A product team with ten flavor ideas and a budget for four faces a real problem. Pick the wrong four, and the launch reaches only a fraction of the market. TURF analysis solves exactly this problem by finding which combination of products, features, or messages reaches the largest possible share of an audience, not just the most popular single option.<\/p>\n\n\n\n
Most teams default to ranking choices by popularity alone. That habit misses overlap. Two popular options can appeal to the same buyers, while a less popular one pulls in a completely different audience segment.<\/p>\n\n\n\n
In this blog, we will break down how TURF analysis works, walk through real examples, and cover the exact steps to run one using your own survey data.<\/p>\n\n\n\n\n\n
TURF analysis, short for Total Unduplicated Reach and Frequency analysis, is a market research method that finds the combination of items, such as products, flavors, features, or ad messages, that reaches the largest possible share of an audience with the least overlap.<\/p>\n\n\n\n
Researchers first built the method for media planning, where the goal was reaching the most viewers with a limited number of ad placements. Teams later expanded it to product lines, menu planning, and messaging tests, anywhere a business has to pick a short list from a longer one.<\/p>\n\n\n\n
As a market research technique, TURF analysis works alongside tools like surveys and conjoint studies to turn raw preference data into a short list a team can actually act on.<\/p>\n\n\n\n
TURF analysis works by testing every possible combination of items from a list respondents rated or selected, then ranking each combination by unduplicated reach. Unduplicated reach is the share of people who would choose at least one item in that set.<\/p>\n\n\n\n
Frequency, the second half of the acronym, counts how many items in a set each respondent picked. It shows whether interest runs deep across a few items or spreads thin across many.<\/p>\n\n\n\n
Analysts also track top-down reach: how much of the audience a brand would reach by offering only its single top-ranked item, before adding anything else to the mix.<\/p>\n\n\n\n
Here is a simple illustration. A newsletter survey shows one topic chosen by 30 percent of subscribers. That looks like the obvious winner until a TURF run reveals a different set of three topics, none of them the single most popular choice, that together reaches 78 percent of the list. Reach, not popularity, is the number that actually matters.<\/p>\n\n\n\n
Behind the scenes, the calculation is mostly brute force. The model checks every possible group of a given size, adds up how many respondents picked at least one item in that group, and sorts the results from highest reach to lowest. A list of ten items tested in groups of three already produces 120 combinations, which is why this calculation is normally left to software rather than done by hand.<\/p>\n\n\n\n
TURF analysis earns its place whenever a team has to cut a long list down to a short one and needs to know the cut will still reach the most people. A few signals point directly to it.<\/p>\n\n\n\n
Not every preference question calls for TURF. When the goal is judging one concept at a time with no combination logic involved, monadic testing<\/a> is usually the better fit.<\/p>\n\n\n\n TURF analysis and conjoint analysis both start from a preference survey, and that overlap causes real confusion about which one to run. Conjoint analysis measures how people trade off individual attributes like price, size, or color. TURF measures which whole combination of options reaches the most people. For a full breakdown of the trade-off method, see our guide to conjoint analysis<\/a>.<\/p>\n\n\n\nTURF analysis vs conjoint analysis: What’s the difference?<\/strong><\/h2>\n\n\n\n