometimes difficult to know where to begin to understand the data. Large, quantitative data sets collected in a survey can be a gold mine of understanding the customer if properly analyzed. Depending on the data, the analysis plan could vary, with each individual test or analysis providing its own spin to help recognize the important takeaways. Some of these analyses include:
- Examining proportions or looking at two sets of percentages as part of your overall customer base, which allows you to see whether one item or group of items performs better than another. For example, imagine a survey showed two potential advertisements and asked respondents how appealing each is and how likely they would be to act if they saw that advertisement. The percentages for these metrics would be examined side-by-side to determine whether one advertisement ranks higher at a statistically significant level, likely indicating a better performing advertisement.
- A comparison of averages is similar to proportions but has a more robust analysis. By examining the means between two samples, you can identify shifts in a variety of metrics, such as overall satisfaction. For example, a tracking survey may ask respondents to report their overall satisfaction with the company or brand. The averages can then be tested for significance to determine whether there’s been a positive or negative shift over time. Alternatively, this can be used to identify significant differences between particular demographics of interest, such as different age groups, different genders, or different regions. This sort of analysis is best when there is a numerical scale.
- Correlation is an analysis which measures the relation between two variables. The analysis provides a coefficient from a 0 to 1 scale for the items you’re examining. The closer the coefficient is to 1, the stronger the relation between the variables. For example, a company may seek to understand the correlation between customer satisfaction and perceptions of the company. There may be a strong positive correlation between customer satisfaction and experiences with friendly staff/customer service. It is important to note, however, that correlation does not mean causation. In order to determine whether one variable causes another variable, you will need to consider a more robust analysis.
- Regression is a good measure to use if you would like to understand the causality behind your metrics. Using the same example, a regression analysis measures how much experiences with friendly staff/customer service impacts customer satisfaction ratings. This type of analysis is also helpful in understanding what factors are most important to different customer segments or groups. It will be easier to predict which products or services these different groups will be likely to use.
When it comes to analysis, there are many options available ranging from basic understanding to complex algorithms and other advanced analyses to help you bring the stories within your quantitative data to life. Most importantly, different analyses can be conducted within the same set of survey data.
For example, it may make more sense to analyze some questions using a proportion analysis, but other variables or questions using a correlation or regression analysis. If you are not as familiar with the many analysis options, working with a partner during your survey design process and data analysis stage can help you easily identify which test(s) are right for your objectives.
When considering analysis, however, it is important to make sure you have enough sample to justify the analysis. To conduct a comparison between groups, for example, usually requires a minimum of 30 respondents per individual group. The larger the sample, the more confident you can be in your results representing your overall population.
However, if you don’t have as many respondents, don’t fret! Quantitative research among smaller groups can still be considered; it should just be viewed as directional in nature. Finally, if you find your sample is skewed towards a particular group, you may want to consider weighting the data to ensure the numbers and analysis accurately reflect your overall customer base.
Understanding the Customer: A Case Study in Utilities
As previously mentioned, it is important to integrate qualitative and quantitative research methods to draw a clear roadmap to understanding the customer experience. We’d like to take a moment to illustrate an example of how quantitative and qualitative research methods can blend together to develop a full, clear picture of the customer experience.
A utility company recognized their bill layout remained consistent for decades and sought to incorporate a modern design which would be easier for customers to read and understand. In order to measure this, the company turned to a mix of surveys and focus groups to understand how customers felt about the original bill design as well as two new designs.
In the first survey in this project, customers were shown the original bill design and one bill redesign layout. They were asked to select their preferred design, and then asked how the new bill design would impact perceptions of the company.
Customers were also asked whether the new layout highlights the most important information. In a second survey sent a few months later, the company asked questions about how closely customers look at the original bill. Then, customers rated different components of the new bill design such as the due date and usage charts to determine how well customers understand the information in the new layout.
Some adjustments were made to potential new bill designs based on the customer feedback. The company then conducted a series of focus groups to conduct a deeper analysis into the new bill prototypes. The same three bill designs were evaluated among a larger group of customers in an online survey.
Customers saw one bill design and were asked how easy it was to locate information in the bill, likes and dislikes of the bill design, whether they prefer larger or smaller font, and which sections of the bill they prefer to see on the first page of the new bill. Then, customers were exposed to all three bill layouts and asked to select the bill design they most preferred.
Customers provided an overwhelming positive response to the new bill designs, finding them easier to read and locate important information. Bold colors also made the bill more visually appealing for most compared to the original design.
Throughout the process, the direct feedback customers provided the company allowed for a mutually beneficial understanding of customer needs. It also allowed customers to feel as though they were involved in the process and feel heard. After the conclusion of the project, customers expressed their gratitude for being involved and being able to contribute to their own experience as well as that of all customers.
Understanding the Customer: Practical Applications and Beyond
Taking this all together, it is important to obtain feedback from your customers often to ensure you are meeting their needs and expectations. Looping your customers in at every stage of product development can be critical to ensure success. It is especially important to include some closed-ended, quantitative questions in your research to ensure you can measure any specific performance indicators you may need.
There are several survey tools and quantitative platforms available to help you determine which mode(s) of reaching your customers is best. Different research questions can have different strategies and needs, but a good way to help blend qualitative and quantitative research methods on an ongoing basis is through an online co-creation community. Reaching out to customers via an online co-creation community may even provide richer insights, as you have the same group of customers available at your fingertips, ready to provide feedback.
This is especially valuable when conducting longitudinal or tracking studies, as you can easily see differences among the same customers over time, allowing for more ability to probe and deep dive into topics of interest as they come up.
Additionally, reaching out to the same select group of customers allows them to feel included in the process and feel heard, which is likely to inspire a sense of loyalty to your brand. Tapping into a pre-existing online community creates a mutually beneficial relationship between company and customer and makes it likely that your customers will come back to provide more feedback for future surveys and other quantitative projects.