What Is a Weight?
A weight is a feature that adjusts the proportion of responses from specific groups to ensure data representativeness when the composition of survey respondents differs from actual market conditions (the population).
For example, if 70% of actual service users are in their 20s and 30s but survey responses were collected equally by age group (25% each), the opinions of those in their 20s and 30s may be underrepresented. Applying weights in this case lets you obtain accurate analysis results aligned with the actual market proportion.
When Do You Need Weight Settings?
✅ When the proportion of survey respondents differs from the actual customer distribution
"70% of our service users are in their 20s, but survey responses came in at 25% per age group. I want to see results aligned with actual market conditions. 🤔"
🧙 Here's how you can use it
Using the weight feature, you can artificially adjust the proportion of collected data. By assigning a higher weight to response data from those in their 20s, you can correct the overall results to reflect actual market conditions centered on those in their 20s.
✅ When you want to give more weight to the voice of a specific target in the analysis
"The proportion of heavy users among all respondents is low, but I want to treat their opinions as more important in the analysis results. 🤔"
🧙 Here's how you can use it
Try setting weights for a specific group with high purchase frequency or amount. This increases the influence of the minority respondents' opinions on the overall statistics, helping you derive insights centered on your core target.
How to Set Weights
📌 The weight setting feature is available on the Professional or Enterprise plan.
1️⃣ Select the target variable to apply weights to.
step 1. Click [Variables] at the top of the Analytics screen, then select [Create variable > Weight] from the left menu.
step 2. In the [Target variable] dropdown menu, select the variable to use as the reference for applying weights (e.g., age, gender, etc.). When a variable is selected, the value labels and current frequency (%) of that variable are displayed below.
2️⃣ Enter the target figures (desired %).
step 1. Enter the 'New variable name' and 'New variable label.' It is a good idea to write them clearly so it's evident that this is a variable with weights applied.
step 2. In the [Desired %] column of the weight work area, enter the proportion that matches actual market conditions or your analysis goal. When a proportion is entered, the weight value is automatically calculated in the [Weight F] column, and the 'Desired % Total' at the bottom also updates accordingly.
💡 Usage tip | The 'Desired % Total' value must be exactly 100% in order to create the variable.
3️⃣ Create the weight variable and apply it to analysis.
step 1. Once all settings are complete, click the [Create variable] button in the upper right. A new numeric variable with weights applied is created.
step 2. The created variable can be viewed in the 'Variable list,' and can be used directly in the 'Results tab,' 'Cross-tab tab,' etc. of Analytics — just like a standard choice-type question.
Frequently Asked Questions
Q. After applying weights, response counts are displayed as decimals. Is this an error?
A. This is not an error — it is a normal occurrence. Since weights are calculated by multiplying each response by a specific value, the response count after applying weights may appear in decimal units.
Q. Can weights be set based on multiple variables simultaneously?
A. Currently, the weight feature can only be set based on a single variable.
If you need to consider multiple criteria together — such as gender and age — first use the [Combination] feature to create a single combined variable like 'gender + age,' then set weights based on that variable.
Do you have a good understanding of weights?
If you still have questions after reviewing this guide, please contact us anytime via the [Customer Support icon] in the bottom right corner of your screen. Our team will do its best to help you resolve any difficulties you're experiencing.