Zappi Optimize Reach

Optimize Reach helps you to conduct complicated analysis and understand how to combine your flavors, claims, taglines, and more robustly, using TURF analysis. Combine between 5 to 40 stim to learn how you should best compose your innovations.

This solution provides a fast visualization of the TURF combinations for maximizing your product reach, as well as a ranking text and image-based items (like claims, flavors, features, ingredients, and more).

Key Measures

Quickly understand the potential reach of your stimuli via TURF analysis.
Flexibility to use with a wide variety of stimuli (such as features, claims flavors, taglines, etc.).
Expert-led deliverables, showing recommended combinations of 1-5 stimuli to maximize Reach.
Simple and dynamic output: get a visualization of the TURF combinations for maximizing reach, a ranking of the tested items, and the ability to create custom combinations of the results.

Flexible configuration on the Optimize Suite

With the Zappi Optimize Suite you tailor your projects by choosing the metric with which you want to run your rankings against (rank by appeal, behavior change, believability, relevance, meet needs, impact to appeal, impact to behavior change, fit with brand impressions).

Testing Process

A user uploads between 5-40 text or image-based stimuli, with an optional survey ‘primer’ (e.g. a video, an image, and/or a statement).
From this information, the Zappi platform uses an algorithm to calculate the optimal sample size and survey design to enable an optimal analysis.
Respondents are exposed to up to 11 exercises (screens) where they are asked to select the most and least favorite from a selection of five stimuli, as determined by the algorithm (Best-Worst Scaling).
TURF calculations create both recommended combinations of 1-5 stimuli and custom reach analysis in the reporting.

As the number of stim increases, the number of combinations increases exponentially

Because we believe in keeping surveys short, we don’t expose any respondent to more than 11 exercises. Making respondents do more than that would be a significant cognitive load for a discrete choice. Thus, to meet the requirements of serving all 5-stim combinations in order, we need more respondents. Because of this reason, the complexity of the TURF calculation, and also to keep our fielding time reasonable, we limit the max number of stims per survey to 40 (resulting into max n=1.200).

Additionally, Optimize Reach’s TURF calculation uses an exhaustive method to identify the optimal combination of items with the largest reach, analyzing every possible combination. This approach ensures accuracy but significantly increases the complexity as the number of items grows—for example, selecting 10 items from a set of 30 involves over 10 billion combinations. To manage this complexity, the combination limit is set to 5.

Understanding the reach calculation

The score you’re seeing (sometimes referred to by Zappi as the 'satisfaction score') is the reach of each individual item. In this case, this is the percentage of respondents who found the item more appealing than the average after completing the max diff exercise.

The calculation takes into account the average performance of all items for each respondent. Any item which performs better than a threshold above average (in this case 1.15x) is considered to be one that is favored by the respondent.

The best combinations chart shows the best combinations for a given number of stimuli (e.g. “Show me my top options to release a product with 5 flavors”).

The optimum reach chart shows your optimum reach with specific stimuli ‘forced’ into the combinations (e.g. “Show me my optimal combinations for reach which include the ‘Orange’ variant”).

Why is the top variety not coming through in all combinations?

The way Optimize Reach works is that it looks for the combinations in which the highest percentage of respondents like at least one of the options in order to achieve a portfolio with the highest unique reach.

As you create portfolios with a larger number of options, it is possible that the overall top variety will lose its place. If that happens, we can conclude that any new options we could have added were liked by the same respondents who we already reached with our existing choices. Instead, it was possible to reach a larger audience by leaving out the top choice and bringing in a mixture of other options, which have a smaller reach individually, but collectively are favored to a broader set of respondents.