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A crosstab is the cross referencing or comparison of two questions to determine how they are interrelated. A crosstab gets its name from the layout of variable definitions into rows and columns.
Total Unduplicated Reach and Frequency Analysis is used to identify a subset of items that, when taken together, maximizes the net level of consumer interest in a superset of choices. Example: you’ve just tested consumer interest in 10 proposed new ice cream flavors but marketing constraints only allow you to introduce three flavors. TURF can identify the three flavors that, taken together, generate the highest net level of interest for the total line.
|Geocoding Survey Responses
Each response is coded with the respondent’s location based on an IP address mapping.
The Trend Analysis module allows you to plot aggregated response data over time. Analyzing trends is useful in detecting patterns in survey responses that could lead to future quality problems, and in forecasting future demand periods.
|Response Based Data Segmentation
Grouping / Data Segmentation based on responses. For example, compare how males answered the survey as opposed to females.
|External Variable Based Data Segmentation
Grouping / Data Segmentation based on external variable passed via survey URL, email list management.
|Time Based Data Segmentation
Grouping / Data Segmentation based on time.
Grouping analysis gives you the ability to segment your data for analysis based on one or more survey responses. Enabling filters on your data offers the capability to analyze data based on gender or age for example.
|Banner Tables (Tabs)
Banner tables allow you to visualize and view your data relative to a single question. For example, if in your survey, one of the questions is “Where are you from?” – You can see how different users responded to all the questions in your survey, based on that question.
With the Dropout Analysis option, you can dive into your drop-out rates (users not completing the survey.) The dropout analysis takes a look at all the users who have not completed the survey and gives you a snapshot view of where they “dropped out” ie. the last question they completed successfully.
Fundamentally Gap Analysis is a very simple concept. For each attribute, the difference between the expectation and delivery is measured and sorted from highest to lowest. It effectively highlights the attributes you need to focus on.
|Multiple Criteria Segmentation
Multiple criteria segmentation is a mechanism to allow for data segments across multiple questions. For example, you could create a data-segment where Gender = Male OR AGE > 25 etc.