Understanding TURF Analysis in Market Research
TURF analysis gives you a strategic advantage to make important product decisions in market research. Resources and shelf space often come at a premium, and this technique helps identify the perfect mix of products or features that appeal to the widest audience. Let's take a closer look at what makes this analytical method valuable to market researchers worldwide.
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TURF analysis meaning and origin
TURF stands for Total Unduplicated Reach and Frequency, a statistical analysis technique that started in media planning. The media world developed TURF long before it became part of market research. It helped advertisers maximize both reach (percentage of target audience that sees at least one ad) and frequency (how often the ad is seen).
Media spaces got more competitive and started to get pricey in the mid-1970s. This led to the concept of "effective reach and frequency." Researchers created models to find the best levels of reach and frequency that would make advertising work better. By 1990, TURF analysis became a formal technique to find product line combinations that would draw the highest number of potential consumers.
The methodology's shift to market research applications put more emphasis on reach, while the frequency component became less important. TURF analysis now helps answer questions like: "Which products should we make with limited support?" or "What group of services will bring the biggest return on investment?"
What is unduplicated reach and frequency?
The "unduplicated reach" in TURF analysis shows the percentage of people who would be drawn to at least one item in a specific combination. This vital metric shows how many unique customers a particular product portfolio might attract.
To name just one example, see an ice cream manufacturer with three flavors (chocolate, vanilla, and strawberry) wanting to add two new ones. Simply picking the two most popular flavors from consumer ratings might backfire if both flavors appeal to the same group of consumers.
TURF fixes this by calculating which combination reaches the most people. Here's a simple example: 34% of people might prefer chocolate. After removing those respondents, 20% of the remaining people might prefer vanilla. This means a chocolate-vanilla combination would reach 54%. Running this process for each flavor reveals the best product mix.
Frequency shows the average number of items in a combination that appeal to each respondent. Reach tells you how many people you connect with, while frequency shows how much they might involve themselves with your product portfolio.
TURF analysis vs MaxDiff: Key differences
MaxDiff (Maximum Difference Scaling) and TURF analysis work together but serve different purposes in market research. MaxDiff finds out how important various attributes are by asking people to pick the most and least important options from a set. This creates trade-offs and shows clearer preference rankings.
MaxDiff identifies individual preferences, while TURF finds the best combinations. MaxDiff answers "Which items do people prefer most?" while TURF tells you "Which combination of items reaches the most people?"
These two techniques can create powerful results together. MaxDiff results become input data for TURF analysis, which optimizes product development. MaxDiff provides reliable preference data because people must make trade-offs, which reduces response bias. TURF analysis then uses these preference scores to explore feature combinations that will reach the most people and gain the highest preference share.
A health insurance plan study shows this partnership at work. MaxDiff identified the most valued features (saving points, extending expiration dates), and TURF analysis found the best feature combinations that would appeal to the widest customer base.
Your research questions should guide which technique you use. MaxDiff works best to understand relative preferences. TURF analysis gives you strategic insights to maximize market potential when optimizing product portfolios with limited resources.
How TURF Analysis Works with Real Data
TURF analysis implementation requires a clear understanding of data structure and calculation methods. The power of this technique becomes evident when we look at real market research data.
Data input structure: respondent-level preferences
TURF analysis starts with well-laid-out input data in respondent-level priorities. The best structure uses a binary matrix where:
- Rows represent individual respondents (consumers)
- Columns represent the products or features being tested
- Each cell contains either a 1 (that indicates preference/would purchase) or 0 (that indicates no preference)
This binary format is a vital component because TURF analysis calculates which individuals are "reached" by specific product combinations. To cite an instance, when evaluating beverage priorities, each respondent would have binary values for all flavor options, with 1s marking their preferred choices.
The data needs cleaning before analysis by removing respondents who selected zero options since they won't add value to the results. A wine priorities study of 20,000 people removed all but one of these 1,401 respondents who hadn't selected any wine varieties. The analysis focused on the remaining 18,599 participants.
Calculating reach and frequency for combinations
The analysis calculates unduplicated reach for different product combinations once the data is structured properly. Here's the systematic approach:
- Generate all possible combinations of products for a given set size
- For each combination, determine whether each respondent would be "reached" (if they selected at least one product in that combination)
- Calculate the percentage of respondents reached by each combination
- Find which combination maximizes unduplicated reach
The unduplicated reach calculation for a combination counts respondents who selected at least one product, divided by total sample size. A four-wine combination reaching 16,003 out of 18,599 respondents would have 86.04% reach.
Frequency calculations show the average number of products each respondent prefers from the combination. A 1.5 frequency means respondents like 1.5 products from the set on average.
TURF analysis example: juice flavor selection
A juice company's real-world example makes this concept clearer. The company wanted to determine which flavors would work best in their product line. They surveyed consumers about their priorities for ten different flavors and analyzed various combinations to maximize market reach.
The original frequency analysis showed orange (64%), apple, peach, and passion fruit as the most popular flavors. All the same, this approach missed the overlap – many orange juice fans might also like other top flavors.
The company found through TURF analysis that orange, apple, grapefruit, and mango made the optimal four-flavor combination – reaching 91% of potential customers. The analysis revealed that customers liked 2.3 of these varieties on average (frequency).
A fifth flavor would only increase reach by 2% more, which might not justify production costs. The analysis showed that some popular flavors like passion fruit didn't make the optimal set because they didn't add substantially to unduplicated reach.
This example shows TURF's superiority over simple preference counting – it finds specific combinations that maximize unique customer reach rather than just selecting individually popular options.
Optimizing Product Portfolios Using TURF
Product managers often struggle with tough choices about their portfolios. They just need to make strategic decisions about which products to keep, given limited shelf space, production limits, and budget constraints. TURF analysis offers a practical way to tackle these challenges and helps teams maximize their market reach while cutting down on waste.
Using TURF to select top-performing SKUs
Teams that develop new product lines or expand existing ones can use TURF analysis to find the best mix of SKUs that will reach the widest audience. The LA Cookie Company's story is a great example. They had three successful products (Chocolate Chip, Peanut Butter, and Oatmeal) and needed to pick three new flavors from eight options.
The three flavors that scored highest based on purchase intent were Lemon Cream, Sugar, and Chocolate Chocolate Chip. All the same, TURF analysis showed a different winning combination: Lemon Cream, Ginger Snaps, and Chocolate Chocolate Chip would push total portfolio reach to 84%.
The analysis found that Ginger Snaps added by a lot more new customers than Sugar cookies, even though fewer people said they'd buy them individually. This happens because TURF looks at how different products attract different groups of customers, not just which ones are popular on their own.
Building a TURF ladder for range size decisions
TURF helps figure out the ideal number of SKUs in your range through what we call a "TURF ladder." This method shows you how many new customers each additional product brings in, helping you find the sweet spot between variety and efficiency.
Building a TURF ladder requires you to:
- Calculate reach for your best single SKU
- Find out how many new customers a second SKU adds
- Keep going with this process for each extra SKU
- Stop when new products don't bring enough value
To name just one example, if your financial targets say each SKU should reach at least 5% of the market to cover advertising costs, you'd stop adding products once they bring in less than 5% new customers. This approach stops your product line from getting too big and makes sure each SKU earns its keep.
Avoiding cannibalization in product lines
TURF really shines at preventing cannibalization - where new products steal sales from existing ones instead of growing your customer base. Ipsos research explains that "optimizing a product line isn't necessarily about finding which flavors are the most popular and simply going with them because of their individual appeal".
TURF keeps your product line healthy by finding combinations that reach different customer groups. The LA Cookie Company case shows how sometimes a less popular flavor adds more value than what seems like a stronger choice.
TURF also shows when a new addition might just copy what existing products already do. So when aytm used TURF analysis on a sports bar's pizza menu expansion, they found that while "Sausage" and "Pepperoni" were popular choices individually, offering "Pepperoni" and "Hawaiian" would reach more customers by attracting different types of pizza lovers.
This focus on reaching new customers helps create product lines that serve a variety of customer priorities without products competing against each other.
Comparing TURF with Other Research Methods
Market researchers must choose from several analytical tools at their disposal. The right method can make a huge difference to get useful business insights. Let's look at how TURF analysis stacks up against other popular research methods and learn when each approach works best.
TURF analysis vs conjoint analysis
Conjoint analysis works as both a question-asking methodology and an analytical framework. TURF is just a calculation that runs on previously collected data. This basic difference shapes how researchers use each method.
Conjoint analysis helps understand how consumers make trade-offs between different product features like price, flavor, or size by simulating purchase decisions. The method shows which features drive consumer choices and their relative value.
TURF's focus lies on finding optimal combinations that maximize reach. You can use conjoint data as input for TURF when analyzing combinations of levels within a specific attribute like flavors. The opposite rarely works.
Choice modeling, which has conjoint analysis, handles several issues that TURF doesn't deal very well with by adding:
- Market-expansion potential measurement
- Competitive brands and pricing variations
- Cannibalization assessment
- More realistic distribution and awareness scenarios
When to use TURF over preference share simulations
TURF and preference share simulations answer different research questions:
| TURF Simulator | Preference Share Simulator |
|---|---|
| "What combinations of variants/features appeal to the largest number of potential customers?" | "What is the optimal combination of variants/features that maximizes market volume and revenue?" |
| "Which variants are most incremental in terms of reach?" | "How will market volume perform after re-pricing?" |
Of course, TURF becomes your best choice when the main goal is maximizing unique customer reach—especially when you have limited shelf space or SKUs. Preference share simulations work better when you need to understand competitive market dynamics or predict revenue effects.
Limitations of TURF in competitive benchmarking
TURF analysis has several constraints that affect its use in competitive benchmarking:
TURF looks at reach rather than frequency. Some advanced versions try to add frequency measurements, but these don't work well because they measure each product separately.
The analysis assumes 100% distribution and awareness for each product variant—something rarely seen in competitive markets. This leads to optimistic projections when measuring against competitors with different market presence.
TURF can't measure cannibalization or source of volume. This makes it hard to assess a new product's effect on existing offerings in a competitive market.
Popular options get too much weight while less-popular ones don't get enough, which can distort competitive assessments.
Tools and Platforms for TURF Analysis Implementation
Market researchers need the right tool to implement TURF analysis. Here are some popular options you can use today.
Running TURF in Excel: pros and cons
Excel gives you a simple starting point for TURF analysis. You start by creating a binary matrix where rows show respondents and columns represent products. The next step tests combinations and calculates unduplicated reach for each option. Excel works well for smaller studies. However, it struggles with larger datasets that TURF usually needs. On top of that, you'll need complex formulas or VBA programming skills to test all possible combinations quickly.
Using Conjointly's TURF simulator
Conjointly's advanced TURF simulator stands out as a specialized tool. The platform helps you find top combinations with the highest reach. You can assess individual item performance and spot complementary options that work together. The accessible interface lets you adjust reach methods, threshold values, and forced alternatives. Interactive outputs like prioritized sequence ladders and performance tables make result visualization easy.
Automated TURF tools: QuestionPro and Quantilope
QuestionPro and Quantilope provide strong automated solutions. QuestionPro's TURF simulator gives you one-click access to optimized configurations. It runs through every possible combination to maximize reach. Quantilope takes automation a step further. Its drag-and-drop feature lets you add TURF to your survey and see live results instantly.
SurveySparrow might be perfect for your next TURF analysis. The accessible interface makes this powerful technique easy to use, even for newcomers.
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Conclusion
TURF analysis is a powerful way to optimize product portfolios with limited resources. Unlike simple preference counts, it focuses on unduplicated reach—finding product combinations that appeal to different customer segments. As seen in the juice company example, the most effective mix isn’t always the most popular flavors but the ones that expand market reach.
The TURF ladder helps determine when adding SKUs drives growth versus cannibalization. While it assumes perfect distribution and works best alongside methods like conjoint analysis, TURF still provides clear, data-driven guidance. Whether using Excel or advanced platforms like Conjointly or Quantilope, TURF ensures every product in your lineup earns its place and maximizes overall reach.
