Professional Reports
We generate professional reports for a variety of question types, including NPS, MaxDiff, Conjoint Analysis, KANO Model, and PSM. These reports will automatically appear on the "Results" page when you add any of these question types into your survey.
NPS Report
NPS report displays the Net Promoter Score (NPS), the number of responses, the distribution of customer categories, and the NPS trend over time.
There are three customer categories in NPS scale:
- Promoters (scores of 9-10): These are people with a high degree of loyalty who will continue to buy and refer others.
- Passives (scores of 7-8): These customers are generally satisfied but not enthusiastic and may consider other competitors' products.
- Detractors (scores of 0-6): These customers are not satisfied with the product or service and may not be loyal to your company.
The Net Promoter Score (NPS) is calculated by the following formula:
NPS =% Promoters - % Detractors
For example, the NPS for this question is 41.67, it is calculated:
50 (% Promoters) - 8.33 (% Detractors) = 41.67
You can also view NPS Trend over different time periods (e.g. by month, week, day, or year) to track changes in customer loyalty and satisfaction.
Maxdiff Report
The MaxDiff results are presented using charts and tables to visualize the data. The following statistical analysis is provided for each attribute:
- Preference %: The percentage of times an attribute was selected as the "best" option in a task. A higher preference percentage indicates a more preferred attribute.
- Probability %: The likelihood that an attribute will be selected as the "best" option in a task. Probability scores range from 0 to 1, with higher scores indicating a higher likelihood of being chosen as the best option.
- P-Value: A p-value less than 0.05 is typically considered to be statistically significant.
- Most Important / Least Important: The number of times an attribute was selected as the most important or least important.
- Frequency: The number of times an attribute was displayed.
- Score: (# of Selected Times for "Best" Label - # of Selected Times for "Worst" Label) / Frequency. A higher score indicates a more important attribute for the respondents.
Conjoint Analysis Report
Multi-attribute Muti-horizontal Product Report
The Conjoint Analysis report shows the attribute importance and conceptual utility.
- Attribute Importance: This table shows the importance of each attribute and level. Within the same attribute, the greater the utility value of a level, the more important that level is to the respondent. The greater the importance of an attribute, the more important that attribute is to the respondent. In here, CPU is the most important attribute for respondents.
Here is the formula for calculating the importance of an attribute:
Importance of an attribute = maximum level utility value for each attribute / sum(maximum level utility value for each attribute) x 100%
For example, the importance of CPU is 28.97% , it is calculated:
21.69 / (21.69 + 21.35 + 15.08 +10.82 + 5.93) x 100% = 28.97%
- Conceptual Utility: The preference ranking of a concept can intuitively show its importance. The higher the utility value, the more important the concept is to the respondents.
Simple Product Report
The simple product reports are presented using charts and tables to visualize the data. The following statistical analysis is provided for each attribute:
- Preference %: The percentage of times an attribute was selected as the "best" option in a task. A higher preference percentage indicates a more preferred attribute.
- Probability %: The likelihood that an attribute will be selected as the "best" option in a task. Probability scores range from 0 to 1, with higher scores indicating a higher likelihood of being chosen as the best option.
- P-Value: A p-value less than 0.05 is typically considered to be statistically significant.
- Selected Counts: The number of times an attribute was selected as the most important.
- Occurrence Counts: The number of times an attribute was displayed.
- Score: Selected Counts / Occurrence Counts. A higher score indicates a more important attribute for the respondents.
NOTE: The conjoint analysis requires a sufficient sample size to calculate relatively accurate data, so reports based on a sufficiently large sample size (number of responses > 100) are meaningful.
KANO Report
In KANO Report, you can see the KANO Attribute, Better Coefficient, and Worse Coefficient for each function or service.
There are five KANO Attribute types. The KANO Attribute type of function or service is decided by the highest score for the feature:
- Basic features: These are basic features that customers expect to be present in a product or service. If this feature is missing, customers will be dissatisfied, but its presence does not necessarily lead to increased satisfaction.
- Performance features: These have a linear relationship with customer satisfaction. As the performance of these features increases, customer satisfaction also increases. Conversely, as the performance of these features decreases, customer satisfaction decreases.
- Excitement features: These features are not expected by customers, but their presence leads to increased satisfaction. They are often the features that differentiate a product or service from its competitors.
- No Difference features: These features do not significantly impact customer satisfaction, regardless of whether they are present or not.
- Reverse features: These features actually decrease customer satisfaction when they are present. These features may be seen as unnecessary or even annoying by customers.
The Better Coefficient measures the degree to which an increase in attribute performance leads to an increase in customer satisfaction, while the Worse Coefficient measures the degree to which a decrease in attribute performance leads to a decrease in customer satisfaction. There are calculate by the following formulas:
Better Coefficient: (Performance % + Excitement %) / (Basic % + Performance % + Excitement % + No difference %)
Worse Coefficient: [(Basic % + Performance %) / (Basic % + Performance % + Excitement % + No difference %) x (-1)
PSM Report
When you click on the PSM Report, you can view four price points:
- Best Price: This is where “Too expensive” and “Too cheap” curves intersect.
- Acceptable Price: This is where “Cost effective” and “High price” curves intersect.
- Maximum Price Point: This is where “Cost effective” and “Too expensive” curves intersect.
- Minimum Price Point: This is where “Too cheap” and “High price” curves intersect.
