Customer experience surveys can gather insights for any business across any industry. However, the real value of surveys lies not just in collecting data but in analyzing it effectively. This is where the statistical treatment of data gets involved. This method cleans, organises, analyses, and interprets data to derive actionable insights.

What is Statistical Treatment of Data?

Statistical treatment uses mathematical techniques to process raw data from market research surveys. These techniques help identify patterns, test hypotheses, and validate findings. It is the application of mathematical techniques to:

• Organize Data: Categorize raw data into meaningful segments.
• Summarize Data: Use measures such as averages or percentages.
• Analyze Data: Identify patterns, relationships, or anomalies.
• Interpret Results: Derive insights to inform decisions.

These steps will ensure accuracy, reliability, and relevance in the survey findings.

For example, high-end brands will publish a research survey to learn about the purchasing patterns of a high-end luxury product. Parameters like income level, lifestyle preferences, brand perception, and purchasing power will play a significant role in influencing a person’s decision to buy the product. So the data needs to be analyzed within these reference frames.

Statistical treatment of data is also responsible for handling errors. Surveys and market research can have margin of errors. One needs to consider systemic and random errors.  

Importance of surveys for statistical treatment

Customer feedback and market research surveys are a common way to gain critical data. Surveys collect massive amounts of qualitative questions and quantitative information. Statistical data treatment turns this customer feedback into actionable insights. Surveys that have a smaller sample size are easier to assess. 

Why do market research surveys need statistical treatment?

1. Data accuracy: Eliminates inconsistencies, outliers, and errors that might skew results.
2. Insight discovery: Identifies trends, correlations, and patterns within customer feedback.
3. Decision support: Provides evidence-backed insights for strategic decision-making.
4. Reliability: Increases the confidence in results by reducing response biases and errors.

Key Methods of Statistical Treatment of Survey Data

1. Data cleaning

Data cleaning removes incomplete responses, duplicate, or irrelevant responses. It ensures that the analysis is based on high-quality data.

• Methods used:
  • Handling missing data: Replace missing values using techniques like mean imputation or deletion.
  • Outlier detection: Use statistical tools like z-scores to identify anomalies.

2. Data organizing

Data organizing will structure customer data into formats suitable for analysis, such as tables or spreadsheets. It facilitates smoother application of statistical methods.

3. Descriptive statistics

Descriptive statistics summarize and organize survey data, making it easier to understand.

• Measures of Central Tendency: Mean, median, and mode highlight central values in your dataset. For example, calculating the average satisfaction score from a customer survey.
• Measures of Dispersion: Range, variance, and standard deviation reveal how spread out the data is. For instance, understanding variability in customer preferences.

 For example: Calculating the average Net Promoter Score (NPS) for a customer satisfaction survey.

4. Inferential statistics

Inferential statistics will draw conclusions about a population based on a sample size. It  helps customer experience professionals generalize findings.

• Methods used:
  • Hypothesis testing: Determines the significance of results (e.g., t-tests, chi-square tests).
  • Confidence Intervals: Estimate population parameters with a degree of certainty.

Example: In a survey of 1,000 users, you might estimate the overall customer satisfaction rate for your entire customer base with a 95% confidence interval.

5. Regression analysis

Regression analysis explores relationships between variables to predict outcomes.

• Linear Regression: Analyze the relationship between two variables, such as advertising spend and customer acquisition.
• Multiple Regression: Examine how multiple factors (e.g., pricing, product quality, customer service) influence customer satisfaction.
• Actionable Tip: Plot regression results visually to communicate trends and predictions effectively to stakeholders.

6. Data visualization

Data visualization will present data in graphical formats such as bar charts, line graphs, or pie charts. It can also visualize data via word cloud or sentiment analysis based on metrics of ‘frustration, happy or sad. This simplifies complex insights for stakeholders.

Common Types of Statistical Treatments for Surveys

1. Frequency analysis: Counts the number of times a response occurs, such as tracking how many customers rate your service as “excellent” on a rating scale. 
2. Cross-tabulation: Examines relationships between different variables, like age groups and satisfaction levels.
3. Correlation analysis: Determines the strength and direction of the relationship between two variables.
4. Sentiment scoring: Applies numerical scores to open-ended feedback to analyze sentiment trends.

Steps to Apply Statistical Treatment of Data

• Outline the survey objective: Get a framework of the purpose of your survey. Who is the target audience? What insights do you wish to gather? A framework can help you make decisions on how you can analyze results. 
• Clean the data: Remove duplicate responses with Merren. Merren can detect multiple similar responses from a single user with duplicate response handling. Handle missing values and standardize formats to ensure accuracy.
• Choose the right methods: Select statistical techniques based on your objectives (e.g., descriptive stats for summaries, regression for predictions).
• Analyze and present data: Use new-age smart BI tools to analyze data and present your findings. You can showcase overall results using graphs or charts on the CX dashboard. 

Example in a customer satisfaction survey

Use Merren to collect survey data on customer satisfaction. Using statistical treatment, you can:

1. Clean the data to remove duplicate responses.
2. Use descriptive statistics to calculate the average satisfaction score.
3. Apply regression analysis to explore how satisfaction correlates with loyalty.
4. Visualize the insights in a dashboard for the marketing team.

Example: you’ve conducted a customer satisfaction survey for an e-commerce platform with 500 responses. Here’s how statistical treatment can be applied:

1. Descriptive Statistics: Calculate the average satisfaction score (e.g., 4.2/5).
2. Inferential Statistics: Use a t-test to determine if satisfaction differs significantly between male and female customers.
3. Regression Analysis: Identify whether factors like delivery time or product quality have a stronger impact on buyer satisfaction.
4. Visualization: Get automated bar charts or scatter plots to showcase trends.

Conclusion

For customer experience professionals, the statistical treatment of survey data is not just a technical exercise—it’s a strategic necessity. It ensures that decisions are backed by robust, reliable insights. By mastering these techniques, CX teams can move closer to delivering exceptional experiences that delight customers and drive loyalty. Sign up for a 14 day free trial to publish highly responsive surveys that help you gauge customer feedback with AI-powered tools.