In this simulated research project, we're looking into the market of theme-park resorts.

Please note that much of this data is actual data collected from a previous research project that's been scrubbed, relabeled, and repurposed for this hypothetical project.

This dashboard demonstrates the results of a psychographic segmentation.

Our hypothetical client, Adventure Rezorts, is interested in growing their customer base among vacation travelers. To get a better idea of who their target customer might be, they've decided to segment the population on the basis of attitudes, beliefs, and/or opinions around what motivates people to take vacations.

Because this type of research focuses on core attitudes and beliefs around the product category of interest, psychographic segmentations tend to be stable time. The results can be used to develop customer profiles (e.g. personas) to use as a foundation for future product development research.

Percent of sample

Vacation Motivations

Purchase Simulation

Survey respondents performed a choice-based-conjoint (CBC) exercise where they were asked to choose between different hypothetical vacation packages.

In a CBC, respondents perform 10-15 choice tasks where they're asked to choose which product in a set they prefer, and whether or not they would purchase that option if it were available. This allows the researcher to predict product preference as a function of the attributes that describe that product.

Preference Share: If respondents HAD to choose one of the following vacation packages below, which would they prefer?

Purchase Share: What percentage of respondents would purchase each vacation package if it were available in the market?

A good way to use this tool is define a product that closely matches YOUR offering, and then your COMPETITOR'S offering. Then, use the final product description to explore what NEW features gain market share from your competitor, while minimizing canabalization of market share from your current product offering. This is especially useful if you're testing product features designed to appeal to 1 or 2 target segments.

Package 1

Package 2

Package 3

Preference Share

Purchase Share

Ticket Pricing

van Westendorp pricing is used to find a range of acceptable prices.

As prices increase, FEWER people will consider the price cheap, and FEWER people will think that the price is NOT expensive.

Similarly, as prices increase, MORE people will consider the price to NOT be a bargain, and MORE people will consider the price to be expensive.

Prices BELOW the first intersection end will lose customers. More people would consider the product too cheap at prices LOWER than this than those who think it would no longer be a bargain at that price.

Prices ABOVE the second intersection will lose customers. More people would consider the product too expensive at prices HIGHER than this than those who'd consider it expensive, but still worth considering.


Feature Demand

What's the marginal added value of vacation package features?

The calculations shown here make the assumption that customers are ONLY influenced by the selected attribute. In other words, it assumes they are are indifferent to every other feature offered in the vacation package. This allows us to focus on the feature/price tradeoffs within a single attribute.

For example, all other things being equal, there's a 50% chance that a customer would buy a night's stay at '5 Flags' for $211 AND a 50% chance that a customer would buy a night's stay at 'Adventure Rezort' for $257. At this purchase probability of 50%, Adventure Rezort adds $46 in value.

Use this tool to help price specific features for your target segments, or to help learn what features to add at a specific price to gain market share



Heat map of segment crosstabs. The percentages show the percent of respondents within each column (segment) responding with answer shown in each row. Not that for multiple select questions, this can add up to over 100%.

The heatmap tests expected frequencies of responses within each column. White means we'd expect to see these frequencies by chance. Blue means we're seeing fewer observations in that cell than expected. Red means we're seeing more observations in that cell than expected.

Note that this test controls for the baseline observations shown in the 'Overall' column. (The 'Overall' column tests whether or not the responses are evenly distributed, regardless of segment).