Rankifier turns the endless parade of MaxDiff choice screens into one integrated, fluid experience.
In the market research and consumer insights industries, “MaxDiff” is the popularized term for best-worst scaling. This discrete choice modeling technique presents people with repeated subsets of items to choose from, pulled from a larger set, in an effort to evaluate their relative value or utility. Traditionally, MaxDiff exercises in survey research have suffered from high dropoff rates due to their repetitive nature.
Rankifier has been proven to improve user satisfaction and boost response rates. By using an adaptive “shuffling” algorithm, researchers no longer need to build and balance MaxDiff models or manually construct dozens of screens to implement their question; just add your attributes to the Rankifier configuration, publish into your study, and enjoy the results.
- A deck of cards containing multiple copies of each of the test attributes is “shuffled” and dealt to the user in sets of between 3 and 6 cards (configurable)
- Players select their most preferred and least preferred item from their hand, based on instructions provided by researcher-configured question prompts
- Play repeats until the full deck has been played (deck size adjustable by the researcher based on research needs)
- Hand Size: Display between 3 and 6 cards per hand
- Number of Hands: Adjust overall game duration, considering tradeoffs between game length and amount of data collected per player
- Best-of-Best: The optional “best of best” mode takes the items chosen as “best” from each hand and conducts a runoff to determine the most-preferred item (useful for increasing data fidelity at the top end of the preference range)
- Text colors
- Card back and card front images
- Card contents (text and images)
Pre-Game Splash Screen
Optionally display a pre-game welcome page, including:
- Customizable logo (or no logo)
- Secondary poster image for visual enhancement
- Title, subheading, and introductory instruction text
Rankifier games can be configured to optionally display an end-of-game scoring page, which provides the player with feedback on how their responses compared with others who have participated.
Rankifier games can optionally present an end-of-game feedback screen, that captures both quantitative and qualitative input:
- An additional overall 5-point rating scale; and
- Open-ended text question prompt and feedback box for qualitative insights
Sample Use Cases
Rankifier excels at reducing a large number of candidates, such as logos or packaging designs, to a set of top performers that can be prioritized for more in-depth assessment.
This Rankifier example evaluates a set of hypothetical logos for a research agency.
When developing a concept for a new product or service, it’s often challenging to prioritize which components are “above the line” and can’t be cut. Using Rankifier and its simplified choice modeling approach makes it fast and easy to distill a large inventory of potential ingredients into what’s most critical to users.
This Rankifier example explores what is most important to healthcare professionals when communicating outcomes from clinical trials.