TURF analysis: what it is and how to run one

Customers love having a choice, but not too many options. It can be tempting to attempt to satisfy the desires and preferences of every possible customer by offering a broad range of products. In reality, offering too many choices can actually reduce sales. In this article, we’ll show you how to use TURF analysis with your survey data to streamline your product portfolio—putting you in the best position to maximize sales. 

Total Unduplicated Reach and Frequency analysis, or TURF analysis, is a statistical technique used to optimize product placement, offerings, communication, and promotional strategies when resources are limited. TURF analysis in market research estimates how many customers a given product portfolio would reach, so you can decide which combination of products or services to offer and which to drop.

Say you run a fitness studio and could offer aerobics, Zumba, Pilates, and yoga, but you do not know which combination of classes, days, and times would maximize attendance without paying instructors to teach half-empty rooms. A TURF analysis finds the configuration that reaches and satisfies the maximum number of clients without overlap, a result known as maximizing unduplicated reach.

TURF analysis helps you:

TURF analysis is especially useful when launching a new product range or entering a market for the first time. Consider a survey administered to prospective customers to identify their preferences from a range of possible options.

Say you are planning to launch a one-size-fits-all swimsuit in five colors, five necklines, and two waist rises. Those three features alone create 220 possible SKU combinations, far more than most retailers could stock. In reality, customers might only care about two or three colors, one neckline, and one rise. A short survey narrows this down:

Running a TURF algorithm on the answers identifies the top colors, the strongest and weakest necklines, and the preferred rise. Assembling different combinations of these features produces two key metrics:

Let’s look at how to perform a TURF analysis step by step:

Find an audience that represents your target market. TURF determines the combination of products that reach the greatest share of the market you survey, so capturing data from the wrong audience produces a suboptimal product range. SurveyMonkey Audience can quickly identify a target audience with the same characteristics as your customers.

Prepare questions that ask respondents their preferences across all the options you are evaluating. A multiple-choice question can ask respondents to select every item in a range they would consider buying, or you can ask them to rank their top choices. Research consultants can help you decide which format fits your study if you need it.

After fielding the survey, review the results. A bar chart quickly visualizes the most popular items or the frequency with which respondents chose each one.

A bar chart alone will not tell you which products belong in your portfolio. Imagine surveying 1,000 people about yogurt flavors and finding that vanilla, coconut, and raspberry are the top three choices by frequency. Those look like the clear winners, until you consider whether the same respondents selected all three, which would leave a whole subset of the market unserved.

A frequency analysis alone shows how often each product was selected. A TURF test shows unduplicated frequency alongside reach, which is what actually reveals the best product mix. You can also filter results by customer characteristics, such as gender or region, for more targeted comparisons.

Picture an ice cream stand with room for only three flavors out of many candidates. A market research survey asks customers to rate, on a 5-point scale, whether they would buy each flavor if available, then narrows the focus to those who strongly agreed, since that shows the strongest preference:

A frequency analysis alone might suggest stocking vanilla, chocolate, and cherry, the three most popular flavors, reaching 70% of consumers. But further analysis often reveals that many of those same respondents like more than one of the top three, which means that combination duplicates reach rather than expanding it. It might also show that 90% of cinnamon fans and 50% of pistachio fans like nothing else on the list, meaning stocking chocolate, cinnamon, and pistachio would reach 96% of consumers instead: the 75% who would buy chocolate, the 15% who would buy only pistachio, and the 6% who would buy only cinnamon.

That higher-reach mix is not automatically the best one. Chocolate might sell out quickly, leaving only two niche flavors that move slowly, and total volume could end up lower than expected even though reach went up. TURF analysis solves this by systematically testing every possible combination and comparing reach against frequency for each:

Mix B reaches 100% of respondents and does so with the highest frequency, making it the strongest option here, ahead of Mix C, which reaches the same 100% with less depth per respondent. This is the central trade-off in TURF analysis: some product mixes maximize reach at the cost of frequency, and finding the right balance is the point of running the test rather than stopping at a simple frequency count.

In an illustrative Feature Prioritization example from SurveyMonkey LaunchPad, a TURF simulation on ice cream flavor preference found that vanilla and chocolate alone covered 70% of the market, the kind of output an automated TURF simulation produces once responses come in.

Researcher Sheena Iyengar of Columbia University tested the same underlying principle in a real-world jam tasting experiment. On one Saturday, shoppers were offered 6 jam flavors; on the next, 24 flavors.

More people stopped to sample when 24 flavors were on offer (60%, versus 40% for 6 flavors), but only 3% of that group made a purchase, compared with 30% of the group offered just 6 flavors.

More choice increased attention but reduced conversion, the opposite of what a simple reach-maximizing strategy would predict.

Done well, TURF analysis pays off for both customers and the business running it.

A TURF analysis is a statistical method that identifies the combination of products or features that reaches the largest possible share of a target market without wasting overlap on options that appeal to the same people.

TURF stands for Total Unduplicated Reach and Frequency. Reach measures how many people like at least one option in a combination, and frequency measures how many options each person likes on average.

MaxDiff ranks individual items by forcing respondents to choose their most and least preferred option from small sets, revealing true relative preference. TURF analysis takes those preferences and tests every possible combination to find the mix that reaches the widest audience, which is why the two methods are typically run together.

It is the method used to decide which limited set of products, flavors, features, or messages to keep when you cannot offer everything, based on how many distinct customers each combination reaches rather than on raw popularity alone.

TURF analysis only pays off once you run it on real customer data instead of a spreadsheet guess. Feature Prioritization from SurveyMonkey LaunchPad runs MaxDiff and an automated TURF simulation together, turning trade-off responses into a stack-ranked list and the combination most likely to reach your widest possible audience, without any manual modeling.

If you'd rather hand the legwork to someone else, SurveyMonkey Market Research Services can design, field, and analyze a custom study for you, from questionnaire design through the final report.