Guide to analyzing survey result

Gathering valuable customer feedback poses challenges that extend beyond merely formulating strong survey questions. Effective insights exist behind a well-executed distribution system that targets respondents at the right time. However, the journey doesn't end with data collection; survey data requires careful sorting and analysis.

What is Survey Analysis?

Survey analysis is the process of examining and making sense of information collected through surveys. This process involves applying statistical methods, data manipulation techniques, and analytical frameworks to derive meaningful insights, trends, and patterns from the collected responses.
Imagine you ask a group of people questions about their opinions, experiences, or preferences. After collecting their responses, survey analysis involves going through that data to find patterns, trends, and important information. It's like putting together puzzle pieces to understand what people are saying and to make informed decisions based on their feedback. This analysis helps identify key insights, such as popular opinions, areas for improvement, or trends, which can be valuable for businesses, organizations, or researchers in making informed decisions or improvements based on what the surveyed people shared.

This blog not only covers the intricacies of survey result analysis but also delves into strategies for effectively presenting findings throughout your organization.

How to Evaluate Survey Findings

In order to make the best decisions from the findings of your survey result, there are 5 areas we need to look into. As follows:
1. Comprehend the Four Measurement Levels
2. Choose the right survey tool
3. Choose your survey questions
4. Prioritize quantitative data
5. Utilize Cross-Tabulation for Targeted Insights

Now let us go over these important elements one after the other.

1. Comprehend the Four Measurement Levels

Before delving into data analysis, it's crucial to grasp the four measurement levels that dictate how survey questions are assessed. These levels—nominal scales, ordinal scales, interval scales, and ratio scales—guide the appropriate statistical analyses.

Nominal Scale:  Classifying data without quantitative value, such as selecting a car brand.
Ordinal Scale: Depicting value order, like ranking reasons for using a laptop.

Interval Scale:  Representing order and difference without a true zero point, as seen in an IQ test.
Ratio Scale: Similar to interval scales but with a true zero point, like selecting the average amount spent on online shopping.

2. Choose the right survey tool

A good survey tool streamlines the process of data analysis, making it simpler to understand what people are saying, and that makes analyzing the results much smoother. It's all about having the right tools to get the job done well! Consider using a tool like Enquete, which has analysis features to guide you through the entire data analysis and presentation.

3. Choose Your Survey Question(s)

Once familiar with measurement levels, pinpoint the overarching survey question(s) you aim to answer, for instance, "How do respondents rate our brand?" Design survey questions aligned with this goal, such as "How likely are you to recommend our brand to others?" Remember to include both close-ended and open-ended questions.

Close-Ended Questions: Limited set of answers, offering pre-determined options.
Open-Ended Questions: Encourages respondents to explain their opinions, providing more qualitative insights.

4. Prioritize Quantitative Data Analysis

Quantitative data, derived from close-ended questions, utilizes statistics to draw objective conclusions. Starting with quantitative data allows for a clearer understanding of qualitative data. For instance, if 60% of customers express dissatisfaction, focus on negative user experience reviews to identify and rectify pain points.

5. Utilize Cross-Tabulation for Targeted Insights

To enhance the accuracy of survey results and prevent data skewing, use cross-tabulation to segment responses based on demographics or other variables.

Split Up Data by Demographics: Analyze specific audience segments to draw targeted insights.
Cross-Tabulation: Compares two sets of data within one chart, revealing participant responses to different questions.

By employing cross-tabulation and considering specific variables, you can ensure that your survey results accurately represent your intended target audience.

How to present survey results

The way we present your survey results and findings can affect the way the findings are interpreted and how decisions are derived from them. There are several ways to present survey results. Let's consider each one and the best kind of survey to use each one.

1. Use a graph or chart.

Utilizing graphs and charts provides a visually engaging method for presenting data. The colors and patterns not only enhance visual appeal but also contribute to a clearer understanding of the information being conveyed. Selecting an appropriate graph that effectively emphasizes your results is crucial for ensuring the relevance and impact of the visual representation.
Depending on the survey you've conducted, there are many types of graphs and charts you can use. A few options you can choose from are pie charts, Venn diagrams, line graphs, scatter plots, histograms, pictograms, and more. Be sure to pick one that accurately displays your data and is clear to your stakeholders.

2. Create a data table

Tables are a great way to share numerical data. You can use software like SPSS or Excel to easily display data. Using tables to present survey results involves organizing information in a structured way for clarity. Start by labeling columns with headers that describe the data they contain. Use rows to categorize information, whether it's different survey questions, demographic groups, or response options. Keep the format consistent throughout the table, including units, formats, and styles.
To emphasize key findings or trends, consider using different font styles, colors, or shading. Include totals or percentages where relevant to provide a summarized view of the data. Keep the table concise, focusing on essential information to avoid clutter.
For added context, use footnotes or comments to explain specific details within the table. Ensure readability with legible fonts and appropriate spacing. A well-constructed table simplifies complex survey data, making it more accessible and understandable for your audience.

3. Tell a story

A crucial objective in effective data analysis is to interconnect information seamlessly, akin to constructing a house. Consider certain data as the foundational underpinning of your narrative, with every point in your presentation intricately linked to this base. Much like the walls of a house, structure your key findings to support and reinforce this foundation. These findings, in turn, culminate in the conclusion of your research, which acts as the overarching roof – the primary message you aim to convey during your data analysis presentation.

Even when dealing with stakeholders possessing diverse levels of analytical proficiency, the art of storytelling remains universally impactful. Regardless of your team's analytical sophistication, crafting a narrative around the central conclusion derived from the data is essential. Take the time to discern the pivotal point to which the data leads, and weave a compelling story around that core conclusion.

4. Visual Representations

Sometimes combining visuals with text creates a thorough description of your findings. In these cases, a presentation could be a good fit for showcasing your data. This gives you a chance to present the earlier stages of your survey, including research questions, hypotheses, survey questions, and methods of analysis.

Analyse data like a pro

Collecting data is a crucial first step, but its true value emerges through a strategic analysis process. Without this critical step, attaining conclusions that align with your business goals might prove challenging. This post offers valuable tips to guide you in crafting meticulous data analyses, ensuring robust support for even the most ambitious projects. By following these guidelines, you can unlock the full potential of your collected data and make informed decisions that contribute meaningfully to your business objectives.